Create modeling_pcmlp.py

modeling_pcmlp.py

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+"""PC-MLP model for HuggingFace Hub with auto_map."""
+import math
+import torch
+import torch.nn as nn
+from transformers import PreTrainedModel
+from .configuration_pcmlp import PCMLPConfig
+
+class PCMLP(PreTrainedModel):
+    config_class = PCMLPConfig
+
+    def __init__(self, config):
+        super().__init__(config)
+        d = config.vocab_size
+        h = config.hidden_dim
+        scale = 1.0 / math.sqrt(h)
+
+        self.tok = nn.Embedding(config.vocab_size, d)
+        self.pos = nn.Embedding(config.seq_len, d)
+        self.W1 = nn.Linear(d, h, bias=False)
+        self.W2 = nn.Linear(h, h, bias=False)
+        self.head = nn.Linear(h, config.num_classes)
+
+        # match JAX init scale
+        nn.init.normal_(self.tok.weight, std=0.05)
+        nn.init.normal_(self.pos.weight, std=0.05)
+        nn.init.normal_(self.W1.weight, std=scale)
+        nn.init.normal_(self.W2.weight, std=scale)
+        nn.init.normal_(self.head.weight, std=0.02)
+
+    def forward(self, input_ids, labels=None):
+        B, T = input_ids.shape
+        pos_ids = torch.arange(T, device=input_ids.device)
+        h = self.tok(input_ids) + self.pos(pos_ids)
+        h = h.reshape(B, -1)[:, :self.W1.in_features]
+        h0 = h
+        h1 = torch.nn.functional.gelu(self.W1(h0))
+        h2 = torch.nn.functional.gelu(self.W2(h1))
+        logits = self.head(h2)
+
+        loss = None
+        if labels is not None:
+            ce = torch.nn.functional.cross_entropy(logits, labels)
+            local = ((h1.mean(1) - h0.mean(1)) ** 2).mean() + \
+                    ((h2.mean(1) - h1.mean(1)) ** 2).mean()
+            loss = ce + self.config.local_loss_weight * local
+        return {"loss": loss, "logits": logits}