Upload modeling_pruned_flex_olmo.py with huggingface_hub

modeling_pruned_flex_olmo.py

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+"""Pruned FlexOlmo model with variable-width expert 1.
+
+This module provides a HuggingFace-compatible model that can be loaded with:
+    AutoModelForCausalLM.from_pretrained("hbfreed/flex-math-8192", trust_remote_code=True)
+"""
+
+import torch
+import torch.nn as nn
+from transformers import FlexOlmoForCausalLM
+from transformers.models.flex_olmo.modeling_flex_olmo import FlexOlmoMLP
+
+from configuration_pruned_flex_olmo import PrunedFlexOlmoConfig
+
+
+class PrunedFlexOlmoMLP(nn.Module):
+    """Pruned MLP with same interface as FlexOlmoMLP but variable width."""
+
+    def __init__(self, intermediate_size: int, hidden_size: int, act_fn, dtype=torch.bfloat16):
+        super().__init__()
+        self.gate_proj = nn.Linear(hidden_size, intermediate_size, bias=False, dtype=dtype)
+        self.up_proj = nn.Linear(hidden_size, intermediate_size, bias=False, dtype=dtype)
+        self.down_proj = nn.Linear(intermediate_size, hidden_size, bias=False, dtype=dtype)
+        self.act_fn = act_fn
+
+    def forward(self, x):
+        return self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))
+
+
+class PrunedFlexOlmoForCausalLM(FlexOlmoForCausalLM):
+    """FlexOlmo with pruned expert 1 for variable-width MoE.
+
+    Expert 0 remains at full intermediate_size, while expert 1 is pruned
+    to expert_1_intermediate_size specified in the config.
+    """
+
+    config_class = PrunedFlexOlmoConfig
+
+    def __init__(self, config: PrunedFlexOlmoConfig):
+        # Initialize parent with full architecture
+        super().__init__(config)
+
+        # Replace expert 1 in each layer with pruned version
+        expert_1_width = config.expert_1_intermediate_size
+        hidden_size = config.hidden_size
+
+        for layer in self.model.layers:
+            # Get activation function from existing expert
+            act_fn = layer.mlp.experts[1].act_fn
+
+            # Replace expert 1 with pruned version
+            layer.mlp.experts[1] = PrunedFlexOlmoMLP(
+                intermediate_size=expert_1_width,
+                hidden_size=hidden_size,
+                act_fn=act_fn,
+                dtype=self.dtype,
+            )
+
+    @classmethod
+    def from_pretrained(cls, pretrained_model_name_or_path, *args, **kwargs):
+        """Load pruned model, handling both local and hub paths."""
+        # Let parent handle the loading - it will use our config_class
+        # and __init__ which sets up the pruned architecture
+        return super().from_pretrained(pretrained_model_name_or_path, *args, **kwargs)