Update meshlayer.py

meshlayer.py

@@ -1,3 +1,55 @@
-# Source code for MeshLayer from cell 39f2782d
-# Please replace this with the actual code from the notebook cell.
+from transformers import PretrainedConfig, PreTrainedModel, AutoModelForCausalLM # Import AutoModelForCausalLM
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import math
+from transformers.modeling_outputs import CausalLMOutputWithPast # Import the necessary output class
 
+# Define the main Mesh Layer
+class MeshLayer(nn.Module):
+    def __init__(self, config: MeshConfig):
+        super().__init__()
+        self.config = config
+        self.router = MeshRouter(config)
+        self.experts = nn.ModuleList([MeshExpert(config) for _ in range(config.mesh_grid_size[0] * config.mesh_grid_size[1])])
+        self.neighbor_exchange = NeighborExchange(config)
+        self.cross_expert_attention = CrossExpertAttention(config)
+
+    def forward(self, hidden_states):
+        # hidden_states shape: (batch_size, sequence_length, hidden_size)
+
+        # 1. Routing
+        topk_weights, topk_indices = self.router(hidden_states)
+        # topk_weights shape: (batch_size, sequence_length, k)
+        # topk_indices shape: (batch_size, sequence_length, k)
+
+        # Prepare expert inputs: repeat hidden_states for each expert
+        # shape: (batch_size, sequence_length, num_experts, hidden_size)
+        expanded_hidden_states = hidden_states.unsqueeze(2).expand(-1, -1, self.config.mesh_grid_size[0] * self.config.mesh_grid_size[1], -1)
+
+        # 2. Expert Computation
+        # Compute output for all experts (can be optimized to only compute for selected experts)
+        expert_outputs = torch.stack([expert(expanded_hidden_states[:, :, i, :]) for i, expert in enumerate(self.experts)], dim=2)
+        # expert_outputs shape: (batch_size, sequence_length, num_experts, hidden_size)
+
+        # 3. Neighbor Exchange (conceptual implementation needed)
+        exchanged_expert_outputs = self.neighbor_exchange(expert_outputs, topk_indices)
+
+        # 4. Cross-Expert Attention (conceptual implementation needed)
+        cross_attned_expert_outputs = self.cross_expert_attention(exchanged_expert_outputs)
+
+        # 5. Combine expert outputs based on routing weights
+        # Create a tensor to gather the outputs of the selected experts
+        # shape: (batch_size, sequence_length, k, hidden_size)
+        gathered_outputs = torch.gather(
+            cross_attned_expert_outputs,
+            dim=2,
+            index=topk_indices.unsqueeze(-1).expand(-1, -1, -1, self.config.hidden_size)
+        )
+
+        # Apply routing weights: (batch_size, sequence_length, k, 1) * (batch_size, sequence_length, k, hidden_size)
+        combined_output = (gathered_outputs * topk_weights.unsqueeze(-1)).sum(dim=2)
+        # combined_output shape: (batch_size, sequence_length, hidden_size)
+
+        # Return the combined output and the expert indices for potential visualization
+        return combined_output, topk_indices # Return combined output and expert indices