Upload model.py

model.py

@@ -0,0 +1,197 @@
+# -*- coding: utf-8 -*-
+"""
+Created on Sun Sep 15 19:55:23 2024
+
+@author: salikha4
+"""
+
+import os
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import numpy as np
+
+from inference import *
+from load_data import load_DeepMIMO_data
+from input_preprocess import *
+from lwm_model import LWM, load_model
+
+
+ELEMENT_LENGTH = 16
+D_MODEL = 64
+MAX_LEN = 129
+N_LAYERS = 12
+N_HEADS = 12
+D_FF = D_MODEL * 4
+D_K = D_MODEL // N_HEADS
+D_V = D_MODEL // N_HEADS
+DROPOUT = 0.1
+
+class LayerNormalization(nn.Module):
+    def __init__(self, d_model: int, eps: float = 1e-6) -> None:
+        super().__init__()
+        self.eps = eps
+        self.alpha = nn.Parameter(torch.ones(d_model))
+        self.bias = nn.Parameter(torch.zeros(d_model))
+
+    def forward(self, x):
+        mean = x.mean(dim=-1, keepdim=True)
+        std = x.std(dim=-1, keepdim=True)
+        return self.alpha * (x - mean) / (std + self.eps) + self.bias
+
+class Embedding(nn.Module):
+    def __init__(self, element_length, d_model, max_len):
+        super().__init__()
+        self.element_length = element_length
+        self.d_model = d_model
+        self.proj = nn.Linear(element_length, d_model)
+        self.pos_embed = nn.Embedding(max_len, d_model)
+        self.norm = LayerNormalization(d_model)
+
+    def forward(self, x):
+        seq_len = x.size(1)
+        pos = torch.arange(seq_len, dtype=torch.long, device=x.device)
+        pos = pos.unsqueeze(0).expand_as(x[:, :, 0])
+        tok_emb = self.proj(x.float())
+        embedding = tok_emb + self.pos_embed(pos)
+        return self.norm(embedding)
+
+class ScaledDotProductAttention(nn.Module):
+    def __init__(self):
+        super().__init__()
+
+    def forward(self, Q, K, V):
+        scores = torch.matmul(Q, K.transpose(-1, -2)) / np.sqrt(D_K)
+        attn = F.softmax(scores, dim=-1)
+        context = torch.matmul(attn, V)
+        return context, attn
+
+class MultiHeadAttention(nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.W_Q = nn.Linear(D_MODEL, D_K * N_HEADS)
+        self.W_K = nn.Linear(D_MODEL, D_K * N_HEADS)
+        self.W_V = nn.Linear(D_MODEL, D_V * N_HEADS)
+        self.linear = nn.Linear(N_HEADS * D_V, D_MODEL)
+        self.norm = LayerNormalization(D_MODEL)
+        self.dropout = nn.Dropout(DROPOUT)
+        
+    def forward(self, Q, K, V):
+        residual, batch_size = Q, Q.size(0)
+        q_s = self.W_Q(Q).view(batch_size, -1, N_HEADS, D_K).transpose(1, 2)
+        k_s = self.W_K(K).view(batch_size, -1, N_HEADS, D_K).transpose(1, 2)
+        v_s = self.W_V(V).view(batch_size, -1, N_HEADS, D_V).transpose(1, 2)
+
+        context, attn = ScaledDotProductAttention()(q_s, k_s, v_s)
+        output = context.transpose(1, 2).contiguous().view(batch_size, -1, N_HEADS * D_V)
+        output = self.linear(output)
+        return residual + self.dropout(output), attn #residual + self.dropout(output), attn
+
+class PoswiseFeedForwardNet(nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.fc1 = nn.Linear(D_MODEL, D_FF)
+        self.fc2 = nn.Linear(D_FF, D_MODEL)
+        self.dropout = nn.Dropout(DROPOUT)
+        self.norm = LayerNormalization(D_MODEL)
+
+    def forward(self, x):
+        output = self.fc2(self.dropout(F.relu(self.fc1(x))))
+        return x + self.dropout(output) #x + self.dropout(output)
+
+class EncoderLayer(nn.Module):
+    def __init__(self):
+        super().__init__()
+        self.enc_self_attn = MultiHeadAttention()
+        self.pos_ffn = PoswiseFeedForwardNet()
+        self.norm = LayerNormalization(D_MODEL)
+
+    def forward(self, enc_inputs):
+        attn_outputs, attn = self.enc_self_attn(enc_inputs, enc_inputs, enc_inputs)
+        attn_outputs = self.norm(attn_outputs)
+        enc_outputs = self.pos_ffn(attn_outputs)
+        return enc_outputs, attn
+    
+# class LWM(torch.nn.Module):
+#     def __init__(self, element_length=16, d_model=64, max_len=129, n_layers=12):
+#         super().__init__()
+
+#         self.embedding = Embedding(element_length, d_model, max_len)
+#         self.layers = nn.ModuleList([EncoderLayer() for _ in range(n_layers)])
+#         self.linear = nn.Linear(d_model, d_model)
+#         self.norm = LayerNormalization(d_model)
+
+#         embed_weight = self.embedding.proj.weight
+#         d_model, n_dim = embed_weight.size()
+#         self.decoder = nn.Linear(d_model, n_dim, bias=False)
+#         self.decoder.weight = nn.Parameter(embed_weight.transpose(0, 1))
+#         self.decoder_bias = nn.Parameter(torch.zeros(n_dim))
+
+#     @classmethod
+#     def from_pretrained(cls, ckpt_name='model_weights.pth', device='cuda'):
+#         # Define model
+#         model = cls().to(device)
+        
+#         # Download the model weights (from a remote or local repository)
+#         ckpt_path = f'https://huggingface.co/sadjadalikhani/LWM/resolve/main/{ckpt_name}'
+        
+#         # Load the model weights
+#         model.load_state_dict(torch.hub.load_state_dict_from_url(ckpt_path, map_location=device))
+#         print(f"Model loaded successfully from {ckpt_path} to {device}")
+        
+#         return model
+
+#     def forward(self, input_ids, masked_pos):
+#         output = self.embedding(input_ids)
+        
+#         for layer in self.layers:
+#             output, _ = layer(output)
+
+#         masked_pos = masked_pos.long()[:, :, None].expand(-1, -1, output.size(-1))
+#         h_masked = torch.gather(output, 1, masked_pos)
+#         h_masked = self.norm(F.relu(self.linear(h_masked)))
+#         logits_lm = self.decoder(h_masked) + self.decoder_bias
+
+#         return logits_lm, output
+
+from huggingface_hub import hf_hub_download
+import torch
+
+class LWM(torch.nn.Module):
+    def __init__(self, element_length=16, d_model=64, max_len=129, n_layers=12):
+        super().__init__()
+        # Model architecture...
+        self.embedding = Embedding(element_length, d_model, max_len)
+        self.layers = nn.ModuleList([EncoderLayer() for _ in range(n_layers)])
+        self.linear = nn.Linear(d_model, d_model)
+        self.norm = LayerNormalization(d_model)
+        embed_weight = self.embedding.proj.weight
+        d_model, n_dim = embed_weight.size()
+        self.decoder = nn.Linear(d_model, n_dim, bias=False)
+        self.decoder.weight = nn.Parameter(embed_weight.transpose(0, 1))
+        self.decoder_bias = nn.Parameter(torch.zeros(n_dim))
+
+    @classmethod
+    def from_pretrained(cls, ckpt_name='model_weights.pth', device='cuda', use_auth_token=None):
+        # Define model
+        model = cls().to(device)
+        
+        # Download model weights using Hugging Face Hub
+        ckpt_path = hf_hub_download(repo_id="sadjadalikhani/LWM", filename=ckpt_name, use_auth_token=use_auth_token)
+        
+        # Load the model weights
+        model.load_state_dict(torch.load(ckpt_path, map_location=device))
+        print(f"Model loaded successfully from {ckpt_path} to {device}")
+        
+        return model
+
+    def forward(self, input_ids, masked_pos):
+        # Define the forward pass
+        output = self.embedding(input_ids)
+        for layer in self.layers:
+            output, _ = layer(output)
+        masked_pos = masked_pos.long()[:, :, None].expand(-1, -1, output.size(-1))
+        h_masked = torch.gather(output, 1, masked_pos)
+        h_masked = self.norm(F.relu(self.linear(h_masked)))
+        logits_lm = self.decoder(h_masked) + self.decoder_bias
+        return logits_lm, output