Update lwm_model.py

lwm_model.py

@@ -1,153 +1,152 @@
-# -*- 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 input_preprocess import *
-
-
-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', 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)
-        ckpt_path = ckpt_name
-        
-        # 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):
-        # 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
-
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import numpy as np
+import random
+
+# Set manual seed for reproducibility
+def set_random_seed(seed=42):
+    torch.manual_seed(seed)
+    np.random.seed(seed)
+    random.seed(seed)
+    if torch.cuda.is_available():
+        torch.cuda.manual_seed_all(seed)
+    # Ensures deterministic behavior
+    torch.backends.cudnn.deterministic = True
+    torch.backends.cudnn.benchmark = False
+
+# Call the seed function
+set_random_seed()
+
+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())  # Ensure consistency in floating-point precision
+        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
+
+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)
+
+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', use_auth_token=None):
+        # Define model
+        model = cls().to(device)
+
+        # Load model weights
+        ckpt_path = ckpt_name
+        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):
+        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