Add application file

Assign12_Model.py

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+# Solving for residual std scaling issue
+import os
+import math
+import time
+import inspect
+from dataclasses import dataclass
+import torch
+import torch.nn as nn
+from torch.nn import functional as F
+from tqdm import tqdm  # Import tqdm
+
+
+class CausalSelfAttention(nn.Module):
+
+    def __init__(self, config):
+        super().__init__()
+        assert config.n_embd % config.n_head == 0
+        # key, query, value projections for all heads, but in a batch
+        self.c_attn = nn.Linear(config.n_embd, 3 * config.n_embd)
+        # output projection
+        self.c_proj = nn.Linear(config.n_embd, config.n_embd)
+        self.c_proj.NANGPT_SCALE_INIT = 1
+        # regularization
+        self.n_head = config.n_head
+        self.n_embd = config.n_embd
+        self.register_buffer("bias", torch.tril(torch.ones(config.block_size, config.block_size)).view(1, 1, config.block_size, config.block_size))
+
+    def forward(self, x):
+        B, T, C = x.size() # batch size, sequence length, embedding dimensionality (n_embd)
+        # calculate query, key, values for all heads in batch and move head forward to be the batch dim
+        # nh is "number of heads", hs is "head size", and C (number of channels) = nh * hs
+        # e.g. in GPT-2 (124M), n_head=12, hs=64, so nh*hs=C=768 channels in the Transformer
+        qkv = self.c_attn(x)
+        q, k, v = qkv.split(self.n_embd, dim=2)
+        k = k.view(B, T, self.n_head, C // self.n_head).transpose(1, 2) # (B, nh, T, hs)
+        q = q.view(B, T, self.n_head, C // self.n_head).transpose(1, 2) # (B, nh, T, hs)
+        v = v.view(B, T, self.n_head, C // self.n_head).transpose(1, 2) # (B, nh, T, hs)
+
+        att = (q @ k.transpose(-2, -1)) * (1.0 / math.sqrt(k.size(-1)))
+        att = att.masked_fill(self.bias[:, :, :T, :T] == 0, float('-inf'))
+        att = F.softmax(att, dim=-1)
+        y = att @ v # (B, nh, T, T) x (B, nh, T, hs) -> (B, nh, T, hs)
+
+        y = y.transpose(1, 2).contiguous().view(B, T, C) # re-assemble all head outputs side by side
+        # output projection
+        y = self.c_proj(y)
+        return y
+
+
+class MLP(nn.Module):
+
+    def __init__(self, config):
+        super().__init__()
+        self.c_fc    = nn.Linear(config.n_embd, 4 * config.n_embd)
+        self.gelu    = nn.GELU(approximate='tanh')
+        self.c_proj  = nn.Linear(4 * config.n_embd, config.n_embd)
+        self.c_proj.NANOGPT_SCALE_INIT = 1
+
+    def forward(self, x):
+        x = self.c_fc(x)
+        x = self.gelu(x)
+        x = self.c_proj(x)
+        return x
+
+class Block(nn.Module):
+
+    def __init__(self, config):
+        super().__init__()
+        self.ln_1 = nn.LayerNorm(config.n_embd)
+        self.attn = CausalSelfAttention(config)
+        self.ln_2 = nn.LayerNorm(config.n_embd)
+        self.mlp = MLP(config)
+
+    def forward(self, x):
+        x = x + self.attn(self.ln_1(x))
+        x = x + self.mlp(self.ln_2(x))
+        return x
+
+
+@dataclass
+class GPTConfig:
+    block_size: int = 1024 # max sequence length
+    vocab_size: int = 50257 # number of tokens: 50,000 BPE merges + 256 bytes tokens + 1 <|endoftext|> token
+    n_layer: int = 12 # number of layers
+    n_head: int = 12 # number of heads
+    n_embd: int = 768 # embedding dimension
+
+
+class GPT(nn.Module):
+
+    def __init__(self, config):
+        super().__init__()
+        self.config = config
+
+        self.transformer = nn.ModuleDict(dict(
+            wte = nn.Embedding(config.vocab_size, config.n_embd),
+            wpe = nn.Embedding(config.block_size, config.n_embd),
+            h = nn.ModuleList([Block(config) for _ in range(config.n_layer)]),
+            ln_f = nn.LayerNorm(config.n_embd),
+        ))
+        self.lm_head = nn.Linear(config.n_embd, config.vocab_size, bias=False)
+
+        # weight sharing
+        self.transformer.wte.weight = self.lm_head.weight
+
+        # weight initialization
+        self.apply(self._init_weights)
+
+    def _init_weights(self, module):
+        if isinstance(module, nn.Linear):
+            std = 0.02
+            if hasattr(module, 'NANGPT_SCALE_INIT'):
+                std *= (2 * self.config.n_layer) ** -0.5
+            torch.nn.init.normal_(module.weight, mean = 0.0, std = std)
+            if module.bias is not None:
+                torch.nn.init.zeros_(module.bias)
+        elif isinstance(module, nn.Embedding):
+            torch.nn.init.normal_(module.weight, mean=0.0, std = 0.02)
+
+
+
+    def forward(self, idx, targets=None):
+        # idx is of shape (B, T)
+        B, T = idx.size()
+        assert T <= self.config.block_size, f"Cannot forward sequence of length {T}, block size is only {self.config.block_size}"
+        # forward the token and posisition embeddings
+        pos = torch.arange(0, T, dtype=torch.long, device=idx.device) # shape (T)
+        pos_emb = self.transformer.wpe(pos) # position embeddings of shape (T, n_embd)
+        tok_emb = self.transformer.wte(idx) # token embeddings of shape (B, T, n_embd)
+        x = tok_emb + pos_emb
+        # forward the blocks of the transformer
+        for block in self.transformer.h:
+            x = block(x)
+        # forward the final layernorm and the classifier
+        x = self.transformer.ln_f(x)
+        logits = self.lm_head(x) # (B, T, vocab_size)
+        loss = None
+        if targets is not None:
+            loss = F.cross_entropy(logits.view(-1, logits.size(-1)), targets.view(-1))
+        return logits, loss
+
+    @classmethod
+    def from_pretrained(cls, model_type):
+        """Loads pretrained GPT-2 model weights from huggingface"""
+        assert model_type in {'gpt2', 'gpt2-medium', 'gpt2-large', 'gpt2-xl'}
+        from transformers import GPT2LMHeadModel
+        #print("loading weights from pretrained gpt: %s" % model_type)
+
+        # n_layer, n_head and n_embd are determined from model_type
+        config_args = {
+            'gpt2':         dict(n_layer=12, n_head=12, n_embd=768),  # 124M params
+            'gpt2-medium':  dict(n_layer=24, n_head=16, n_embd=1024), # 350M params
+            'gpt2-large':   dict(n_layer=36, n_head=20, n_embd=1280), # 774M params
+            'gpt2-xl':      dict(n_layer=48, n_head=25, n_embd=1600), # 1558M params
+        }[model_type]
+        config_args['vocab_size'] = 50257 # always 50257 for GPT model checkpoints
+        config_args['block_size'] = 1024 # always 1024 for GPT model checkpoints
+        # create a from-scratch initialized minGPT model
+        config = GPTConfig(**config_args)
+        model = GPT(config)
+        sd = model.state_dict()
+        sd_keys = sd.keys()
+        sd_keys = [k for k in sd_keys if not k.endswith('.attn.bias')] # discard this mask / buffer, not a param
+
+        # init a huggingface/transformers model
+        model_hf = GPT2LMHeadModel.from_pretrained(model_type)
+        sd_hf = model_hf.state_dict()
+
+        # copy while ensuring all of the parameters are aligned and match in names and shapes
+        sd_keys_hf = sd_hf.keys()
+        sd_keys_hf = [k for k in sd_keys_hf if not k.endswith('.attn.masked_bias')] # ignore these, just a buffer
+        sd_keys_hf = [k for k in sd_keys_hf if not k.endswith('.attn.bias')] # same, just the mask (buffer)
+        transposed = ['attn.c_attn.weight', 'attn.c_proj.weight', 'mlp.c_fc.weight', 'mlp.c_proj.weight']
+        # basically the openai checkpoints use a "Conv1D" module, but we only want to use a vanilla Linear
+        # this means that we have to transpose these weights when we import them
+        assert len(sd_keys_hf) == len(sd_keys), f"mismatched keys: {len(sd_keys_hf)} != {len(sd_keys)}"
+        for k in sd_keys_hf:
+            if any(k.endswith(w) for w in transposed):
+                # special treatment for the Conv1D weights we need to transpose
+                assert sd_hf[k].shape[::-1] == sd[k].shape
+                with torch.no_grad():
+                    sd[k].copy_(sd_hf[k].t())
+            else:
+                # vanilla copy over the other parameters
+                assert sd_hf[k].shape == sd[k].shape
+                with torch.no_grad():
+                    sd[k].copy_(sd_hf[k])
+
+        return model
+
+# model = GPT.from_pretrained('gpt2')
+
+import tiktoken
+
+class DataLoaderLite:
+    def __init__(self, B, T):
+        self.B = B
+        self.T = T
+
+        # at init load tokens from disk and store them in memory
+        with open('input.txt', 'r') as f:
+            text = f.read()
+        self.enc = tiktoken.get_encoding('gpt2') 
+        tokens = self.enc.encode(text)
+        self.tokens = torch.tensor(tokens)
+        print(f'loaded {len(self.tokens)} tokens')
+        print(f'1 epoch = {len(self.tokens) // (B * T)} batches')
+
+        # state
+        self.current_position = 0
+    
+    def next_batch(self):
+        B, T = self.B, self.T
+        buf = self.tokens[self.current_position: self.current_position + B * T + 1]
+        x = (buf[:-1]).view(B, T) # inputs
+        y = (buf[1:]).view(B, T) # targets
+        # advance the position in the tensor
+        self.current_position += B*T
+        # if loading the next batch would be out of bounds, reset
+        if self.current_position + (B * T + 1) > len(self.tokens):
+            self.current_position = 0
+        return x, y
+
+
+if __name__ == "__main__":
+    # SEED
+    device = 'cpu'
+    if torch.cuda.is_available():
+        device = 'cuda'
+    elif hasattr(torch.backends, "mps") and torch.backends.mps.is_available():
+        device = "mps"
+    print(f"using device: {device}")
+
+    torch.manual_seed(1337)
+    if torch.cuda.is_available():
+        torch.cuda.manual_seed(1337)
+
+    def count_parameters(model):
+        return sum(p.numel() for p in model.parameters() if p.requires_grad)
+
+    model = GPT(GPTConfig())
+    model.to(device)
+    print(f"Number of parameters: {count_parameters(model):,}")
+
+    train_loader = DataLoaderLite(B = 16, T = 128) #Only changed B = 16, T = 128
+    target_loss = 0.08
+    best_loss = float( 'inf')
+    # NEW CODE
+    optimizer = torch.optim.AdamW(model.parameters(), lr = 3e-4)
+    num_batches = len(train_loader.tokens) // (train_loader.B * train_loader.T)
+    for epoch in range(100):
+        # Training Loop
+        model.train()  # Make sure the model is in training mode
+        total_train_loss = 0.0
+        with tqdm(total=num_batches, desc=f'Epoch {epoch+1}/100', ncols=100, leave=True) as pbar:
+            for _ in range(num_batches):  # Iterate over batches
+
+                x, y = train_loader.next_batch()
+                x, y = x.to(device), y.to(device)
+                optimizer.zero_grad()
+                logits, loss = model(x, y)
+                loss.backward()
+                optimizer.step()
+                # Update the progress bar
+                pbar.set_postfix(loss=loss.item())  # Display loss in the progress bar
+                pbar.update(1)  # Increment progress by 1 step
+
+                # Check if the target loss is reached
+                if loss.item() < best_loss:
+                    best_loss = loss.item()
+                    if best_loss < target_loss:
+                        torch.save(model.state_dict(), "trained_model.pth")
+                        print(f"Target loss reached: {best_loss:.4f}. Model saved.")
+                        break
+
+    print( loss )
+
+
+
+    import sys; sys.exit(0)
+
+
+
+    # STOP
+    num_return_sequences = 5
+    max_length = 30
+
+
+    torch.manual_seed(42)
+    torch.cuda.manual_seed(42)
+    while x.size(1) < max_length:
+        # forward the model to get the logits
+        with torch.no_grad():
+            logits = model(x)[0] # (B, T, vocab_size)
+            # take the logits at the last position
+            logits = logits[:, -1, :] # (B, vocab_size)
+            # get the probabilities
+            probs = F.softmax(logits, dim=-1)
+            # do top-k sampling of 50 (huggingface pipeline default)
+            # topk_probs here becomes (5, 50), topk_indices is (5, 50)
+            topk_probs, topk_indices = torch.topk(probs, 50, dim=-1)
+            # select a token from the top-k probabilities
+            # note: multinomial does not demand the input to sum to 1
+            ix = torch.multinomial(topk_probs, 1) # (B, 1)
+            # gather the corresponding indices
+            xcol = torch.gather(topk_indices, -1, ix) # (B, 1)
+            # append to the sequence
+            x = torch.cat((x, xcol), dim=1)
+
+    # print the generated text
+    for i in range(num_return_sequences):
+        tokens = x[i, :max_length].tolist()
+        decoded = enc.decode(tokens)
+        print(">", decoded)

README.md

@@ -1,12 +1 @@
----
-title: TransformerModel
-emoji: ⚡
-colorFrom: red
-colorTo: blue
-sdk: gradio
-sdk_version: 5.12.0
-app_file: app.py
-pinned: false
----
-
-Check out the configuration reference at https://huggingface.co/docs/hub/spaces-config-reference
+# Assign12_ERAV3

app.py

@@ -0,0 +1,109 @@
+print("Step 1: Importing libraries...")
+import torch
+from transformers import GPT2Tokenizer
+import gradio as gr
+from Assign12_Model import GPT, GPTConfig
+import torchvision
+torchvision.disable_beta_transforms_warning()
+
+print("Step 2: Loading the model...")
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+config = GPTConfig(block_size=1024, vocab_size=50257, n_layer=12, n_head=12, n_embd=768)
+model = GPT(config)
+
+print("Step 3: Loading model weights...")
+model.load_state_dict(torch.load("trained_model.pth", map_location=device, weights_only=True))
+model.eval().to(device)
+
+print("Step 4: Loading tokenizer...")
+tokenizer = GPT2Tokenizer.from_pretrained("gpt2")
+
+# Add print statements in the function
+print("Step 5: Defining the inference function...")
+def generate_text(prompt, max_length=50, num_return_sequences=1):
+    print(f"Received input prompt: {prompt}")
+    inputs = tokenizer.encode(prompt, return_tensors="pt").to(device)
+    outputs = []
+    for _ in range(num_return_sequences):
+        with torch.no_grad():
+            logits, _ = model(inputs)
+            generated_token = torch.argmax(logits[:, -1, :], dim=-1)
+            inputs = torch.cat((inputs, generated_token.unsqueeze(0)), dim=1)
+            if inputs.size(1) >= max_length:
+                break
+        output = tokenizer.decode(inputs[0].tolist())
+        outputs.append(output)
+    return outputs
+
+import os
+import torch
+import torch.nn.functional as F
+from transformers import GPT2LMHeadModel, GPT2Tokenizer
+import gradio as gr
+
+# Initialize model and tokenizer
+model_name = 'gpt2'  # You can replace this with your specific model
+model = GPT2LMHeadModel.from_pretrained(model_name)
+tokenizer = GPT2Tokenizer.from_pretrained(model_name)
+model.eval()
+
+# Ensure we're using CUDA if available
+device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
+model.to(device)
+
+# Define parameters
+max_length = 100
+num_return_sequences = 1  # Default number of sequences to generate
+
+# Function to generate text
+def generate_text(prompt, max_len=50, num_outputs=1):
+    global max_length, num_return_sequences
+    
+    max_length = max_len
+    num_return_sequences = num_outputs
+    
+    # Encode the input text
+    input_ids = tokenizer.encode(prompt, return_tensors='pt').to(device)
+
+    generated_sequences = []  # List to store generated text
+
+    # Generate sequences
+    with torch.no_grad():
+        for i in range(num_return_sequences):
+            x = input_ids.clone()
+            while x.size(1) < max_length:
+                logits = model(x).logits  # (B, T, vocab_size)
+                logits = logits[:, -1, :]  # (B, vocab_size)
+                probs = F.softmax(logits, dim=-1)
+                topk_probs, topk_indices = torch.topk(probs, 50, dim=-1)
+                ix = torch.multinomial(topk_probs, 1)  # (B, 1)
+                xcol = torch.gather(topk_indices, -1, ix)  # (B, 1)
+                x = torch.cat((x, xcol), dim=1)
+
+            # Decode the generated tokens and append it to the list
+            tokens = x[0, :max_length].tolist()
+            decoded = tokenizer.decode(tokens, skip_special_tokens=True)
+            generated_sequences.append(f"Generated Text {i+1}:")
+            generated_sequences.append(f"> {decoded}\n")
+
+    # Join the generated sequences into a structured output
+    structured_output = "\n".join(generated_sequences)
+    
+    return structured_output
+
+# Set up Gradio interface
+print("Step 6: Setting up the Gradio interface...")
+interface = gr.Interface(
+    fn=generate_text,
+    inputs=[
+        gr.Textbox(label="Input Prompt"),
+        gr.Slider(10, 200, step=10, label="Max Length", value=50),
+        gr.Number(label="Number of Outputs", value=10),
+    ],
+    outputs=gr.Textbox(label="Generated Text"),
+    title="Transformer Text Generator",
+    description="Enter a prompt and generate text using the trained transformer model.",
+)
+
+print("Step 7: Launching the Gradio interface...")
+interface.launch(share=True)

requirements.txt

@@ -0,0 +1,5 @@
+torch
+transformers
+gradio
+tqdm
+torchvision

trained_model.pth

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:b2b357993c1ef80272691ea3e6a09e88bab5b28d03005ac0443b56fdcd1faedb
+size 548146637