model v8

.gitattributes

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+*.pth filter=lfs diff=lfs merge=lfs -text

README.md

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+# lafontaine-gpt
+A rudimentary gpt model trained on the Fables de La Fontaine.

bigram_model.py

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+import os
+import torch
+import onnx
+import torch.nn as nn
+from torch.nn import functional as F
+from datetime import datetime
+torch.manual_seed(1337)  # for reproducibility
+
+SEP = 50 * '-'
+
+# hyperparameters ----------------------------------------------------------------------------------
+batch_size = 64  # how many independent sequences will we process in parallel
+block_size = 256  # what i sthe maximum context length for predictions
+max_iters = 5000  # how many iterations to train for
+eval_interval = 500  # how often to evaluate the model
+learning_rate = 3e-4  # how fast we update the weights, lowering the learning rate as the model gets bigger
+device = 'cuda' if torch.cuda.is_available() else 'cpu'  # check if GPU is available
+eval_iters = 200  # how many batches to average for evaluation
+n_embd = 384  # number of embedding dimensions
+n_head = 6  # number of self-attention heads
+n_layer = 6  # number of transformer blocks
+dropout = 0.2  # dropout rate
+
+# dataset ------------------------------------------------------------------------------------------
+dataset_path = 'dataset/tiny-lafontaine.txt'
+with open(dataset_path, 'r', encoding='utf-8') as f:
+    text = f.read()
+
+# here are all the unique characters that occur in this text
+chars = sorted(list(set(text)))
+vocab_size = len(chars)
+
+# create a mapping from characters to integers
+stoi = {ch: i for i, ch in enumerate(chars)}  # chars -> ints table
+itos = {i: ch for i, ch in enumerate(chars)}  # ints -> chars table
+encode = lambda s: [stoi[c] for c in s]  # encoder: takes a string, outputs a list of integers
+decode = lambda l: ''.join([itos[i] for i in l])  # decoder: takes a list of integers, output a string
+
+# train and test splits
+data = torch.tensor(encode(text), dtype=torch.long)
+n = int(0.9 * len(data))   # first 90% of the data will be the training set, rest will be the validation set
+train_data = data[:n]
+val_data = data[n:]
+
+
+# data loading -------------------------------------------------------------------------------------
+def get_batch(split):
+    # Generate a small batch of data of inputs x and targets y
+    data = train_data if split == 'train' else val_data  # choose the split
+    ix = torch.randint(len(data) - block_size, (batch_size,))  # sample random starting indices for the sequences
+    x = torch.stack([data[i: i + block_size] for i in ix])  # create a batch of context windows
+    y = torch.stack([data[i + 1:i + block_size + 1] for i in ix])  # create a batch of targets, one step forward
+    x, y = x.to(device), y.to(device)  # move the data to the device
+    return x, y
+
+
+@torch.no_grad()  # this is just to reduce memory consumption, block won't call backward, no back-propagation
+def estimate_loss():
+    out = {}  # store the losses for the train and val splits
+    model.eval()  # switch to evaluation mode
+    for split in ['train', 'val']:  # iterate over both splits
+        losses = torch.zeros(eval_iters)  # store the loss for each batch
+        for k in range(eval_iters):  # iterate over the number of batches
+            X, Y = get_batch(split)  # get a batch of data
+            _, loss = model(X, Y)  # compute the loss
+            losses[k] = loss.item()  # store the loss
+        out[split] = losses.mean()  # store the average loss for the split
+    model.train()  # switch back to training mode
+    return out  # return the losses
+
+
+# self-attention head ------------------------------------------------------------------------------
+class Head(nn.Module):
+
+    def __init__(self, head_size):
+        super().__init__()
+        self.key = nn.Linear(n_embd, head_size, bias=False)  # key projection
+        self.query = nn.Linear(n_embd, head_size, bias=False)  # query projection
+        self.value = nn.Linear(n_embd, head_size, bias=False)  # value projection
+        self.register_buffer('tril', torch.tril(torch.ones(block_size, block_size)))  # causal mask
+        self.dropout = nn.Dropout(dropout)  # dropout layer
+
+    def forward(self, x):
+        B, T, C = x.shape
+        k = self.key(x)  # (B, T, C)
+        q = self.query(x)  # (B, T, C)
+        # compute attention scores ("affinities")
+        wei = q @ k.transpose(-2, -1) * C**-0.5  # (B, T, T) @ (B, C, T) -> (B, T, T)
+        wei = wei.masked_fill(self.tril[:T, :T] == 0, float('-inf'))  # (B, T, T)
+        wei = F.softmax(wei, dim=-1)  # (B, T, T)
+        wei = self.dropout(wei)  # apply dropout
+        # perform the weighted aggregation of the values
+        v = self.value(x)
+        out = wei @ v  # (B, T, T) @ (B, T, C) -> (B, T, C)
+        return out
+
+
+# multi-attention head -----------------------------------------------------------------------------
+class MultiHeadAttention(nn.Module):
+    """multiple heads of self-attention in parallel"""
+
+    def __init__(self, num_heads, head_size):
+        super().__init__()
+        self.heads = nn.ModuleList([Head(head_size) for _ in range(num_heads)])  # create n_heads heads
+        self.proj = nn.Linear(n_embd, n_embd)  # linear projection to get back to the original dimension
+
+    def forward(self, x):
+        out = torch.cat([h(x) for h in self.heads], dim=-1)  # concatenate the outputs of each head
+        out = self.proj(out)  # linear projection to get back to the original dimension
+        return out
+
+
+# feedforward block --------------------------------------------------------------------------------
+class FeedForward(nn.Module):
+    """a simple linear layer followed by a non-linearity"""
+
+    def __init__(self, n_embd):
+        super().__init__()  # call the constructor of the parent class
+        self.net = nn.Sequential(
+            nn.Linear(n_embd, 4 * n_embd),  # linear layer
+            nn.ReLU(),  # activation function
+            nn.Linear(4 * n_embd, n_embd),  # projection layer to get back to the original dimension
+            nn.Dropout(dropout),  # dropout layer
+        )
+
+    def forward(self, x):
+        return self.net(x)  # apply the feedforward block
+
+
+# transformer block --------------------------------------------------------------------------------
+class Block(nn.Module):
+    """ Transformer block: communication followed by computation """
+
+    def __init__(self, n_embd, n_head):
+        # n_embd: embedding dimension, n_head: number of heads we'd like
+        super().__init__()
+        head_size = n_embd // n_head  # size of the self-attention heads
+        self.sa = MultiHeadAttention(n_head, head_size)  # self-attention layer
+        self.ffwd = FeedForward(n_embd)  # feedforward block
+        self.ln1 = nn.LayerNorm(n_embd)  # layer normalization
+        self.ln2 = nn.LayerNorm(n_embd)  # layer normalization
+
+    def forward(self, x):
+        x = x + self.sa(self.ln1(x))  # apply the self-attention block. Layer normalization is applied before
+        x = x + self.ffwd(self.ln2(x))  # apply the feedforward block. Layer normalization is applied before
+        return x
+
+
+# simple bigram model ------------------------------------------------------------------------------
+class BigramLanguageModel(nn.Module):
+
+    def __init__(self):
+        super().__init__()
+        # each token directly reads off the logits from the next token from a lookup table
+        self.token_embedding_table = nn.Embedding(vocab_size, n_embd)  # token embeddings
+        self.position_embedding_table = nn.Embedding(block_size, n_embd)  # positional embeddings
+        self.blocks = nn.Sequential(*[Block(n_embd, n_head=n_head) for _ in range(n_layer)])  # stack of transformer blocks
+        self.ln_f = nn.LayerNorm(n_embd),  # final layer normalization
+        self.lm_head = nn.Linear(n_embd, vocab_size)  # output layer
+
+    def forward(self, idx, targets=None):
+        B, T = idx.shape
+
+        # idx and targets are both (B, T) tensors of integers
+        tok_emb = self.token_embedding_table(idx)  # (B, T, C) = Batch, Time (block_size), Channels (vocab_size)
+        pos_emb = self.position_embedding_table(torch.arange(T, device=device))  # (T, C)
+        x = tok_emb + pos_emb  # (B, T, C)
+        x = self.blocks(x)  # apply the transformer blocks, multiple layers of self-attention and feedforward, (B, T, C)
+        logits = self.lm_head(x)  # decoder head (B, T, vocab_size)
+
+        if targets is None:  # if we don't have targets, we can't compute the loss
+            loss = None
+
+        else:
+            # reshape the logits to be (B*T, C) and the targets to be (B*T) so we can compute the loss
+            B, T, C = logits.shape  # unpack batch, time, channels
+            logits = logits.view(B * T, C)  # flatten the Time and Batch dimensions
+            targets = targets.view(B * T)  # flatten the Time and Batch dimensions
+
+            # compute the loss using cross entropy = quality of the logicts in respect to the targets
+            loss = F.cross_entropy(logits, targets)
+
+        return logits, loss
+
+    def generate(self, idx, max_new_tokens):
+        # idx is a (B, T) array of indices in the current context
+        for _ in range(max_new_tokens):
+            # crop idx to the last block_size tokens
+            idx_cond = idx[:, -block_size:]  # (B, T)
+            # get the predictions
+            logits, loss = self(idx_cond)  # (B, T, C)  internally calls the forward method in pytorch
+            # focus only on the last time step
+            logits = logits[:, -1, :]  # becomes (B, C)
+            # apply softmax to get probabilities
+            probs = F.softmax(logits, dim=-1)  # (B, C)
+            # sample from the distribution
+            idx_next = torch.multinomial(probs, num_samples=1)  # (B, 1)
+            # append sampled index to the running sequence
+            idx = torch.cat((idx, idx_next), dim=1)  # (B, T+1)
+
+        return idx
+
+
+# train model --------------------------------------------------------------------------------------
+def train_model():
+    # create the model and optimizer
+    model = BigramLanguageModel()
+    m = model.to(device)  # move the model to the device (cuda)
+
+    # create a PyTorch optimizer
+    optimizer = torch.optim.AdamW(m.parameters(), lr=learning_rate)  # AdamW is a good optimizer for transformers
+
+    # training loop ------------------------------------------------------------------------------------
+    for iter in range(max_iters):
+
+        # every once in a while evaluate the loss on the train and val sets
+        if iter % eval_interval == 0:
+            losses = estimate_loss()
+            print(f"step {iter}: train loss {losses['train']:.4f}, val loss {losses['val']:.4f}")
+
+        # sample a batch of data
+        xb, yb = get_batch('train')
+
+        # evaluate the loss
+        _, loss = m(xb, yb)  # calling the model and passing in the input and the targets
+        optimizer.zero_grad(set_to_none=True)  # clear previous gradients
+        loss.backward()  # compute new gradients
+        optimizer.step()  # update the weights
+
+    # generate from the model
+    context = torch.zeros((1, 1), dtype=torch.long, device=device)  # initialize context to be a single token
+    print(decode(m.generate(context, max_new_tokens=500)[0].tolist()))  # generate 100 new tokens
+
+    # save model
+    save_model(model)
+
+    return m
+
+
+# save model ---------------------------------------------------------------------------------------
+def save_model(model, save_path=None):
+    try:
+        if save_path is None:
+            filename = os.path.splitext(os.path.basename(__file__))[0]
+            timestamp = datetime.now().strftime('%y%m%d_%H%M')
+            save_path = f'{filename}_{timestamp}.pth'
+
+        torch.save(model.state_dict(), save_path)
+        print(f"Model saved to {save_path}.")
+        return save_path
+
+    except Exception as e:
+        print(f"Error saving the model: {e}")
+
+
+# load model ---------------------------------------------------------------------------------------
+def load_model(model_path):
+    try:
+        # Load the model
+        device = 'cuda' if torch.cuda.is_available() else 'cpu'
+        model = BigramLanguageModel().to(device)
+        model.load_state_dict(torch.load(model_path, map_location=device, weights_only=True))
+        print(f"Model loaded from {model_path}.")
+        return model
+
+    except Exception as e:
+        print(f"Error loading the model: {e}")
+
+
+# run inference ------------------------------------------------------------------------------------
+def run_inference(model, max_tokens=500):
+    # Set to evaluation mode
+    model.eval()
+    # Define a starting context and run inference
+    context = torch.zeros((1, 1), dtype=torch.long, device=device)  # Initialize with a single token
+    generated_sequence = model.generate(context, max_tokens)  # Generate text
+    generated_text = decode(generated_sequence[0].tolist())  # Decode the generated indices to text
+    return generated_text
+
+
+# export model to onnx format ----------------------------------------------------------------------
+def export_onnx_model(pt_model, onnx_path):
+    try:
+        # Dummy input tensor of the same shape as your training input
+        dummy_input = torch.zeros((1, 256), dtype=torch.long).to(device)  # Example input shape
+
+        # Export the model to ONNX format
+        torch.onnx.export(
+            pt_model,  # your trained model
+            dummy_input,  # example input tensor
+            onnx_path,  # output file path
+            input_names=["input"],  # input layer names
+            output_names=["output"],  # output layer names
+            dynamic_axes={"input": {0: "batch_size"}, "output": {0: "batch_size"}},  # dynamic axis support
+            opset_version=13  # compatibility with latest ONNX version
+        )
+
+        print(f"Model exported to {onnx_path}.")
+
+    except Exception as e:
+        print(f"Error exporting the onnx model: {e}")
+
+
+if __name__ == '__main__':
+
+    # train model
+    model = train_model()

gradio_app.py

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+import gradio as gr
+import torch
+from bigram_model import BigramLanguageModel, encode, decode
+
+# Assuming 'BigramLanguageModel' and 'decode' are defined as in your code
+
+class GradioInterface:
+    def __init__(self, model_path=None):
+        self.device = 'cuda' if torch.cuda.is_available() else 'cpu'
+        self.model = self.load_model(model_path)
+        self.model.eval()
+
+    def load_model(self, model_path):
+        model = BigramLanguageModel().to(self.device)
+        if model_path:
+            model.load_state_dict(torch.load(model_path, map_location=self.device))
+        return model
+
+    def generate_text(self, input_text, max_tokens=100):
+        context = torch.tensor([encode(input_text)], dtype=torch.long, device=self.device)
+        output = self.model.generate(context, max_new_tokens=max_tokens)
+        return decode(output[0].tolist())
+
+# Load the model
+model_path = "models/lafontaine_gpt_v8_241011_1307.pth"
+model_interface = GradioInterface(model_path)
+
+# Define Gradio interface
+gr_interface = gr.Interface(
+    fn=model_interface.generate_text,
+    inputs=["text", gr.Slider(50, 500)],
+    outputs="text",
+    description="Bigram Language Model text generation. Enter some text, and the model will continue it.",
+    examples=[["Once upon a time"]]
+)
+
+# Launch the interface
+gr_interface.launch()

lafontaine_gpt_v1.pth

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