app.py

import copy
import torch
import math
import torch.nn as nn
from torch.nn.parameter import Parameter
import random
import numpy as np
from load_weights import load_weight
from sklearn.model_selection import train_test_split
from transformers import GPT2TokenizerFast
import pandas as pd
from torch.utils.data import Dataset, DataLoader
from transformers import AdamW, get_linear_schedule_with_warmup
torch.manual_seed(42)
import nltk
# nltk.download('punkt')

from transformers import GPT2Tokenizer
from torch.utils.data import Dataset, DataLoader, random_split, RandomSampler, SequentialSampler
import datetime
import time
import os
os.environ["CUDA_LAUNCH_BLOCKING"] = "1"
from tqdm import trange
import gradio as gr



def gelu(x):
    return 0.5 * x * (1 + torch.tanh(math.sqrt(2 / math.pi) * (x + 0.044715 * torch.pow(x, 3))))

class Conv1D(nn.Module):
    def __init__(self, nf, nx):
        super(Conv1D, self).__init__()
        self.nf = nf
        w = torch.empty(nx, nf)
        nn.init.normal_(w, std=0.02)
        self.weight = Parameter(w)
        self.bias = Parameter(torch.zeros(nf))

    def forward(self, x):
        size_out = x.size()[:-1] + (self.nf,)
        x = torch.addmm(self.bias, x.view(-1, x.size(-1)), self.weight)
        x = x.view(*size_out)
        return x
    
class LayerNorm(nn.Module):
    def __init__(self, hidden_size, eps=1e-12):
        """Construct a layernorm module in the TF style (epsilon inside the square root).
        """
        super(LayerNorm, self).__init__()
        self.weight = nn.Parameter(torch.ones(hidden_size))
        self.bias = nn.Parameter(torch.zeros(hidden_size))
        self.variance_epsilon = eps

    def forward(self, x):
        u = x.mean(-1, keepdim=True)
        s = (x - u).pow(2).mean(-1, keepdim=True)
        x = (x - u) / torch.sqrt(s + self.variance_epsilon)
        return self.weight * x + self.bias
    


class Attention(nn.Module):
    def __init__(self, nx, n_ctx, config, scale=False):
        super(Attention, self).__init__()
        n_state = nx  # in Attention: n_state=768 (nx=n_embd)
        # [switch nx => n_state from Block to Attention to keep identical to TF implem]
        assert n_state % config.n_head == 0
        self.register_buffer("bias", torch.tril(torch.ones(n_ctx, n_ctx)).view(1, 1, n_ctx, n_ctx))
        self.n_head = config.n_head
        self.split_size = n_state
        self.scale = scale
        self.c_attn = Conv1D(n_state * 3, nx)
        self.c_proj = Conv1D(n_state, nx)

    def _attn(self, q, k, v):
        w = torch.matmul(q, k)
        if self.scale:
            w = w / math.sqrt(v.size(-1))
        nd, ns = w.size(-2), w.size(-1)
        b = self.bias[:, :, ns-nd:ns, :ns]
        w = w * b - 1e10 * (1 - b)
        w = nn.Softmax(dim=-1)(w)
        return torch.matmul(w, v)

    def merge_heads(self, x):
        x = x.permute(0, 2, 1, 3).contiguous()
        new_x_shape = x.size()[:-2] + (x.size(-2) * x.size(-1),)
        return x.view(*new_x_shape)  # in Tensorflow implem: fct merge_states

    def split_heads(self, x, k=False):
        new_x_shape = x.size()[:-1] + (self.n_head, x.size(-1) // self.n_head)
        x = x.view(*new_x_shape)  # in Tensorflow implem: fct split_states
        if k:
            return x.permute(0, 2, 3, 1)  # (batch, head, head_features, seq_length)
        else:
            return x.permute(0, 2, 1, 3)  # (batch, head, seq_length, head_features)

    def forward(self, x, layer_past=None):
        x = self.c_attn(x)
        query, key, value = x.split(self.split_size, dim=2)
        query = self.split_heads(query)
        key = self.split_heads(key, k=True)
        value = self.split_heads(value)
        if layer_past is not None:
            past_key, past_value = layer_past[0].transpose(-2, -1), layer_past[1]  # transpose back cf below
            key = torch.cat((past_key, key), dim=-1)
            value = torch.cat((past_value, value), dim=-2)
        present = torch.stack((key.transpose(-2, -1), value))  # transpose to have same shapes for stacking
        a = self._attn(query, key, value)
        a = self.merge_heads(a)
        a = self.c_proj(a)
        return a, present
    

class MLP(nn.Module):
    def __init__(self, n_state, config):  # in MLP: n_state=3072 (4 * n_embd)
        super(MLP, self).__init__()
        nx = config.n_embd
        self.c_fc = Conv1D(n_state, nx)
        self.c_proj = Conv1D(nx, n_state)
        self.act = gelu

    def forward(self, x):
        h = self.act(self.c_fc(x))
        h2 = self.c_proj(h)
        return h2
    

class Block(nn.Module):
    def __init__(self, n_ctx, config, scale=False):
        super(Block, self).__init__()
        nx = config.n_embd
        self.ln_1 = LayerNorm(nx, eps=config.layer_norm_epsilon)
        self.attn = Attention(nx, n_ctx, config, scale)
        self.ln_2 = LayerNorm(nx, eps=config.layer_norm_epsilon)
        self.mlp = MLP(4 * nx, config)

    def forward(self, x, layer_past=None):
        a, present = self.attn(self.ln_1(x), layer_past=layer_past)
        x = x + a
        m = self.mlp(self.ln_2(x))
        x = x + m
        return x, present
    


class GPT2Model(nn.Module):
    def __init__(self, config):
        super(GPT2Model, self).__init__()
        self.n_layer = config.n_layer
        self.n_embd = config.n_embd
        self.n_vocab = config.vocab_size

        self.wte = nn.Embedding(config.vocab_size, config.n_embd)
        self.wpe = nn.Embedding(config.n_positions, config.n_embd)
        block = Block(config.n_ctx, config, scale=True)
        self.h = nn.ModuleList([copy.deepcopy(block) for _ in range(config.n_layer)])
        self.ln_f = LayerNorm(config.n_embd, eps=config.layer_norm_epsilon)

    def set_embeddings_weights(self, model_embeddings_weights):
        embed_shape = model_embeddings_weights.shape
        self.decoder = nn.Linear(embed_shape[1], embed_shape[0], bias=False)
        self.decoder.weight = model_embeddings_weights  # Tied weights

    

    def forward(self, input_ids, position_ids=None, token_type_ids=None, past=None):

        if (input_ids >= self.n_vocab).any():
            raise ValueError(f"Invalid token ID found in input_ids: {input_ids}")

        # print(f"input_ids: {input_ids}")  # Debugging statement
        # print(f"Max input_id: {input_ids.max().item()}")  # Debugging statement
        # print(f"Min input_id: {input_ids.min().item()}")  # Debugging statement

        if past is None:
            past_length = 0
            past = [None] * len(self.h)
        else:
            past_length = past[0][0].size(-2)
        if position_ids is None:
            position_ids = torch.arange(past_length, input_ids.size(-1) + past_length, dtype=torch.long,
                                        device=input_ids.device)
            position_ids = position_ids.unsqueeze(0).expand_as(input_ids)

        input_shape = input_ids.size()
        input_ids = input_ids.view(-1, input_ids.size(-1))
        position_ids = position_ids.view(-1, position_ids.size(-1))

        inputs_embeds = self.wte(input_ids)
        position_embeds = self.wpe(position_ids)

        # print(f"inputs_embeds shape: {inputs_embeds.shape}")
        # print(f"position_embeds shape: {position_embeds.shape}")


        if token_type_ids is not None:
            token_type_ids = token_type_ids.view(-1, token_type_ids.size(-1))
            token_type_embeds = self.wte(token_type_ids)
        else:
            token_type_embeds = 0
        hidden_states = inputs_embeds + position_embeds + token_type_embeds
        presents = []
        for block, layer_past in zip(self.h, past):
            hidden_states, present = block(hidden_states, layer_past)
            presents.append(present)
        hidden_states = self.ln_f(hidden_states)
        output_shape = input_shape + (hidden_states.size(-1),)
        return hidden_states.view(*output_shape), presents

class GPT2LMHead(nn.Module):
    def __init__(self, model_embeddings_weights, config):
        super(GPT2LMHead, self).__init__()
        self.n_embd = config.n_embd
        self.set_embeddings_weights(model_embeddings_weights)

    def set_embeddings_weights(self, model_embeddings_weights):
        embed_shape = model_embeddings_weights.shape
        self.decoder = nn.Linear(embed_shape[1], embed_shape[0], bias=False)
        self.decoder.weight = model_embeddings_weights  # Tied weights

    def forward(self, hidden_state):
        # Truncated Language modeling logits (we remove the last token)
        # h_trunc = h[:, :-1].contiguous().view(-1, self.n_embd)
        lm_logits = self.decoder(hidden_state)
        return lm_logits
    
import torch.nn.functional as F

def top_k_top_p_filtering(logits, top_k=0, top_p=0.0, filter_value=-float('Inf')):
        """ Filter a distribution of logits using top-k and/or nucleus (top-p) filtering
            Args:
                logits: logits distribution shape (batch size, vocabulary size)
                top_k > 0: keep only top k tokens with highest probability (top-k filtering).
                top_p > 0.0: keep the top tokens with cumulative probability >= top_p (nucleus filtering).
                filter_value: value to replace filtered logits.
        """
        assert logits.dim() == 2  # batch size x vocabulary size
        top_k = min(top_k, logits.size(-1))  # Safety check
        if top_k > 0:
            # Remove all tokens with a probability less than the last token of the top-k
            indices_to_remove = logits < torch.topk(logits, top_k)[0][..., -1, None]
            logits[indices_to_remove] = filter_value

        if top_p > 0.0:
            sorted_logits, sorted_indices = torch.sort(logits, descending=True)
            cumulative_probs = torch.cumsum(F.softmax(sorted_logits, dim=-1), dim=-1)

            # Remove tokens with cumulative probability above the threshold
            sorted_indices_to_remove = cumulative_probs > top_p
            # Shift the indices to the right to keep also the first token above the threshold
            sorted_indices_to_remove[..., 1:] = sorted_indices_to_remove[..., :-1].clone()
            sorted_indices_to_remove[..., 0] = 0

            indices_to_remove = sorted_indices[sorted_indices_to_remove]
            logits[indices_to_remove] = filter_value
        return logits


class GPT2LMHeadModel(nn.Module):
    def __init__(self, config):
        super(GPT2LMHeadModel, self).__init__()
        self.transformer = GPT2Model(config)
        self.lm_head = GPT2LMHead(self.transformer.wte.weight, config)

    def set_tied(self):
        """ Make sure we are sharing the embeddings
        """
        self.lm_head.set_embeddings_weights(self.transformer.wte.weight)

    def forward(self, input_ids, position_ids=None, token_type_ids=None, lm_labels=None, past=None):
        hidden_states, presents = self.transformer(input_ids, position_ids, token_type_ids, past)
        lm_logits = self.lm_head(hidden_states)

        outputs = (lm_logits,presents)

        if lm_labels is not None:
            shift_logits = lm_logits[..., :-1, :].contiguous()
            shift_labels = lm_labels[..., 1:].contiguous()
            loss_fct = nn.CrossEntropyLoss()
            loss = loss_fct(shift_logits.view(-1, shift_logits.size(-1)), shift_labels.view(-1))
            outputs = (loss,) + outputs
        return outputs
    
    import torch.nn.functional as F

    
    
    def generate(
        self, input_ids, max_length, temperature=1.0, top_k=0, top_p=0.9, repetition_penalty=1.0, device='cuda'
    ):
        self.eval()
        input_ids = input_ids.to(device)
        batch_size = input_ids.shape[0]
        past = None

        generated = input_ids
        with torch.no_grad():
            for _ in range(max_length):
                outputs = self(input_ids, past=past)
                next_token_logits = outputs[0][:, -1, :]
                past = outputs[1]

                for i in range(batch_size):
                    for token_id in set(generated[i].tolist()):
                        next_token_logits[i, token_id] /= repetition_penalty

                next_token_logits = next_token_logits / temperature
                filtered_logits = top_k_top_p_filtering(next_token_logits, top_k=top_k, top_p=top_p)
                next_token = torch.multinomial(F.softmax(filtered_logits, dim=-1), num_samples=1)
                generated = torch.cat((generated, next_token), dim=1)

                if (next_token == self.config.eos_token_id).all():
                    break

                input_ids = next_token

        return generated
        

class GPT2Config(object):
    def __init__(
            self,
            vocab_size_or_config_json_file=50257,
            n_positions=1024,
            n_ctx=1024,
            n_embd=768,
            n_layer=12,
            n_head=12,
            layer_norm_epsilon=1e-5,
            initializer_range=0.02,
    ):
        self.vocab_size = vocab_size_or_config_json_file
        self.n_ctx = n_ctx
        self.n_positions = n_positions
        self.n_embd = n_embd
        self.n_layer = n_layer
        self.n_head = n_head
        self.layer_norm_epsilon = layer_norm_epsilon
        self.initializer_range = initializer_range



device = torch.device("cuda" if torch.cuda.is_available() else "cpu")
config = GPT2Config()
model = GPT2LMHeadModel(config)
state_dict = torch.load(r'epoch_4.pth', map_location='cpu' if not torch.cuda.is_available() else None)
model = load_weight(model, state_dict)
model.to(device)
print(model)
model.eval()

tokenizer = GPT2Tokenizer.from_pretrained('gpt2')
tokenizer.pad_token = tokenizer.eos_token



def top_k_top_p_filtering(logits, top_k=0, top_p=0.0, filter_value=-float('Inf')):
    """ Filter a distribution of logits using top-k and/or nucleus (top-p) filtering
        Args:
            logits: logits distribution shape (batch size x vocabulary size)
            top_k > 0: keep only top k tokens with highest probability (top-k filtering).
            top_p > 0.0: keep the top tokens with cumulative probability >= top_p (nucleus filtering).
                Nucleus filtering is described in Holtzman et al. (http://arxiv.org/abs/1904.09751)
    """
    assert logits.dim() == 2, "Expected logits dimension to be 2 (batch size x vocabulary size)"
    top_k = min(top_k, logits.size(-1))  # Safety check
    if top_k > 0:
        # Remove all tokens with a probability less than the last token of the top-k
        indices_to_remove = logits < torch.topk(logits, top_k)[0][..., -1, None]
        logits[indices_to_remove] = filter_value

    if top_p > 0.0:
        sorted_logits, sorted_indices = torch.sort(logits, descending=True)
        cumulative_probs = torch.cumsum(nn.Softmax(dim=-1)(sorted_logits), dim=-1)

        # Remove tokens with cumulative probability above the threshold
        sorted_indices_to_remove = cumulative_probs > top_p
        # Shift the indices to the right to keep also the first token above the threshold
        sorted_indices_to_remove[..., 1:] = sorted_indices_to_remove[..., :-1].clone()
        sorted_indices_to_remove[..., 0] = 0

        # Ensure that the dimensions match
        if sorted_indices_to_remove.size() != sorted_indices.size():
            raise ValueError(f"Size mismatch: {sorted_indices_to_remove.size()} vs {sorted_indices.size()}")

        indices_to_remove = sorted_indices[sorted_indices_to_remove]

        # Expand dimensions to match logits tensor and use scatter_
        for batch_idx in range(logits.size(0)):
            logits[batch_idx, indices_to_remove[batch_idx]] = filter_value
            
    return logits

# prompt_text = "What is a nucleophile in organic chemistry?"
# prompt = f"\n<|startoftext|>[WP] {prompt_text} \n[RESPONSE]"
# input_ids = tokenizer.encode(prompt, return_tensors='pt').to(device)


max_length = 100
temperature = 0.7
top_k = 1
top_p = 0.95
repetition_penalty = 1.0

with torch.no_grad():
    for _ in range(max_length):
        outputs = model(input_ids)
        logits = outputs[0]
        next_token_logits = logits[:, -1, :] / temperature

        # Apply repetition penalty
        for i in range(input_ids.size(0)):
            for token_id in set(input_ids[i].tolist()):
                next_token_logits[0, token_id] /= repetition_penalty

        # Filter logits using top-k and/or top-p filtering
        filtered_logits = top_k_top_p_filtering(next_token_logits, top_k=top_k, top_p=top_p)
        next_token = torch.multinomial(F.softmax(filtered_logits, dim=-1), num_samples=1)
        input_ids = torch.cat([input_ids, next_token], dim=-1).to(device)


# import re
# # generated_text = tokenizer.decode(input_ids[0], skip_special_tokens=True)
# # wp_responses = re.split(r"\[WP\].*?\n|\[RESPONSE\]", generated_text)[1:]
# print(input_ids[0])

# generated_text = tokenizer.decode(input_ids[0], skip_special_tokens=True)
# wp_responses = re.split(r"\[WP\].*?\n|\[RESPONSE\]", generated_text)[1:]
# print(wp_responses)

# Create a Gradio interface
iface = gr.Interface(
    fn=generate_response,
    inputs="text",
    outputs="text",
    title="Custom GPT-2 Model",
    description="Enter a prompt to get a generated response from the custom-trained GPT-2 model.",
    examples=[
        ["What is a nucleophile in organic chemistry?"],
        ["Explain the concept of quantum entanglement."],
        ["How does photosynthesis work?"]
    ]
)

# Launch the Gradio interface
iface.launch(share=True, debug=True)