Update app.py

app.py

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+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'weights/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)
+
+