CustomGPT / model_class.py

Update model_class.py

f76e395 verified 10 months ago

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import torch
import torch.nn as nn
from transformers import PreTrainedModel, PretrainedConfig


class MultiheadAttention(nn.Module):
    def __init__(self, d_in, d_out, context_length, dropout, num_heads, qkv_bias=False):
        super().__init__()

        self.d_out = d_out
        self.num_heads = num_heads
        self.head_dim = d_out // num_heads

        #step 3
        self.W_query = nn.Linear(d_in, d_out, bias=qkv_bias)
        self.W_key = nn.Linear(d_in, d_out, bias=qkv_bias)
        self.W_value = nn.Linear(d_in, d_out, bias=qkv_bias)

        self.out_proj = nn.Linear(d_out, d_out)
        self.dropout = nn.Dropout(dropout)
        self.register_buffer("mask",torch.triu(torch.ones(context_length, context_length), diagonal=1))


    def forward(self, x):
        b, num_tokens, d_in = x.shape

        #step 4
        keys = self.W_key(x)
        queries = self.W_query(x)
        values = self.W_value(x)

        #step 5
        keys = keys.view(b, num_tokens, self.num_heads, self.head_dim)
        queries = queries.view(b, num_tokens, self.num_heads, self.head_dim)
        values = values.view(b, num_tokens, self.num_heads, self.head_dim)

        #step 6
        keys = keys.transpose(1,2)
        queries = queries.transpose(1,2)
        values = values.transpose(1,2)

        #step 7
        attn_scores = queries @ keys.transpose(2,3)

        #step 8
        mask_bool = self.mask.bool()[:num_tokens, :num_tokens]
        attn_scores.masked_fill_(mask_bool, -torch.inf)

        attn_weights = torch.softmax(attn_scores / keys.shape[-1]**0.5, dim=-1)
        attn_weights = self.dropout(attn_weights)

        #step 9 - 11
        ctx_vec = (attn_weights @ values).transpose(1, 2)

        #step 12
        ctx_vec = ctx_vec.contiguous().view(b, num_tokens, self.d_out)
        ctx_vec = self.out_proj(ctx_vec)

        return ctx_vec

#==========================================================================


class LayerNorm(nn.Module):
    def __init__(self, emb_dim):
        super().__init__()
        self.eps = 1e-5
        self.scale = nn.Parameter(torch.ones(emb_dim))
        self.shift = nn.Parameter(torch.zeros(emb_dim))

    def forward(self, x):
        mean = x.mean(dim=-1, keepdim=True)
        var = x.var(dim=-1, keepdim=True, unbiased=False)
        norm_x = (x - mean) / torch.sqrt(var + self.eps)
        return self.scale * norm_x + self.shift

#==========================================================================


class GeLU(nn.Module):
    def __init__(self):
        super().__init__()

    def forward(self, x):
        return 0.5 * x * (1 + torch.tanh(torch.sqrt(torch.tensor(2.0/torch.pi)) * (x + 0.044715 * torch.pow(x,3))))

#==========================================================================


class FeedForward(nn.Module):
    def __init__(self, cfg):
        super().__init__()
        self.layers = nn.Sequential(
            nn.Linear(cfg.emb_dim, 4*cfg.emb_dim),
            GeLU(),
            nn.Linear(4*cfg.emb_dim, cfg.emb_dim)
        )
    def forward(self, x):
        return self.layers(x)

#==========================================================================

class TransformerBlock(nn.Module):
    def __init__(self, cfg):
        super().__init__()
        self.att = MultiheadAttention(
            d_in = cfg.emb_dim,
            d_out = cfg.emb_dim,
            context_length = cfg.context_length,
            dropout = cfg.drop_rate,
            num_heads = cfg.n_heads,
            qkv_bias = cfg.qkv_bias
        )
        self.ff = FeedForward(cfg)
        self.norm1 = LayerNorm(cfg.emb_dim)
        self.norm2 = LayerNorm(cfg.emb_dim)
        self.drop_shortcut = nn.Dropout(cfg.drop_rate)

    def forward(self, x):
        shortcut = x
        x = self.norm1(x)
        x = self.att(x)
        x = self.drop_shortcut(x)
        x = x + shortcut

        shortcut = x
        x = self.norm2(x)
        x = self.ff(x)
        x = self.drop_shortcut(x)
        x = x + shortcut

        return x

#==========================================================================

class CustomGPTConfig(PretrainedConfig):
    model_type = "custom_gpt"  # Unique identifier for the AutoClass
    def __init__(self, context_length=1024, drop_rate=0.0, emb_dim=1024, n_heads=16, n_layers=24, qkv_bias=True, vocab_size=50257, **kwargs):
        super().__init__(**kwargs)
        self.context_length = context_length
        self.drop_rate = drop_rate
        self.emb_dim = emb_dim
        self.n_heads = n_heads
        self.n_layers = n_layers
        self.qkv_bias = qkv_bias
        self.vocab_size = vocab_size

#==========================================================================

class CustomGPT(
    PreTrainedModel, 
):
    config_class = CustomGPTConfig
    def __init__(self, config):
        super().__init__(config)
        self.tok_emb = nn.Embedding(config.vocab_size, config.emb_dim)
        self.pos_emb = nn.Embedding(config.context_length, config.emb_dim)
        self.drop_emb = nn.Dropout(config.drop_rate)

        self.trf_blocks = nn.Sequential(
            *[TransformerBlock(config) for _ in range(config.n_layers)]
        )

        self.final_norm = LayerNorm(config.emb_dim)
        self.out_head = nn.Linear(config.emb_dim, config.vocab_size, bias=False)

    def forward(self, x):
        batch_size, seq_len = x.shape
        tok_embeddings = self.tok_emb(x) #[2,4,768]
        pos_embeddings = self.pos_emb(torch.arange(seq_len, device=x.device)) #[2,4,768]
        x = tok_embeddings + pos_embeddings #[2,4,768]
        x = self.drop_emb(x)
        x = self.trf_blocks(x)
        x = self.final_norm(x)
        logits = self.out_head(x) #[2,4,50257]
        return logits

    def format_input(self, entry):
        instruction_text = (
            f"Below is an instruction that describes a task. "
            f"Write a response that appropriately completes the request."
            f"\n\n### Instruction:\n{entry['instruction']}"
        )
        # input_text = f"\n\n### Input:\n{entry['input']}" if entry["input"] else ""
        input_text = f"\n\n### Input:\n{entry.get('input', '')}"
        return instruction_text + input_text

    def text_to_token_ids(self, text, tokenizer):
        encoded = tokenizer.encode(text, allowed_special={'<|endoftext|>'})
        encoded_tensor = torch.tensor(encoded).unsqueeze(0) # add batch dimension
        return encoded_tensor

    def token_ids_to_text(self, token_ids, tokenizer):
        flat = token_ids.squeeze(0) # remove batch dimension
        return tokenizer.decode(flat.tolist())

    def generate(self, idx, max_new_tokens, context_size, temperature=0.0, top_k=None, eos_id=None):
        for _ in range(max_new_tokens):
            idx_cond = idx[:, -context_size:]
            with torch.no_grad():
                logits = self(idx_cond)
            logits = logits[:, -1, :]
    
            if top_k is not None:
                # Keep only top_k values
                top_logits, _ = torch.topk(logits, top_k)
                min_val = top_logits[:, -1] # select the last element i.e., the smallest from each batch's output
                logits = torch.where(logits < min_val, torch.tensor(float("-inf")).to(logits.device), logits)
    
            # New: Apply temperature scaling
            if temperature > 0.0:
                logits = logits / temperature
    
                # Apply softmax to get probabilities
                probs = torch.softmax(logits, dim=-1)  # (batch_size, context_len)
    
                # Sample from the distribution
                idx_next = torch.multinomial(probs, num_samples=1)  # (batch_size, 1)
    
            # Otherwise same as before: get idx of the vocab entry with the highest logits value
            else:
                idx_next = torch.argmax(logits, dim=-1, keepdim=True)  # (batch_size, 1)
    
            if idx_next == eos_id:  # Stop generating early if end-of-sequence token is encountered and eos_id is specified
                break
    
            # Same as before: append sampled index to the running sequence
            idx = torch.cat((idx, idx_next), dim=1)  # (batch_size, num_tokens+1)
    
        return idx

    def generate_response(self, input_entry, tokenizer, temperature=0.0, topk=None):
        current_device = next(self.parameters()).device
        self.eval()
        input_text = self.format_input(input_entry)

        token_ids = self.generate(
            idx=self.text_to_token_ids(input_text, tokenizer).to(current_device),
            max_new_tokens=256,
            context_size=1024,
            temperature=temperature,
            top_k=topk,
            eos_id=50256
        )
        generated_text = self.token_ids_to_text(token_ids, tokenizer)
        response_text = (
            generated_text[len(input_text):]
            .replace("### Response:", "")
            .strip()
            )
        return response_text.strip()

	import torch
	import torch.nn as nn
	from transformers import PreTrainedModel, PretrainedConfig


	class MultiheadAttention(nn.Module):
	def __init__(self, d_in, d_out, context_length, dropout, num_heads, qkv_bias=False):
	super().__init__()

	self.d_out = d_out
	self.num_heads = num_heads
	self.head_dim = d_out // num_heads

	#step 3
	self.W_query = nn.Linear(d_in, d_out, bias=qkv_bias)
	self.W_key = nn.Linear(d_in, d_out, bias=qkv_bias)
	self.W_value = nn.Linear(d_in, d_out, bias=qkv_bias)

	self.out_proj = nn.Linear(d_out, d_out)
	self.dropout = nn.Dropout(dropout)
	self.register_buffer("mask",torch.triu(torch.ones(context_length, context_length), diagonal=1))


	def forward(self, x):
	b, num_tokens, d_in = x.shape

	#step 4
	keys = self.W_key(x)
	queries = self.W_query(x)
	values = self.W_value(x)

	#step 5
	keys = keys.view(b, num_tokens, self.num_heads, self.head_dim)
	queries = queries.view(b, num_tokens, self.num_heads, self.head_dim)
	values = values.view(b, num_tokens, self.num_heads, self.head_dim)

	#step 6
	keys = keys.transpose(1,2)
	queries = queries.transpose(1,2)
	values = values.transpose(1,2)

	#step 7
	attn_scores = queries @ keys.transpose(2,3)

	#step 8
	mask_bool = self.mask.bool()[:num_tokens, :num_tokens]
	attn_scores.masked_fill_(mask_bool, -torch.inf)

	attn_weights = torch.softmax(attn_scores / keys.shape[-1]**0.5, dim=-1)
	attn_weights = self.dropout(attn_weights)

	#step 9 - 11
	ctx_vec = (attn_weights @ values).transpose(1, 2)

	#step 12
	ctx_vec = ctx_vec.contiguous().view(b, num_tokens, self.d_out)
	ctx_vec = self.out_proj(ctx_vec)

	return ctx_vec

	#==========================================================================


	class LayerNorm(nn.Module):
	def __init__(self, emb_dim):
	super().__init__()
	self.eps = 1e-5
	self.scale = nn.Parameter(torch.ones(emb_dim))
	self.shift = nn.Parameter(torch.zeros(emb_dim))

	def forward(self, x):
	mean = x.mean(dim=-1, keepdim=True)
	var = x.var(dim=-1, keepdim=True, unbiased=False)
	norm_x = (x - mean) / torch.sqrt(var + self.eps)
	return self.scale * norm_x + self.shift

	#==========================================================================


	class GeLU(nn.Module):
	def __init__(self):
	super().__init__()

	def forward(self, x):
	return 0.5 * x * (1 + torch.tanh(torch.sqrt(torch.tensor(2.0/torch.pi)) * (x + 0.044715 * torch.pow(x,3))))

	#==========================================================================


	class FeedForward(nn.Module):
	def __init__(self, cfg):
	super().__init__()
	self.layers = nn.Sequential(
	nn.Linear(cfg.emb_dim, 4*cfg.emb_dim),
	GeLU(),
	nn.Linear(4*cfg.emb_dim, cfg.emb_dim)
	)
	def forward(self, x):
	return self.layers(x)

	#==========================================================================

	class TransformerBlock(nn.Module):
	def __init__(self, cfg):
	super().__init__()
	self.att = MultiheadAttention(
	d_in = cfg.emb_dim,
	d_out = cfg.emb_dim,
	context_length = cfg.context_length,
	dropout = cfg.drop_rate,
	num_heads = cfg.n_heads,
	qkv_bias = cfg.qkv_bias
	)
	self.ff = FeedForward(cfg)
	self.norm1 = LayerNorm(cfg.emb_dim)
	self.norm2 = LayerNorm(cfg.emb_dim)
	self.drop_shortcut = nn.Dropout(cfg.drop_rate)

	def forward(self, x):
	shortcut = x
	x = self.norm1(x)
	x = self.att(x)
	x = self.drop_shortcut(x)
	x = x + shortcut

	shortcut = x
	x = self.norm2(x)
	x = self.ff(x)
	x = self.drop_shortcut(x)
	x = x + shortcut

	return x

	#==========================================================================

	class CustomGPTConfig(PretrainedConfig):
	model_type = "custom_gpt" # Unique identifier for the AutoClass
	def __init__(self, context_length=1024, drop_rate=0.0, emb_dim=1024, n_heads=16, n_layers=24, qkv_bias=True, vocab_size=50257, **kwargs):
	super().__init__(**kwargs)
	self.context_length = context_length
	self.drop_rate = drop_rate
	self.emb_dim = emb_dim
	self.n_heads = n_heads
	self.n_layers = n_layers
	self.qkv_bias = qkv_bias
	self.vocab_size = vocab_size

	#==========================================================================

	class CustomGPT(
	PreTrainedModel,
	):
	config_class = CustomGPTConfig
	def __init__(self, config):
	super().__init__(config)
	self.tok_emb = nn.Embedding(config.vocab_size, config.emb_dim)
	self.pos_emb = nn.Embedding(config.context_length, config.emb_dim)
	self.drop_emb = nn.Dropout(config.drop_rate)

	self.trf_blocks = nn.Sequential(
	*[TransformerBlock(config) for _ in range(config.n_layers)]
	)

	self.final_norm = LayerNorm(config.emb_dim)
	self.out_head = nn.Linear(config.emb_dim, config.vocab_size, bias=False)

	def forward(self, x):
	batch_size, seq_len = x.shape
	tok_embeddings = self.tok_emb(x) #[2,4,768]
	pos_embeddings = self.pos_emb(torch.arange(seq_len, device=x.device)) #[2,4,768]
	x = tok_embeddings + pos_embeddings #[2,4,768]
	x = self.drop_emb(x)
	x = self.trf_blocks(x)
	x = self.final_norm(x)
	logits = self.out_head(x) #[2,4,50257]
	return logits

	def format_input(self, entry):
	instruction_text = (
	f"Below is an instruction that describes a task. "
	f"Write a response that appropriately completes the request."
	f"\n\n### Instruction:\n{entry['instruction']}"
	)
	# input_text = f"\n\n### Input:\n{entry['input']}" if entry["input"] else ""
	input_text = f"\n\n### Input:\n{entry.get('input', '')}"
	return instruction_text + input_text

	def text_to_token_ids(self, text, tokenizer):
	encoded = tokenizer.encode(text, allowed_special={'<\|endoftext\|>'})
	encoded_tensor = torch.tensor(encoded).unsqueeze(0) # add batch dimension
	return encoded_tensor

	def token_ids_to_text(self, token_ids, tokenizer):
	flat = token_ids.squeeze(0) # remove batch dimension
	return tokenizer.decode(flat.tolist())

	def generate(self, idx, max_new_tokens, context_size, temperature=0.0, top_k=None, eos_id=None):
	for _ in range(max_new_tokens):
	idx_cond = idx[:, -context_size:]
	with torch.no_grad():
	logits = self(idx_cond)
	logits = logits[:, -1, :]

	if top_k is not None:
	# Keep only top_k values
	top_logits, _ = torch.topk(logits, top_k)
	min_val = top_logits[:, -1] # select the last element i.e., the smallest from each batch's output
	logits = torch.where(logits < min_val, torch.tensor(float("-inf")).to(logits.device), logits)

	# New: Apply temperature scaling
	if temperature > 0.0:
	logits = logits / temperature

	# Apply softmax to get probabilities
	probs = torch.softmax(logits, dim=-1) # (batch_size, context_len)

	# Sample from the distribution
	idx_next = torch.multinomial(probs, num_samples=1) # (batch_size, 1)

	# Otherwise same as before: get idx of the vocab entry with the highest logits value
	else:
	idx_next = torch.argmax(logits, dim=-1, keepdim=True) # (batch_size, 1)

	if idx_next == eos_id: # Stop generating early if end-of-sequence token is encountered and eos_id is specified
	break

	# Same as before: append sampled index to the running sequence
	idx = torch.cat((idx, idx_next), dim=1) # (batch_size, num_tokens+1)

	return idx

	def generate_response(self, input_entry, tokenizer, temperature=0.0, topk=None):
	current_device = next(self.parameters()).device
	self.eval()
	input_text = self.format_input(input_entry)

	token_ids = self.generate(
	idx=self.text_to_token_ids(input_text, tokenizer).to(current_device),
	max_new_tokens=256,
	context_size=1024,
	temperature=temperature,
	top_k=topk,
	eos_id=50256
	)
	generated_text = self.token_ids_to_text(token_ids, tokenizer)
	response_text = (
	generated_text[len(input_text):]
	.replace("### Response:", "")
	.strip()
	)
	return response_text.strip()