RaveAI-v0.1 / model.py

Update model.py

8f1dadc verified 3 months ago

No virus

14.6 kB

	import math
	import inspect
	from dataclasses import dataclass

	import torch
	import torch.nn as nn
	from torch.nn import functional as F

	class LayerNorm(nn.Module):
	def __init__(self, ndim, bias):
	super().__init__()
	self.weight = nn.Parameter(torch.ones(ndim))
	self.bias = nn.Parameter(torch.zeros(ndim)) if bias else None

	def forward(self, input):
	return F.layer_norm(input, self.weight.shape, self.weight, self.bias, 1e-5)

	class CausalSelfAttention(nn.Module):

	def __init__(self, config):
	super().__init__()
	assert config.n_embd % config.n_head == 0

	self.c_attn = nn.Linear(config.n_embd, 3 * config.n_embd, bias=config.bias)

	self.c_proj = nn.Linear(config.n_embd, config.n_embd, bias=config.bias)

	self.attn_dropout = nn.Dropout(config.dropout)
	self.resid_dropout = nn.Dropout(config.dropout)
	self.n_head = config.n_head
	self.n_embd = config.n_embd
	self.dropout = config.dropout
	# flash attention make GPU go brrrrr but support is only in PyTorch >= 2.0
	self.flash = hasattr(torch.nn.functional, 'scaled_dot_product_attention')
	if not self.flash:
	print("WARNING: using slow attention. Flash Attention requires PyTorch >= 2.0")
	# causal mask to ensure that attention is only applied to the left in the input sequence
	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()

	q, k, v = self.c_attn(x).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)

	if self.flash:
	y = torch.nn.functional.scaled_dot_product_attention(q, k, v, attn_mask=None, dropout_p=self.dropout if self.training else 0, is_causal=True)
	else:
	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)
	att = self.attn_dropout(att)
	y = att @ v
	y = y.transpose(1, 2).contiguous().view(B, T, C)

	y = self.resid_dropout(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, bias=config.bias)
	self.gelu = nn.GELU()
	self.c_proj = nn.Linear(4 * config.n_embd, config.n_embd, bias=config.bias)
	self.dropout = nn.Dropout(config.dropout)

	def forward(self, x):
	x = self.c_fc(x)
	x = self.gelu(x)
	x = self.c_proj(x)
	x = self.dropout(x)
	return x

	class Block(nn.Module):

	def __init__(self, config):
	super().__init__()
	self.ln_1 = LayerNorm(config.n_embd, bias=config.bias)
	self.attn = CausalSelfAttention(config)
	self.ln_2 = LayerNorm(config.n_embd, bias=config.bias)
	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
	vocab_size: int = 50304 # GPT-2 vocab_size of 50257, padded up to nearest multiple of 64 for efficiency
	n_layer: int = 12
	n_head: int = 12
	n_embd: int = 768
	dropout: float = 0.0
	bias: bool = True # True: bias in Linears and LayerNorms, like GPT-2. False: a bit better and faster

	class GPT(nn.Module):

	def __init__(self, config):
	super().__init__()
	assert config.vocab_size is not None
	assert config.block_size is not None
	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),
	drop = nn.Dropout(config.dropout),
	h = nn.ModuleList([Block(config) for _ in range(config.n_layer)]),
	ln_f = LayerNorm(config.n_embd, bias=config.bias),
	))
	self.lm_head = nn.Linear(config.n_embd, config.vocab_size, bias=False)

	self.transformer.wte.weight = self.lm_head.weight # https://paperswithcode.com/method/weight-tying

	self.apply(self._init_weights)
	# apply special scaled init to the residual projections, per GPT-2 paper
	for pn, p in self.named_parameters():
	if pn.endswith('c_proj.weight'):
	torch.nn.init.normal_(p, mean=0.0, std=0.02/math.sqrt(2 * config.n_layer))

	# report number of parameters
	print("number of parameters: %.2fM" % (self.get_num_params()/1e6,))

	def get_num_params(self, non_embedding=True):
	n_params = sum(p.numel() for p in self.parameters())
	if non_embedding:
	n_params -= self.transformer.wpe.weight.numel()
	return n_params

	def _init_weights(self, module):
	if isinstance(module, nn.Linear):
	torch.nn.init.normal_(module.weight, mean=0.0, std=0.02)
	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):
	device = idx.device
	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}"
	pos = torch.arange(0, t, dtype=torch.long, device=device) # shape (t)

	# forward the GPT model itself
	tok_emb = self.transformer.wte(idx) # token embeddings of shape (b, t, n_embd)
	pos_emb = self.transformer.wpe(pos) # position embeddings of shape (t, n_embd)
	x = self.transformer.drop(tok_emb + pos_emb)
	for block in self.transformer.h:
	x = block(x)
	x = self.transformer.ln_f(x)

	if targets is not None:
	# if we are given some desired targets also calculate the loss
	logits = self.lm_head(x)
	loss = F.cross_entropy(logits.view(-1, logits.size(-1)), targets.view(-1), ignore_index=-1)
	else:
	# inference-time mini-optimization: only forward the lm_head on the very last position
	logits = self.lm_head(x[:, [-1], :]) # note: using list [-1] to preserve the time dim
	loss = None

	return logits, loss

	def crop_block_size(self, block_size):
	# model surgery to decrease the block size if necessary
	# e.g. we may load the GPT2 pretrained model checkpoint (block size 1024)
	# but want to use a smaller block size for some smaller, simpler model
	assert block_size <= self.config.block_size
	self.config.block_size = block_size
	self.transformer.wpe.weight = nn.Parameter(self.transformer.wpe.weight[:block_size])
	for block in self.transformer.h:
	if hasattr(block.attn, 'bias'):
	block.attn.bias = block.attn.bias[:,:,:block_size,:block_size]

	@classmethod
	def from_pretrained(cls, model_type, override_args=None):
	assert model_type in {'gpt2', 'gpt2-medium', 'gpt2-large', 'gpt2-xl'}
	override_args = override_args or {} # default to empty dict
	# only dropout can be overridden see more notes below
	assert all(k == 'dropout' for k in override_args)
	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]
	print("forcing vocab_size=50257, block_size=1024, bias=True")
	config_args['vocab_size'] = 50257 # always 50257 for GPT model checkpoints
	config_args['block_size'] = 1024 # always 1024 for GPT model checkpoints
	config_args['bias'] = True # always True for GPT model checkpoints
	# we can override the dropout rate, if desired
	if 'dropout' in override_args:
	print(f"overriding dropout rate to {override_args['dropout']}")
	config_args['dropout'] = override_args['dropout']
	# 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

	def configure_optimizers(self, weight_decay, learning_rate, betas, device_type, use_8bit, use_4bit):
	# start with all of the candidate parameters
	param_dict = {pn: p for pn, p in self.named_parameters()}
	# filter out those that do not require grad
	param_dict = {pn: p for pn, p in param_dict.items() if p.requires_grad}
	# create optim groups. Any parameters that is 2D will be weight decayed, otherwise no.
	# i.e. all weight tensors in matmuls + embeddings decay, all biases and layernorms don't.
	decay_params = [p for n, p in param_dict.items() if p.dim() >= 2]
	nodecay_params = [p for n, p in param_dict.items() if p.dim() < 2]
	optim_groups = [
	{'params': decay_params, 'weight_decay': weight_decay},
	{'params': nodecay_params, 'weight_decay': 0.0}
	]
	num_decay_params = sum(p.numel() for p in decay_params)
	num_nodecay_params = sum(p.numel() for p in nodecay_params)
	print(f"num decayed parameter tensors: {len(decay_params)}, with {num_decay_params:,} parameters")
	print(f"num non-decayed parameter tensors: {len(nodecay_params)}, with {num_nodecay_params:,} parameters")
	# Create AdamW optimizer and use the fused version if it is available
	fused_available = 'fused' in inspect.signature(torch.optim.AdamW).parameters
	use_fused = fused_available and device_type == 'cuda'
	extra_args = dict(fused=True) if use_fused else dict()
	optimizer = torch.optim.AdamW(optim_groups, lr=learning_rate, betas=betas, **extra_args)
	print(f"using fused AdamW: {use_fused}")

	return optimizer

	def estimate_mfu(self, fwdbwd_per_iter, dt):
	""" estimate model flops utilization (MFU) in units of A100 bfloat16 peak FLOPS """
	# first estimate the number of flops we do per iteration.
	# see PaLM paper Appendix B as ref: https://arxiv.org/abs/2204.02311
	N = self.get_num_params()
	cfg = self.config
	L, H, Q, T = cfg.n_layer, cfg.n_head, cfg.n_embd//cfg.n_head, cfg.block_size
	flops_per_token = 6N + 12LHQ*T
	flops_per_fwdbwd = flops_per_token * T
	flops_per_iter = flops_per_fwdbwd * fwdbwd_per_iter
	# express our flops throughput as ratio of A100 bfloat16 peak flops
	flops_achieved = flops_per_iter * (1.0/dt) # per second
	flops_promised = 312e12 # A100 GPU bfloat16 peak flops is 312 TFLOPS
	mfu = flops_achieved / flops_promised
	return mfu

	@torch.no_grad()
	def generate(self, idx, max_new_tokens, temperature=1.0, top_k=None):
	"""
	Take a conditioning sequence of indices idx (LongTensor of shape (b,t)) and complete
	the sequence max_new_tokens times, feeding the predictions back into the model each time.
	Most likely you'll want to make sure to be in model.eval() mode of operation for this.
	"""
	for _ in range(max_new_tokens):
	# if the sequence context is growing too long we must crop it at block_size
	idx_cond = idx if idx.size(1) <= self.config.block_size else idx[:, -self.config.block_size:]
	# forward the model to get the logits for the index in the sequence
	logits, _ = self(idx_cond)
	# pluck the logits at the final step and scale by desired temperature
	logits = logits[:, -1, :] / temperature
	# optionally crop the logits to only the top k options
	if top_k is not None:
	v, _ = torch.topk(logits, min(top_k, logits.size(-1)))
	logits[logits < v[:, [-1]]] = -float('Inf')
	# apply softmax to convert logits to (normalized) probabilities
	probs = F.softmax(logits, dim=-1)
	# sample from the distribution
	idx_next = torch.multinomial(probs, num_samples=1)
	# append sampled index to the running sequence and continue
	idx = torch.cat((idx, idx_next), dim=1)

	return idx