model.py

import math
import struct
import inspect
from dataclasses import dataclass
from typing import Any, Optional, Tuple
from typing import List, Literal, Optional, Tuple, TypedDict

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

import time 


@dataclass
class ModelArgs:
    # default hyperparameters for the Llama 7B model
    dim: int = 4096
    n_layers: int = 32
    n_heads: int = 32
    n_kv_heads: Optional[int] = None
    vocab_size: int = 32000
    multiple_of: int = 256  # MLP hidden layer size will be multiple of
    norm_eps: float = 1e-5
    max_seq_len: int = 2048
    dropout: float = 0.0


class RMSNorm(torch.nn.Module):
    def __init__(self, dim: int, eps: float):
        super().__init__()
        self.eps = eps
        self.weight = nn.Parameter(torch.ones(dim))

    def _norm(self, x):
        return x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps)

    def forward(self, x):
        output = self._norm(x.float()).type_as(x)
        return output * self.weight


def precompute_freqs_cis(dim: int, end: int, theta: float = 10000.0):
    freqs = 1.0 / (theta ** (torch.arange(0, dim, 2)[: (dim // 2)].float() / dim))
    t = torch.arange(end, device=freqs.device)  # type: ignore
    freqs = torch.outer(t, freqs).float()  # type: ignore
    freqs_cos = torch.cos(freqs)  # real part
    freqs_sin = torch.sin(freqs)  # imaginary part
    return freqs_cos, freqs_sin

def reshape_for_broadcast(freqs_cis: torch.Tensor, x: torch.Tensor):
    ndim = x.ndim
    assert 0 <= 1 < ndim
    assert freqs_cis.shape == (x.shape[1], x.shape[-1])
    shape = [d if i == 1 or i == ndim - 1 else 1 for i, d in enumerate(x.shape)]
    return freqs_cis.view(shape)

def apply_rotary_emb(
    xq: torch.Tensor,
    xk: torch.Tensor,
    freqs_cos: torch.Tensor,
    freqs_sin: torch.Tensor
) -> Tuple[torch.Tensor, torch.Tensor]:

    # reshape xq and xk to match the complex representation
    xq_r, xq_i = xq.float().reshape(xq.shape[:-1] + (-1, 2)).unbind(-1)
    xk_r, xk_i = xk.float().reshape(xk.shape[:-1] + (-1, 2)).unbind(-1)

    # reshape freqs_cos and freqs_sin for broadcasting
    freqs_cos = reshape_for_broadcast(freqs_cos, xq_r)
    freqs_sin = reshape_for_broadcast(freqs_sin, xq_r)

    # apply rotation using real numbers
    xq_out_r = xq_r * freqs_cos - xq_i * freqs_sin
    xq_out_i = xq_r * freqs_sin + xq_i * freqs_cos
    xk_out_r = xk_r * freqs_cos - xk_i * freqs_sin
    xk_out_i = xk_r * freqs_sin + xk_i * freqs_cos

    # flatten last two dimensions
    xq_out = torch.stack([xq_out_r, xq_out_i], dim=-1).flatten(3)
    xk_out = torch.stack([xk_out_r, xk_out_i], dim=-1).flatten(3)

    return xq_out.type_as(xq), xk_out.type_as(xk)

def repeat_kv(x: torch.Tensor, n_rep: int) -> torch.Tensor:
    """torch.repeat_interleave(x, dim=2, repeats=n_rep)"""
    bs, slen, n_kv_heads, head_dim = x.shape
    if n_rep == 1:
        return x
    return (
        x[:, :, :, None, :]
        .expand(bs, slen, n_kv_heads, n_rep, head_dim)
        .reshape(bs, slen, n_kv_heads * n_rep, head_dim)
    )

class Attention(nn.Module):
    def __init__(self, args: ModelArgs):
        super().__init__()
        self.n_kv_heads = args.n_heads if args.n_kv_heads is None else args.n_kv_heads
        assert args.n_heads % self.n_kv_heads == 0
        model_parallel_size = 1
        self.n_local_heads = args.n_heads // model_parallel_size
        self.n_local_kv_heads = self.n_kv_heads // model_parallel_size
        self.n_rep = self.n_local_heads // self.n_local_kv_heads
        self.head_dim = args.dim // args.n_heads
        self.wq = nn.Linear(args.dim, args.n_heads * self.head_dim, bias=False)
        self.wk = nn.Linear(args.dim, self.n_kv_heads * self.head_dim, bias=False)
        self.wv = nn.Linear(args.dim, self.n_kv_heads * self.head_dim, bias=False)
        self.wo = nn.Linear(args.n_heads * self.head_dim, args.dim, bias=False)
        self.attn_dropout = nn.Dropout(args.dropout)
        self.resid_dropout = nn.Dropout(args.dropout)
        self.dropout = args.dropout

        # use flash attention or a manual implementation?
        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")
            mask = torch.full((1, 1, args.max_seq_len, args.max_seq_len), float("-inf"))
            mask = torch.triu(mask, diagonal=1)    # 下三角注意力
            self.register_buffer("mask", mask)
        
        # add
        mask = torch.full((1, 1, args.max_seq_len-1, args.max_seq_len-1), float("-inf"))
        self.mask = torch.triu(mask, diagonal=1)

    def forward(
        self,
        x: torch.Tensor,
        freqs_cos: torch.Tensor,
        freqs_sin: torch.Tensor,
        attention_mask:  Optional[torch.Tensor],   #add
    ):
        bsz, seqlen, _ = x.shape

        # QKV
        xq, xk, xv = self.wq(x), self.wk(x), self.wv(x)
        xq = xq.view(bsz, seqlen, self.n_local_heads, self.head_dim)
        xk = xk.view(bsz, seqlen, self.n_local_kv_heads, self.head_dim)
        xv = xv.view(bsz, seqlen, self.n_local_kv_heads, self.head_dim)

        # RoPE relative positional embeddings
        xq, xk = apply_rotary_emb(xq, xk, freqs_cos, freqs_sin)

        # grouped multiquery attention: expand out keys and values
        xk = repeat_kv(xk, self.n_rep)  # (bs, seqlen, n_local_heads, head_dim)
        xv = repeat_kv(xv, self.n_rep)  # (bs, seqlen, n_local_heads, head_dim)

        # make heads into a batch dimension
        xq = xq.transpose(1, 2)  # (bs, n_local_heads, seqlen, head_dim)
        xk = xk.transpose(1, 2)
        xv = xv.transpose(1, 2)
        
        # flash implementation
        if self.flash:
            if attention_mask == None:
                #原代码，默认不能输入attention mask
                output = torch.nn.functional.scaled_dot_product_attention(xq, xk, xv, attn_mask=None, dropout_p=self.dropout if self.training else 0.0, is_causal=True)
            else: 
                 # 修改,在下三角注意力基础上增加 attention mask
#                 mask = torch.full((1, 1, attention_mask.shape[1], attention_mask.shape[1]), float("-inf"))
#                 mask = torch.triu(mask, diagonal=1).to(attention_mask.device)  
                #增加 attention mask
                attmask = attention_mask.unsqueeze(1).unsqueeze(2) + self.mask.to(attention_mask.device)  
                output = torch.nn.functional.scaled_dot_product_attention(xq, xk, xv, attn_mask=attmask, dropout_p=self.dropout if self.training else 0.0, is_causal=False)
            
        else:
            # manual implementation
            scores = torch.matmul(xq, xk.transpose(2, 3)) / math.sqrt(self.head_dim)
            assert hasattr(self, 'mask')
            # mask下三角注意力
            scores = scores + self.mask[:, :, :seqlen, :seqlen]   # (bs, n_local_heads, seqlen, cache_len + seqlen)
            scores = F.softmax(scores.float(), dim=-1).type_as(xq)
            scores = self.attn_dropout(scores)
            output = torch.matmul(scores, xv)  # (bs, n_local_heads, seqlen, head_dim)

        # restore time as batch dimension and concat heads
        output = output.transpose(1, 2).contiguous().view(bsz, seqlen, -1)

        # final projection into the residual stream
        output = self.wo(output)
        output = self.resid_dropout(output)
        return output


class FeedForward(nn.Module):
    def __init__(self, dim: int, hidden_dim: int, multiple_of: int, dropout: float):
        super().__init__()
        hidden_dim = int(2 * hidden_dim / 3)
        hidden_dim = multiple_of * ((hidden_dim + multiple_of - 1) // multiple_of)
        self.w1 = nn.Linear(dim, hidden_dim, bias=False)
        self.w2 = nn.Linear(hidden_dim, dim, bias=False)
        self.w3 = nn.Linear(dim, hidden_dim, bias=False)
        self.dropout = nn.Dropout(dropout)

    def forward(self, x):
#         import pdb
#         pdb.set_trace()
        return self.dropout(self.w2(F.silu(self.w1(x)) * self.w3(x)))


class TransformerBlock(nn.Module):
    def __init__(self, layer_id: int, args: ModelArgs):
        super().__init__()
        self.n_heads = args.n_heads
        self.dim = args.dim
        self.head_dim = args.dim // args.n_heads
        self.attention = Attention(args)
        self.feed_forward = FeedForward(
            dim=args.dim,
            hidden_dim=4 * args.dim,
            multiple_of=args.multiple_of,
            dropout=args.dropout,
        )
        self.layer_id = layer_id
        self.attention_norm = RMSNorm(args.dim, eps=args.norm_eps)
        self.ffn_norm = RMSNorm(args.dim, eps=args.norm_eps)

    def forward(self, x, freqs_cos, freqs_sin, attention_mask):
#         import pdb
#         pdb.set_trace()
        h = x + self.attention.forward(self.attention_norm(x), freqs_cos, freqs_sin, attention_mask)
#         import pdb
#         pdb.set_trace()
        out = h + self.feed_forward.forward(self.ffn_norm(h))
        return out


# +
class Transformer(nn.Module):
    last_loss: Optional[torch.Tensor]

    def __init__(self, params: ModelArgs):
        super().__init__()
        self.params = params
        self.vocab_size = params.vocab_size
        self.n_layers = params.n_layers
        
        self.tok_embeddings = nn.Embedding(params.vocab_size, params.dim)
        self.dropout = nn.Dropout(params.dropout)
        self.layers = torch.nn.ModuleList()
        for layer_id in range(params.n_layers):
            self.layers.append(TransformerBlock(layer_id, params))
        self.norm = RMSNorm(params.dim, eps=params.norm_eps)
        self.output = nn.Linear(params.dim, params.vocab_size, bias=False)

        # share the unembedding parameters with the embedding parameters
        self.tok_embeddings.weight = self.output.weight # https://paperswithcode.com/method/weight-tying

        # some useful precompute for the RoPE relative positional embeddings
        freqs_cos, freqs_sin = precompute_freqs_cis(self.params.dim // self.params.n_heads, self.params.max_seq_len)
        self.register_buffer("freqs_cos", freqs_cos, persistent=False)
        self.register_buffer("freqs_sin", freqs_sin, persistent=False)

        # init all weights
        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('w3.weight') or pn.endswith('wo.weight'):
                torch.nn.init.normal_(p, mean=0.0, std=0.02/math.sqrt(2 * params.n_layers))

        # Initialize attribute for the loss of the last forward call. This will be set if the forward is called with a targets tensor.
        self.last_loss = None

    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, tokens: torch.Tensor, targets: Optional[torch.Tensor] = None, loss_mask=None, attention_mask=None) -> torch.Tensor:
        _bsz, seqlen = tokens.shape
        h = self.tok_embeddings(tokens)
        h = self.dropout(h)
        freqs_cos = self.freqs_cos[:seqlen]
        freqs_sin = self.freqs_sin[:seqlen]
        
#         import pdb
#         pdb.set_trace()
        for layer in self.layers:
            h = layer(h, freqs_cos, freqs_sin, attention_mask)
        h = self.norm(h)

        if targets is not None:
            # if we are given some desired targets also calculate the loss
            logits = self.output(h)
            if loss_mask is None:
                # 根据原版代码，此处是next token prediction损失函数。
                self.last_loss = F.cross_entropy(logits.view(-1, logits.size(-1)), targets.view(-1), ignore_index=-1)
                loss = F.cross_entropy(logits.view(-1, logits.size(-1)), targets.view(-1), ignore_index=-1)
            else:
                # 根据loss mask分段求loss均值
                loss = F.cross_entropy(logits.view(-1, logits.size(-1)), targets.view(-1), ignore_index=-1, reduction='none')
#                 self.last_loss = 0.2*(loss*(1-loss_mask.view(-1))).sum()/(1-loss_mask).sum() + (loss*loss_mask.view(-1)).sum()/loss_mask.sum() 
                loss = 0.2*(loss*(1-loss_mask.view(-1))).sum()/(1-loss_mask).sum() + (loss*loss_mask.view(-1)).sum()/loss_mask.sum() 
    
        else:
            # inference-time mini-optimization: only forward the output on the very last position
            logits = self.output(h[:, [-1], :]) # note: using list [-1] to preserve the time dim
            self.last_loss = None
            loss = None

        return logits, loss

    def configure_optimizers(self, weight_decay, learning_rate, betas, device_type):
        # 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 = sum(p.numel() for p in self.parameters())
        cfg = self.params
        L, H, Q, T = cfg.n_layers, cfg.n_heads, cfg.dim//cfg.n_heads, cfg.max_seq_len
        flops_per_token = 6*N + 12*L*H*Q*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.inference_mode()
#     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.
#         Also note this is a super inefficient version of sampling with no key/value cache.
#         """
#         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.params.max_seq_len else idx[:, -self.params.max_seq_len:]
#             # forward the model to get the logits for the index in the sequence
#             logits = self(idx_cond)
#             logits = logits[:, -1, :] # crop to just the final time step
#             if temperature == 0.0:
#                 # "sample" the single most likely index
#                 _, idx_next = torch.topk(logits, k=1, dim=-1)
#             else:
#                 # pluck the logits at the final step and scale by desired temperature
#                 logits = logits / 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)
#                 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

    @torch.inference_mode()
    def generate(
        self,
        prompt_tokens: List[List[int]],# 可以不等长
        max_gen_len: int, # 生成的句子的最大长度，不包含提示部分。
        temperature: float = 0.6,
#         top_p: float = 0.9,# 表示概率从大到小累积到0.9的那些token作为随机采样的候选着。
        logprobs: bool = False,
        echo: bool = False,# False表示函数返回值不包含prompt部分
        top_k: int = None,
        tokenizer = None,
        device = "cuda",
    ) -> Tuple[List[List[int]], Optional[List[List[float]]]]:
        bsz = len(prompt_tokens)
        min_prompt_len = min(len(t) for t in prompt_tokens)
        max_prompt_len = max(len(t) for t in prompt_tokens)        
        
        total_len = min(self.params.max_seq_len, max_gen_len + max_prompt_len)
#         total_len = min(self.config.block_size, max_gen_len + max_prompt_len)
    
        pad_id = tokenizer.pad_id()
        tokens = torch.full((bsz, total_len), pad_id, dtype=torch.long, device=device)
        for k, t in enumerate(prompt_tokens):
            tokens[k, : len(t)] = torch.tensor(t, dtype=torch.long, device=device)
        if logprobs:
            token_logprobs = torch.zeros_like(tokens, dtype=torch.float)
         
        
        prev_pos = 0
        eos_reached = torch.tensor([False] * bsz, device=device)
        input_text_mask = tokens != pad_id
        for cur_pos in range(min_prompt_len, total_len):
            logits, loss = self(tokens[:, prev_pos:cur_pos] )
            if logprobs:
                token_logprobs[:, prev_pos + 1 : cur_pos + 1] = -F.cross_entropy(
                    input=logits.transpose(1, 2),
                    target=tokens[:, prev_pos + 1 : cur_pos + 1],
                    reduction="none",
                    ignore_index=pad_id,
                )
        
            if temperature == 0.0:
                # "sample" the single most likely index
                _, next_token = torch.topk(logits, k=1, dim=-1)
            else:
#                 probs = torch.softmax(logits[:, -1] / temperature, dim=-1)
#                 next_token = sample_top_p(probs, top_p)

                # 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
                next_token = torch.multinomial(probs, num_samples=1)
        
            next_token = next_token.reshape(-1)
            # only replace token if prompt has already been generated
            next_token = torch.where(input_text_mask[:, cur_pos], tokens[:, cur_pos], next_token)
            tokens[:, cur_pos] = next_token
            eos_reached |= (~input_text_mask[:, cur_pos]) & (next_token == tokenizer.eos_id())
#             prev_pos = cur_pos
            if all(eos_reached):
                break
              
        if logprobs:
            token_logprobs = token_logprobs.tolist()
        out_tokens, out_logprobs = [], []
        for i, toks in enumerate(tokens.tolist()):
            # cut to max gen len
            start = 0 if echo else len(prompt_tokens[i])
            toks = toks[start : len(prompt_tokens[i]) + max_gen_len]
            probs = None
            if logprobs:
                probs = token_logprobs[i][start : len(prompt_tokens[i]) + max_gen_len]
            # cut to eos tok if any
            if tokenizer.eos_id() in toks:
                eos_idx = toks.index(tokenizer.eos_id())
                toks = toks[:eos_idx]
                probs = probs[:eos_idx] if logprobs else None
            out_tokens.append(toks)
            out_logprobs.append(probs)
        
        return (out_tokens, out_logprobs if logprobs else None)

    def export(self, filepath='model.bin'):
        """export the model weights in fp32 into .bin file to be read from C"""
        f = open(filepath, 'wb')

        def serialize(t):
            d = t.detach().cpu().view(-1).numpy().astype(np.float32)
            b = struct.pack(f'{len(d)}f', *d)
            f.write(b)

        # first write out the header
        hidden_dim = self.layers[0].feed_forward.w1.weight.shape[0]
        p = self.params
        n_kv_heads = p.n_heads if p.n_kv_heads is None else p.n_kv_heads
        header = struct.pack('iiiiiii', p.dim, hidden_dim, p.n_layers, p.n_heads,
                                       n_kv_heads, p.vocab_size, p.max_seq_len)
        f.write(header)

        # next write out the embedding weights
        serialize(self.tok_embeddings.weight)

        # now all the layers
        # attention weights
        for layer in self.layers:
            serialize(layer.attention_norm.weight)
        for layer in self.layers:
            serialize(layer.attention.wq.weight)
        for layer in self.layers:
            serialize(layer.attention.wk.weight)
        for layer in self.layers:
            serialize(layer.attention.wv.weight)
        for layer in self.layers:
            serialize(layer.attention.wo.weight)
        # ffn weights
        for layer in self.layers:
            serialize(layer.ffn_norm.weight)
        for layer in self.layers:
            serialize(layer.feed_forward.w1.weight)
        for layer in self.layers:
            serialize(layer.feed_forward.w2.weight)
        for layer in self.layers:
            serialize(layer.feed_forward.w3.weight)
        # final rmsnorm
        serialize(self.norm.weight)
        # note: no need to write final classifier weights due to weight sharing
        # freqs_cis
        serialize(self.freqs_cos[:p.max_seq_len])
        serialize(self.freqs_sin[:p.max_seq_len])

        # write to binary file
        f.close()
        print(f"wrote {filepath}")