model.py

import math
import struct
import inspect
from .LMConfig import LMConfig
from typing import Any, Optional, Tuple
import numpy as np
import torch
import torch.nn.functional as F
from torch import nn
from transformers import PreTrainedModel
from transformers.modeling_outputs import CausalLMOutputWithPast


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_pos_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
    pos_cis = torch.polar(torch.ones_like(freqs), freqs)  # complex64
    return pos_cis


def apply_rotary_emb(xq, xk, pos_cis):
    def unite_shape(pos_cis, x):
        ndim = x.ndim
        assert 0 <= 1 < ndim
        assert pos_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 pos_cis.view(*shape)

    xq_ = torch.view_as_complex(xq.float().reshape(*xq.shape[:-1], -1, 2))
    xk_ = torch.view_as_complex(xk.float().reshape(*xk.shape[:-1], -1, 2))
    pos_cis = unite_shape(pos_cis, xq_)
    xq_out = torch.view_as_real(xq_ * pos_cis).flatten(3)
    xk_out = torch.view_as_real(xk_ * pos_cis).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: LMConfig):
        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') and args.flash_attn

        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)
            self.register_buffer("mask", mask)

    def forward(
            self,
            x: torch.Tensor,
            pos_cis: torch.Tensor,
            use_kv_cache: bool = False,
            past_kv: Tuple[torch.Tensor] = None
    ):
        bsz, seqlen, _ = x.shape
        # QKV
        # inference
        if use_kv_cache:
            # 只计算最后一个token的Q
            current_token = x[:, -1:, :]

            if not past_kv:
                xq = self.wq(x)
                xk, xv = self.wk(x), self.wv(x)
            else:
                past_key, past_value = past_kv
                xq = torch.cat((torch.zeros_like(x[:, :-1, :]), self.wq(current_token)), dim=1)
                xk = torch.cat((past_key, self.wk(current_token)), dim=1)
                xv = torch.cat((past_value, self.wv(current_token)), dim=1)

            past_kv = (xk, xv)
        else:
            xq = self.wq(x)
            xk, xv = 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, pos_cis)

        # 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:
            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:
            # manual implementation
            scores = torch.matmul(xq, xk.transpose(2, 3)) / math.sqrt(self.head_dim)
            assert hasattr(self, 'mask')
            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, past_kv


class FeedForward(nn.Module):
    def __init__(self, dim: int, hidden_dim: int, multiple_of: int, dropout: float):
        super().__init__()
        if hidden_dim is None:
            hidden_dim = 4 * dim
            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):
        return self.dropout(self.w2(F.silu(self.w1(x)) * self.w3(x)))


class MoEGate(nn.Module):
    def __init__(self, config: LMConfig):
        super().__init__()
        self.config = config
        self.top_k = config.num_experts_per_tok
        self.n_routed_experts = config.n_routed_experts

        self.scoring_func = config.scoring_func
        self.alpha = config.aux_loss_alpha
        self.seq_aux = config.seq_aux

        # topk selection algorithm
        self.norm_topk_prob = config.norm_topk_prob
        self.gating_dim = config.dim
        self.weight = nn.Parameter(torch.empty((self.n_routed_experts, self.gating_dim)))
        self.reset_parameters()

    def reset_parameters(self) -> None:
        import torch.nn.init as init
        init.kaiming_uniform_(self.weight, a=math.sqrt(5))

    def forward(self, hidden_states):
        bsz, seq_len, h = hidden_states.shape
        ### compute gating score
        hidden_states = hidden_states.view(-1, h)
        logits = F.linear(hidden_states, self.weight, None)
        if self.scoring_func == 'softmax':
            scores = logits.softmax(dim=-1)
        else:
            raise NotImplementedError(f'insupportable scoring function for MoE gating: {self.scoring_func}')

        ### select top-k experts
        topk_weight, topk_idx = torch.topk(scores, k=self.top_k, dim=-1, sorted=False)

        ### norm gate to sum 1
        if self.top_k > 1 and self.norm_topk_prob:
            denominator = topk_weight.sum(dim=-1, keepdim=True) + 1e-20
            topk_weight = topk_weight / denominator

        ### expert-level computation auxiliary loss
        if self.training and self.alpha > 0.0:
            scores_for_aux = scores
            aux_topk = self.top_k
            # always compute aux loss based on the naive greedy topk method
            topk_idx_for_aux_loss = topk_idx.view(bsz, -1)
            if self.seq_aux:
                scores_for_seq_aux = scores_for_aux.view(bsz, seq_len, -1)
                ce = torch.zeros(bsz, self.n_routed_experts, device=hidden_states.device)
                ce.scatter_add_(1, topk_idx_for_aux_loss,
                                torch.ones(bsz, seq_len * aux_topk, device=hidden_states.device)).div_(
                    seq_len * aux_topk / self.n_routed_experts)
                aux_loss = (ce * scores_for_seq_aux.mean(dim=1)).sum(dim=1).mean() * self.alpha
            else:
                mask_ce = F.one_hot(topk_idx_for_aux_loss.view(-1), num_classes=self.n_routed_experts)
                ce = mask_ce.float().mean(0)
                Pi = scores_for_aux.mean(0)
                fi = ce * self.n_routed_experts
                aux_loss = (Pi * fi).sum() * self.alpha
        else:
            aux_loss = None
        return topk_idx, topk_weight, aux_loss


class MOEFeedForward(nn.Module):
    def __init__(self, config: LMConfig):
        super().__init__()
        self.config = config
        self.experts = nn.ModuleList([
            FeedForward(
                dim=config.dim,
                hidden_dim=config.hidden_dim,
                multiple_of=config.multiple_of,
                dropout=config.dropout,
            )
            for _ in range(config.n_routed_experts)
        ])

        self.gate = MoEGate(config)
        if config.n_shared_experts is not None:
            self.shared_experts = FeedForward(
                dim=config.dim,
                hidden_dim=config.hidden_dim,
                multiple_of=config.multiple_of,
                dropout=config.dropout,
            )

    def forward(self, x):
        identity = x
        orig_shape = x.shape
        bsz, seq_len, _ = x.shape

        # 使用门控机制选择专家
        topk_idx, topk_weight, aux_loss = self.gate(x)

        x = x.view(-1, x.shape[-1])
        flat_topk_idx = topk_idx.view(-1)

        if self.training:
            # 训练模式下，重复输入数据
            x = x.repeat_interleave(self.config.num_experts_per_tok, dim=0)
            y = torch.empty_like(x, dtype=torch.float16)
            for i, expert in enumerate(self.experts):
                y[flat_topk_idx == i] = expert(x[flat_topk_idx == i])
            y = (y.view(*topk_weight.shape, -1) * topk_weight.unsqueeze(-1)).sum(dim=1)
            y = y.view(*orig_shape)
        else:
            # 推理模式下，只选择最优专家
            y = self.moe_infer(x, flat_topk_idx, topk_weight.view(-1, 1)).view(*orig_shape)

        if self.config.n_shared_experts is not None:
            y = y + self.shared_experts(identity)

        return y

    @torch.no_grad()
    def moe_infer(self, x, flat_expert_indices, flat_expert_weights):
        expert_cache = torch.zeros_like(x)
        idxs = flat_expert_indices.argsort()
        tokens_per_expert = flat_expert_indices.bincount().cpu().numpy().cumsum(0)
        token_idxs = idxs // self.config.num_experts_per_tok
        # 例如当tokens_per_expert=[6, 15, 20, 26, 33, 38, 46, 52]
        # 当token_idxs=[3, 7, 19, 21, 24, 25,  4,  5,  6, 10, 11, 12...]
        # 意味着当token_idxs[:6] -> [3,  7, 19, 21, 24, 25,  4]位置的token都由专家0处理，token_idxs[6:15]位置的token都由专家1处理......
        for i, end_idx in enumerate(tokens_per_expert):
            start_idx = 0 if i == 0 else tokens_per_expert[i - 1]
            if start_idx == end_idx:
                continue
            expert = self.experts[i]
            exp_token_idx = token_idxs[start_idx:end_idx]
            expert_tokens = x[exp_token_idx]
            expert_out = expert(expert_tokens)
            expert_out.mul_(flat_expert_weights[idxs[start_idx:end_idx]])
            # 使用 scatter_add_ 进行 sum 操作
            expert_cache.scatter_add_(0, exp_token_idx.view(-1, 1).repeat(1, x.shape[-1]), expert_out)

        return expert_cache


class TransformerBlock(nn.Module):
    def __init__(self, layer_id: int, args: LMConfig):
        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.layer_id = layer_id
        self.attention_norm = RMSNorm(args.dim, eps=args.norm_eps)
        self.ffn_norm = RMSNorm(args.dim, eps=args.norm_eps)

        if args.use_moe:
            self.feed_forward = MOEFeedForward(args)
        else:
            self.feed_forward = FeedForward(
                dim=args.dim,
                hidden_dim=args.hidden_dim,
                multiple_of=args.multiple_of,
                dropout=args.dropout,
            )

    def forward(self, x, pos_cis, use_kv_cache=False, past_kv: Tuple[torch.Tensor] = None):
        attn_res, past_kv = self.attention(self.attention_norm(x), pos_cis, use_kv_cache, past_kv)
        h = x + attn_res
        out = h + self.feed_forward(self.ffn_norm(h))
        return out, past_kv


class Transformer(PreTrainedModel):
    config_class = LMConfig
    last_loss: Optional[torch.Tensor]

    def __init__(self, params: LMConfig = None):
        super().__init__(params)
        if not params:
            params = LMConfig()
        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(self.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
        pos_cis = precompute_pos_cis(self.params.dim // self.params.n_heads, self.params.max_seq_len)
        self.register_buffer("pos_cis", pos_cis, 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
        self.OUT = CausalLMOutputWithPast()

    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: Optional[torch.Tensor] = None,
                targets: Optional[torch.Tensor] = None,
                use_kv_cache=False, past_kvs=None, **keyargs):
        if past_kvs is None:
            past_kvs = [None for _ in range(self.n_layers)]
        if 'input_ids' in keyargs:
            tokens = keyargs['input_ids']
        if 'attention_mask' in keyargs:
            targets = keyargs['attention_mask']

        _bsz, seqlen = tokens.shape
        h = self.tok_embeddings(tokens)
        h = self.dropout(h)
        pos_cis = self.pos_cis[:seqlen]
        for idx, layer in enumerate(self.layers):
            h, past_kvs[idx] = layer(h, pos_cis, use_kv_cache, past_kvs[idx])

        h = self.norm(h)

        if targets is not None:
            # if we are given some desired targets also calculate the loss
            logits = self.output(h)
            self.last_loss = F.cross_entropy(logits.view(-1, logits.size(-1)), targets.view(-1), ignore_index=-1)
        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

        self.OUT.__setitem__('logits', logits)
        self.OUT.__setitem__('last_loss', self.last_loss)

        if use_kv_cache:
            return self.OUT, past_kvs
        return self.OUT


    @torch.inference_mode()
    def generate(self, idx, eos, max_new_tokens, temperature=0.7, top_k=None, stream=True, repetition_penalty=1.):
        index = idx.shape[1]
        use_kv_cache = True
        past_kvs = [None for _ in range(self.n_layers)]
        while idx.shape[1] < max_new_tokens - 1:
            # 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
            inference_res = self(idx_cond, use_kv_cache=use_kv_cache, past_kvs=past_kvs)
            if use_kv_cache:
                logits, past_kvs = inference_res[0].logits, inference_res[1]
            else:
                logits = inference_res.logits

            logits = logits[:, -1, :]  # crop to just the final time step

            # Apply repetition penalty
            for token in set(idx.tolist()[0]):
                logits[:, token] /= repetition_penalty

            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, generator=None)
            # append sampled index to the running sequence and continue
            if idx_next == eos:
                break

            idx = torch.cat((idx, idx_next), dim=1)
            if stream:
                yield idx[:, index:]

        if not stream:
            yield idx[:, index:]

    @torch.inference_mode()
    def eval_answer(self, idx):
        # 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
        past_kvs = [None for _ in range(self.n_layers)]
        inference_res = self(idx_cond, use_kv_cache=False, past_kvs=past_kvs)
        logits = inference_res.logits
        logits = logits[:, -1, :]
        return logits

    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
        # pos_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}")