Upload model.py

model.py

@@ -0,0 +1,483 @@
+import math
+import struct
+import inspect
+import time
+
+from .config import ModelConfig
+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: ModelConfig):
+        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
+        self.n_local_heads = args.n_heads
+        self.n_local_kv_heads: int = self.n_kv_heads
+        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.k_cache, self.v_cache = None, None
+        self.attn_dropout = nn.Dropout(args.dropout)
+        self.resid_dropout = nn.Dropout(args.dropout)
+        self.dropout = args.dropout
+        self.flash = (
+            hasattr(torch.nn.functional, "scaled_dot_product_attention")
+            and args.flash_attn
+        )
+
+        # 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, persistent=False)
+
+    def forward(self, x: torch.Tensor, pos_cis: torch.Tensor, kv_cache=False):
+        bsz, seqlen, _ = x.shape
+
+        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)
+
+        xq, xk = apply_rotary_emb(xq, xk, pos_cis)
+
+        # 更高效的kv_cache实现
+        if kv_cache and self.eval():
+            if seqlen == 1 and all(
+                cache is not None for cache in (self.k_cache, self.v_cache)
+            ):
+                xk = torch.cat((self.k_cache, xk), dim=1)
+                xv = torch.cat((self.v_cache, xv), dim=1)
+            self.k_cache, self.v_cache = xk, xv
+
+        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)
+
+        xq = xq.transpose(1, 2)
+        xk = xk.transpose(1, 2)
+        xv = xv.transpose(1, 2)
+
+        if self.flash and seqlen != 1:
+            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:
+            scores = torch.matmul(xq, xk.transpose(2, 3)) / math.sqrt(self.head_dim)
+            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)
+
+        output = output.transpose(1, 2).contiguous().view(bsz, seqlen, -1)
+
+        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__()
+        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: ModelConfig):
+        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
+
+        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
+
+        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}"
+            )
+
+        topk_weight, topk_idx = torch.topk(scores, k=self.top_k, dim=-1, sorted=False)
+
+        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
+
+        if self.training and self.alpha > 0.0:
+            scores_for_aux = scores
+            aux_topk = self.top_k
+            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: ModelConfig):
+        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: ModelConfig):
+        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)
+
+        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, kv_cache=False):
+        h = x + self.attention(self.attention_norm(x), pos_cis, kv_cache)
+        out = h + self.feed_forward(self.ffn_norm(h))
+        return out
+
+
+class Transformer(PreTrainedModel):
+    config_class = ModelConfig
+    last_loss: Optional[torch.Tensor]
+
+    def __init__(self, params: ModelConfig = None):
+        super().__init__(params)
+        if not params:
+            params = ModelConfig()
+        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)
+        self.tok_embeddings.weight = self.output.weight
+        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)
+
+        self.apply(self._init_weights)
+
+        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)
+                )
+
+        self.last_loss = None
+        self.OUT = CausalLMOutputWithPast()
+        self._no_split_modules = [name for name, _ in self.named_modules()]
+
+    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,
+        kv_cache=False,
+        **keyargs,
+    ):
+        current_idx = 0
+        if "input_ids" in keyargs:
+            tokens = keyargs["input_ids"]
+        if "attention_mask" in keyargs:
+            targets = keyargs["attention_mask"]
+        if "current_idx" in keyargs:
+            current_idx = int(keyargs["current_idx"])
+
+        _bsz, seqlen = tokens.shape
+        h = self.tok_embeddings(tokens)
+        h = self.dropout(h)
+        pos_cis = self.pos_cis[current_idx : current_idx + seqlen]
+        for idx, layer in enumerate(self.layers):
+            h = layer(h, pos_cis, kv_cache)
+
+        h = self.norm(h)
+
+        if targets is not None:
+            logits = self.output(h)
+            self.last_loss = F.cross_entropy(
+                logits.view(-1, logits.size(-1)),
+                targets.view(-1),
+                ignore_index=0,
+                reduction="none",
+            )
+        else:
+            logits = self.output(h[:, [-1], :])
+            self.last_loss = None
+
+        self.OUT.__setitem__("logits", logits)
+        self.OUT.__setitem__("last_loss", self.last_loss)
+        return self.OUT
+
+    @torch.inference_mode()
+    def generate(
+        self,
+        idx,
+        eos,
+        max_new_tokens,
+        temperature=0.7,
+        top_k=8,
+        stream=True,
+        rp=1.0,
+        kv_cache=True,
+    ):
+        # rp: repetition_penalty
+        index = idx.shape[1]
+        init_inference = True
+        while idx.shape[1] < max_new_tokens - 1:
+            if init_inference or not kv_cache:
+                inference_res, init_inference = self(idx, kv_cache=kv_cache), False
+            else:
+                inference_res = self(
+                    idx[:, -1:], kv_cache=kv_cache, current_idx=idx.shape[1] - 1
+                )
+
+            logits = inference_res.logits
+            logits = logits[:, -1, :]
+
+            for token in set(idx.tolist()[0]):
+                logits[:, token] /= rp
+
+            if temperature == 0.0:
+                _, idx_next = torch.topk(logits, k=1, dim=-1)
+            else:
+                logits = logits / temperature
+                if top_k is not None:
+                    v, _ = torch.topk(logits, min(top_k, logits.size(-1)))
+                    logits[logits < v[:, [-1]]] = -float("Inf")
+
+                probs = F.softmax(logits, dim=-1)
+                idx_next = torch.multinomial(probs, num_samples=1, generator=None)
+
+            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):
+        idx_cond = (
+            idx
+            if idx.size(1) <= self.params.max_seq_len
+            else idx[:, -self.params.max_seq_len :]
+        )
+        inference_res = self(idx_cond)
+        logits = inference_res.logits
+        logits = logits[:, -1, :]
+        return logits