bilzepython/minimind-3v

1import math, torch, torch.nn.functional as F2from torch import nn3from transformers.activations import ACT2FN4from transformers import PreTrainedModel, GenerationMixin, PretrainedConfig5from transformers.modeling_outputs import MoeCausalLMOutputWithPast6 7# 🌏🌎🌍🌏🌎🌍🌏🌎🌍🌏🌎🌍🌏🌎🌍🌏🌎🌍🌏🌎🌍🌏🌎🌍🌏🌎🌍🌏🌎🌍🌏🌎🌍🌏🌎🌍🌏🌎🌍🌏🌎🌍🌏🌎🌍🌏🌎🌍🌏🌎🌍🌏8#                                     MiniMind Config9# 🌏🌎🌍🌏🌎🌍🌏🌎🌍🌏🌎🌍🌏🌎🌍🌏🌎🌍🌏🌎🌍🌏🌎🌍🌏🌎🌍🌏🌎🌍🌏🌎🌍🌏🌎🌍🌏🌎🌍🌏🌎🌍🌏🌎🌍🌏🌎🌍🌏🌎🌍🌏10class MiniMindConfig(PretrainedConfig):11    model_type = "minimind"12    def __init__(self, hidden_size=768, num_hidden_layers=8, use_moe=False, **kwargs):13        super().__init__(**kwargs)14        self.hidden_size = hidden_size15        self.num_hidden_layers = num_hidden_layers16        self.use_moe = use_moe17        self.dropout = kwargs.get("dropout", 0.0)18        self.vocab_size = kwargs.get("vocab_size", 6400)19        self.bos_token_id = kwargs.get("bos_token_id", 1)20        self.eos_token_id = kwargs.get("eos_token_id", 2)21        self.flash_attn = kwargs.get("flash_attn", True)22        self.num_attention_heads = kwargs.get("num_attention_heads", 8)23        self.num_key_value_heads = kwargs.get("num_key_value_heads", 4)24        self.head_dim = kwargs.get("head_dim", self.hidden_size // self.num_attention_heads)25        self.hidden_act = kwargs.get("hidden_act", 'silu')26        self.intermediate_size = kwargs.get("intermediate_size", math.ceil(hidden_size * math.pi / 64) * 64)27        self.max_position_embeddings = kwargs.get("max_position_embeddings", 32768)28        self.rms_norm_eps = kwargs.get("rms_norm_eps", 1e-6)29        self.rope_theta = kwargs.get("rope_theta", 1e6)30        self.inference_rope_scaling = kwargs.get("inference_rope_scaling", False)31        self.rope_scaling = {32            "beta_fast": 32,33            "beta_slow": 1,34            "factor": 16,35            "original_max_position_embeddings": 2048,36            "attention_factor": 1.0,37            "type": "yarn"38        } if self.inference_rope_scaling else None39        ### MoE specific configs (ignored if use_moe = False)40        self.num_experts = kwargs.get("num_experts", 4)41        self.num_experts_per_tok = kwargs.get("num_experts_per_tok", 1)42        self.moe_intermediate_size = kwargs.get("moe_intermediate_size", self.intermediate_size)43        self.norm_topk_prob = kwargs.get("norm_topk_prob", True)44        self.router_aux_loss_coef = kwargs.get("router_aux_loss_coef", 5e-4)45 46# 🌏🌎🌍🌏🌎🌍🌏🌎🌍🌏🌎🌍🌏🌎🌍🌏🌎🌍🌏🌎🌍🌏🌎🌍🌏🌎🌍🌏🌎🌍🌏🌎🌍🌏🌎🌍🌏🌎🌍🌏🌎🌍🌏🌎🌍🌏🌎🌍🌏🌎🌍🌏47#                                     MiniMind Model48# 🌏🌎🌍🌏🌎🌍🌏🌎🌍🌏🌎🌍🌏🌎🌍🌏🌎🌍🌏🌎🌍🌏🌎🌍🌏🌎🌍🌏🌎🌍🌏🌎🌍🌏🌎🌍🌏🌎🌍🌏🌎🌍🌏🌎🌍🌏🌎🌍🌏🌎🌍🌏49class RMSNorm(torch.nn.Module):50    def __init__(self, dim: int, eps: float = 1e-5):51        super().__init__()52        self.eps = eps53        self.weight = nn.Parameter(torch.ones(dim))54 55    def norm(self, x):56        return x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps)57 58    def forward(self, x):59        return (self.weight * self.norm(x.float())).type_as(x)60 61def precompute_freqs_cis(dim: int, end: int = int(32 * 1024), rope_base: float = 1e6, rope_scaling: dict = None):62    freqs, attn_factor = 1.0 / (rope_base ** (torch.arange(0, dim, 2)[: (dim // 2)].float() / dim)), 1.063    if rope_scaling is not None: # YaRN: f'(i) = f(i)((1-γ) + γ/s), where γ∈[0,1] is linear ramp64        orig_max, factor, beta_fast, beta_slow, attn_factor = (65            rope_scaling.get("original_max_position_embeddings", 2048), rope_scaling.get("factor", 16),66            rope_scaling.get("beta_fast", 32.0), rope_scaling.get("beta_slow", 1.0), rope_scaling.get("attention_factor", 1.0)67        )68        if end / orig_max > 1.0:69            inv_dim = lambda b: (dim * math.log(orig_max / (b * 2 * math.pi))) / (2 * math.log(rope_base))70            low, high = max(math.floor(inv_dim(beta_fast)), 0), min(math.ceil(inv_dim(beta_slow)), dim // 2 - 1)71            ramp = torch.clamp((torch.arange(dim // 2, device=freqs.device).float() - low) / max(high - low, 0.001), 0, 1)72            freqs = freqs * (1 - ramp + ramp / factor)73    t = torch.arange(end, device=freqs.device)74    freqs = torch.outer(t, freqs).float()75    freqs_cos = torch.cat([torch.cos(freqs), torch.cos(freqs)], dim=-1) * attn_factor76    freqs_sin = torch.cat([torch.sin(freqs), torch.sin(freqs)], dim=-1) * attn_factor77    return freqs_cos, freqs_sin78 79def apply_rotary_pos_emb(q, k, cos, sin, unsqueeze_dim=1):80    def rotate_half(x): return torch.cat((-x[..., x.shape[-1] // 2:], x[..., : x.shape[-1] // 2]), dim=-1)81    q_embed = ((q * cos.unsqueeze(unsqueeze_dim)) + (rotate_half(q) * sin.unsqueeze(unsqueeze_dim))).to(q.dtype)82    k_embed = ((k * cos.unsqueeze(unsqueeze_dim)) + (rotate_half(k) * sin.unsqueeze(unsqueeze_dim))).to(k.dtype)83    return q_embed, k_embed84 85def repeat_kv(x: torch.Tensor, n_rep: int) -> torch.Tensor:86    bs, slen, num_key_value_heads, head_dim = x.shape87    if n_rep == 1: return x88    return (x[:, :, :, None, :].expand(bs, slen, num_key_value_heads, n_rep, head_dim).reshape(bs, slen, num_key_value_heads * n_rep, head_dim))89 90class Attention(nn.Module):91    def __init__(self, config: MiniMindConfig):92        super().__init__()93        self.num_key_value_heads = config.num_attention_heads if config.num_key_value_heads is None else config.num_key_value_heads94        self.n_local_heads = config.num_attention_heads95        self.n_local_kv_heads = self.num_key_value_heads96        self.n_rep = self.n_local_heads // self.n_local_kv_heads97        self.head_dim = config.head_dim98        self.q_proj = nn.Linear(config.hidden_size, config.num_attention_heads * self.head_dim, bias=False)99        self.k_proj = nn.Linear(config.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)100        self.v_proj = nn.Linear(config.hidden_size, self.num_key_value_heads * self.head_dim, bias=False)101        self.o_proj = nn.Linear(config.num_attention_heads * self.head_dim, config.hidden_size, bias=False)102        self.q_norm = RMSNorm(self.head_dim, eps=config.rms_norm_eps)103        self.k_norm = RMSNorm(self.head_dim, eps=config.rms_norm_eps)104        self.attn_dropout = nn.Dropout(config.dropout)105        self.resid_dropout = nn.Dropout(config.dropout)106        self.dropout = config.dropout107        self.flash = hasattr(torch.nn.functional, 'scaled_dot_product_attention') and config.flash_attn108 109    def forward(self, x, position_embeddings, past_key_value=None, use_cache=False, attention_mask=None):110        bsz, seq_len, _ = x.shape111        xq, xk, xv = self.q_proj(x), self.k_proj(x), self.v_proj(x)112        xq = xq.view(bsz, seq_len, self.n_local_heads, self.head_dim)113        xk = xk.view(bsz, seq_len, self.n_local_kv_heads, self.head_dim)114        xv = xv.view(bsz, seq_len, self.n_local_kv_heads, self.head_dim)115        xq, xk = self.q_norm(xq), self.k_norm(xk)116        cos, sin = position_embeddings117        xq, xk = apply_rotary_pos_emb(xq, xk, cos, sin)118        if past_key_value is not None:119            xk = torch.cat([past_key_value[0], xk], dim=1)120            xv = torch.cat([past_key_value[1], xv], dim=1)121        past_kv = (xk, xv) if use_cache else None122        xq, xk, xv = (xq.transpose(1, 2), repeat_kv(xk, self.n_rep).transpose(1, 2), repeat_kv(xv, self.n_rep).transpose(1, 2))123        if self.flash and (seq_len > 1) and (past_key_value is None) and (attention_mask is None or torch.all(attention_mask == 1)):124            output = F.scaled_dot_product_attention(xq, xk, xv, dropout_p=self.dropout if self.training else 0.0, is_causal=True)125        else:126            scores = (xq @ xk.transpose(-2, -1)) / math.sqrt(self.head_dim)127            scores[:, :, :, -seq_len:] += torch.full((seq_len, seq_len), float("-inf"), device=scores.device).triu(1)128            if attention_mask is not None: scores += (1.0 - attention_mask.unsqueeze(1).unsqueeze(2)) * -1e9129            output = self.attn_dropout(F.softmax(scores.float(), dim=-1).type_as(xq)) @ xv130        output = output.transpose(1, 2).reshape(bsz, seq_len, -1)131        output = self.resid_dropout(self.o_proj(output))132        return output, past_kv133 134class FeedForward(nn.Module):135    def __init__(self, config: MiniMindConfig, intermediate_size: int = None):136        super().__init__()137        intermediate_size = intermediate_size or config.intermediate_size138        self.gate_proj = nn.Linear(config.hidden_size, intermediate_size, bias=False)139        self.down_proj = nn.Linear(intermediate_size, config.hidden_size, bias=False)140        self.up_proj = nn.Linear(config.hidden_size, intermediate_size, bias=False)141        self.act_fn = ACT2FN[config.hidden_act]142 143    def forward(self, x):144        return self.down_proj(self.act_fn(self.gate_proj(x)) * self.up_proj(x))145 146class MOEFeedForward(nn.Module):147    def __init__(self, config: MiniMindConfig):148        super().__init__()149        self.config = config150        self.gate = nn.Linear(config.hidden_size, config.num_experts, bias=False)151        self.experts = nn.ModuleList([FeedForward(config, intermediate_size=config.moe_intermediate_size) for _ in range(config.num_experts)])152        self.act_fn = ACT2FN[config.hidden_act]153 154    def forward(self, x):155        batch_size, seq_len, hidden_dim = x.shape156        x_flat = x.view(-1, hidden_dim)157        scores = F.softmax(self.gate(x_flat), dim=-1)158        topk_weight, topk_idx = torch.topk(scores, k=self.config.num_experts_per_tok, dim=-1, sorted=False)159        if self.config.norm_topk_prob: topk_weight = topk_weight / (topk_weight.sum(dim=-1, keepdim=True) + 1e-20)160        y = torch.zeros_like(x_flat)161        for i, expert in enumerate(self.experts):162            mask = (topk_idx == i)163            if mask.any():164                token_idx = mask.any(dim=-1).nonzero().flatten()165                weight = topk_weight[mask].view(-1, 1)166                y.index_add_(0, token_idx, (expert(x_flat[token_idx]) * weight).to(y.dtype))167            elif self.training:168                y[0, 0] += 0 * sum(p.sum() for p in expert.parameters())169        if self.training and self.config.router_aux_loss_coef > 0:170            load = F.one_hot(topk_idx, self.config.num_experts).float().mean(0)171            self.aux_loss = (load * scores.mean(0)).sum() * self.config.num_experts * self.config.router_aux_loss_coef172        else:173            self.aux_loss = scores.new_zeros(1).squeeze()174        return y.view(batch_size, seq_len, hidden_dim)175 176class MiniMindBlock(nn.Module):177    def __init__(self, layer_id: int, config: MiniMindConfig):178        super().__init__()179        self.self_attn = Attention(config)180        self.input_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)181        self.post_attention_layernorm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)182        self.mlp = FeedForward(config) if not config.use_moe else MOEFeedForward(config)183 184    def forward(self, hidden_states, position_embeddings, past_key_value=None, use_cache=False, attention_mask=None):185        residual = hidden_states186        hidden_states, present_key_value = self.self_attn(187            self.input_layernorm(hidden_states), position_embeddings,188            past_key_value, use_cache, attention_mask189        )190        hidden_states += residual191        hidden_states = hidden_states + self.mlp(self.post_attention_layernorm(hidden_states))192        return hidden_states, present_key_value193 194class MiniMindModel(nn.Module):195    def __init__(self, config: MiniMindConfig):196        super().__init__()197        self.config = config198        self.vocab_size, self.num_hidden_layers = config.vocab_size, config.num_hidden_layers199        self.embed_tokens = nn.Embedding(config.vocab_size, config.hidden_size)200        self.dropout = nn.Dropout(config.dropout)201        self.layers = nn.ModuleList([MiniMindBlock(l, config) for l in range(self.num_hidden_layers)])202        self.norm = RMSNorm(config.hidden_size, eps=config.rms_norm_eps)203        freqs_cos, freqs_sin = precompute_freqs_cis(dim=config.head_dim, end=config.max_position_embeddings, rope_base=config.rope_theta, rope_scaling=config.rope_scaling)204        self.register_buffer("freqs_cos", freqs_cos, persistent=False)205        self.register_buffer("freqs_sin", freqs_sin, persistent=False)206 207    def forward(self, input_ids, attention_mask=None, past_key_values=None, use_cache=False, **kwargs):208        batch_size, seq_length = input_ids.shape209        if hasattr(past_key_values, 'layers'): past_key_values = None210        past_key_values = past_key_values or [None] * len(self.layers)211        start_pos = past_key_values[0][0].shape[1] if past_key_values[0] is not None else 0212        hidden_states = self.dropout(self.embed_tokens(input_ids))213        position_embeddings = (self.freqs_cos[start_pos:start_pos + seq_length], self.freqs_sin[start_pos:start_pos + seq_length])214        presents = []215        for layer, past_key_value in zip(self.layers, past_key_values):216            hidden_states, present = layer(217                hidden_states,218                position_embeddings,219                past_key_value=past_key_value,220                use_cache=use_cache,221                attention_mask=attention_mask222            )223            presents.append(present)224        hidden_states = self.norm(hidden_states)225        aux_loss = sum([l.mlp.aux_loss for l in self.layers if isinstance(l.mlp, MOEFeedForward)], hidden_states.new_zeros(1).squeeze())226        return hidden_states, presents, aux_loss227 228class MiniMindForCausalLM(PreTrainedModel, GenerationMixin):229    config_class = MiniMindConfig230    def __init__(self, config: MiniMindConfig = None):231        self.config = config or MiniMindConfig()232        super().__init__(self.config)233        self.model = MiniMindModel(self.config)234        self.lm_head = nn.Linear(self.config.hidden_size, self.config.vocab_size, bias=False)235        self.model.embed_tokens.weight = self.lm_head.weight236    237    def forward(self, input_ids, attention_mask=None, past_key_values=None, use_cache=False, logits_to_keep=0, labels=None, **kwargs):238        hidden_states, past_key_values, aux_loss = self.model(input_ids, attention_mask, past_key_values, use_cache, **kwargs)239        slice_indices = slice(-logits_to_keep, None) if isinstance(logits_to_keep, int) else logits_to_keep240        logits = self.lm_head(hidden_states[:, slice_indices, :])241        loss = None242        if labels is not None:243            x, y = logits[..., :-1, :].contiguous(), labels[..., 1:].contiguous()244            loss = F.cross_entropy(x.view(-1, x.size(-1)), y.view(-1), ignore_index=-100)245        return MoeCausalLMOutputWithPast(loss=loss, aux_loss=aux_loss, logits=logits, past_key_values=past_key_values, hidden_states=hidden_states)246    247    # https://github.com/jingyaogong/minimind/discussions/611248    @torch.inference_mode()249    def generate(self, inputs=None, attention_mask=None, max_new_tokens=8192, temperature=0.85, top_p=0.85, top_k=50, eos_token_id=2, streamer=None, use_cache=True, num_return_sequences=1, do_sample=True, repetition_penalty=1.0, **kwargs):250        input_ids = kwargs.pop("input_ids", inputs).repeat(num_return_sequences, 1)251        attention_mask = attention_mask.repeat(num_return_sequences, 1) if attention_mask is not None else None252        past_key_values = kwargs.pop("past_key_values", None)253        finished = torch.zeros(input_ids.shape[0], dtype=torch.bool, device=input_ids.device)254        if streamer: streamer.put(input_ids.cpu())255        for _ in range(max_new_tokens):256            past_len = past_key_values[0][0].shape[1] if past_key_values else 0257            outputs = self.forward(input_ids[:, past_len:], attention_mask, past_key_values, use_cache=use_cache, **kwargs)258            attention_mask = torch.cat([attention_mask, attention_mask.new_ones(attention_mask.shape[0], 1)], -1) if attention_mask is not None else None259            logits = outputs.logits[:, -1, :] / temperature260            if repetition_penalty != 1.0:261                for i in range(input_ids.shape[0]): logits[i, torch.unique(input_ids[i])] /= repetition_penalty262            if top_k > 0: 263                logits[logits < torch.topk(logits, top_k)[0][..., -1, None]] = -float('inf')264            if top_p < 1.0:265                sorted_logits, sorted_indices = torch.sort(logits, descending=True)266                mask = torch.cumsum(torch.softmax(sorted_logits, dim=-1), dim=-1) > top_p267                mask[..., 1:], mask[..., 0] = mask[..., :-1].clone(), 0268                logits[mask.scatter(1, sorted_indices, mask)] = -float('inf')269            next_token = torch.multinomial(torch.softmax(logits, dim=-1), num_samples=1) if do_sample else torch.argmax(logits, dim=-1, keepdim=True)270            if eos_token_id is not None: next_token = torch.where(finished.unsqueeze(-1), next_token.new_full((next_token.shape[0], 1), eos_token_id), next_token)271            input_ids = torch.cat([input_ids, next_token], dim=-1)272            past_key_values = outputs.past_key_values if use_cache else None273            if streamer: streamer.put(next_token.cpu())274            if eos_token_id is not None:275                finished |= next_token.squeeze(-1).eq(eos_token_id)276                if finished.all(): break277        if streamer: streamer.end()278        if kwargs.get("return_kv"): return {'generated_ids': input_ids, 'past_kv': past_key_values}279        return input_ids