feat: Add modeling_minimind.py for AutoModelForCausalLM support

modeling_minimind.py

@@ -0,0 +1,163 @@
+"""MiniMind Max2 Model for Transformers"""
+import math
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from typing import Optional, Tuple, List, Union
+from transformers import PreTrainedModel
+from transformers.modeling_outputs import CausalLMOutputWithPast
+from .configuration_minimind import MiniMindConfig
+
+class RMSNorm(nn.Module):
+    def __init__(self, dim, eps=1e-6):
+        super().__init__()
+        self.weight = nn.Parameter(torch.ones(dim))
+        self.eps = eps
+    def forward(self, x):
+        return x * torch.rsqrt(x.pow(2).mean(-1, keepdim=True) + self.eps) * self.weight
+
+class RotaryEmbedding(nn.Module):
+    def __init__(self, dim, max_pos=32768, base=10000.0):
+        super().__init__()
+        inv_freq = 1.0 / (base ** (torch.arange(0, dim, 2).float() / dim))
+        self.register_buffer("inv_freq", inv_freq)
+    def forward(self, x, pos_ids):
+        freqs = torch.outer(pos_ids.float(), self.inv_freq)
+        emb = torch.cat((freqs, freqs), dim=-1)
+        return emb.cos(), emb.sin()
+
+def rotate_half(x):
+    x1, x2 = x.chunk(2, dim=-1)
+    return torch.cat((-x2, x1), dim=-1)
+
+def apply_rope(q, k, cos, sin):
+    cos, sin = cos.unsqueeze(0).unsqueeze(0), sin.unsqueeze(0).unsqueeze(0)
+    return (q * cos) + (rotate_half(q) * sin), (k * cos) + (rotate_half(k) * sin)
+
+class Attention(nn.Module):
+    def __init__(self, config, layer_idx):
+        super().__init__()
+        self.num_heads = config.num_attention_heads
+        self.num_kv_heads = config.num_key_value_heads
+        self.head_dim = config.hidden_size // self.num_heads
+        self.kv_groups = self.num_heads // self.num_kv_heads
+        self.q_proj = nn.Linear(config.hidden_size, self.num_heads * self.head_dim, bias=False)
+        self.k_proj = nn.Linear(config.hidden_size, self.num_kv_heads * self.head_dim, bias=False)
+        self.v_proj = nn.Linear(config.hidden_size, self.num_kv_heads * self.head_dim, bias=False)
+        self.o_proj = nn.Linear(self.num_heads * self.head_dim, config.hidden_size, bias=False)
+        self.rotary = RotaryEmbedding(self.head_dim, config.max_position_embeddings, config.rope_theta)
+
+    def forward(self, x, mask=None, pos_ids=None, past_kv=None, use_cache=False):
+        B, L, _ = x.shape
+        q = self.q_proj(x).view(B, L, self.num_heads, self.head_dim).transpose(1, 2)
+        k = self.k_proj(x).view(B, L, self.num_kv_heads, self.head_dim).transpose(1, 2)
+        v = self.v_proj(x).view(B, L, self.num_kv_heads, self.head_dim).transpose(1, 2)
+        if pos_ids is None: pos_ids = torch.arange(L, device=x.device)
+        cos, sin = self.rotary(v, pos_ids)
+        q, k = apply_rope(q, k, cos, sin)
+        if past_kv: k, v = torch.cat([past_kv[0], k], 2), torch.cat([past_kv[1], v], 2)
+        new_kv = (k, v) if use_cache else None
+        k = k.repeat_interleave(self.kv_groups, 1)
+        v = v.repeat_interleave(self.kv_groups, 1)
+        attn = (q @ k.transpose(-2, -1)) / math.sqrt(self.head_dim)
+        if mask is not None: attn = attn + mask
+        attn = F.softmax(attn, dim=-1)
+        out = (attn @ v).transpose(1, 2).reshape(B, L, -1)
+        return self.o_proj(out), new_kv
+
+class Expert(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.gate = nn.Linear(config.hidden_size, config.intermediate_size // config.num_experts, bias=False)
+        self.up = nn.Linear(config.hidden_size, config.intermediate_size // config.num_experts, bias=False)
+        self.down = nn.Linear(config.intermediate_size // config.num_experts, config.hidden_size, bias=False)
+    def forward(self, x):
+        return self.down(F.silu(self.gate(x)) * self.up(x))
+
+class MoE(nn.Module):
+    def __init__(self, config):
+        super().__init__()
+        self.num_experts = config.num_experts
+        self.top_k = config.num_experts_per_token
+        self.router = nn.Linear(config.hidden_size, self.num_experts, bias=False)
+        self.experts = nn.ModuleList([Expert(config) for _ in range(self.num_experts)])
+
+    def forward(self, x):
+        B, L, D = x.shape
+        x_flat = x.view(-1, D)
+        logits = self.router(x_flat)
+        weights = F.softmax(logits, dim=-1)
+        top_w, top_i = torch.topk(weights, self.top_k, dim=-1)
+        top_w = top_w / top_w.sum(-1, keepdim=True)
+        out = torch.zeros_like(x_flat)
+        for i, exp in enumerate(self.experts):
+            mask = (top_i == i).any(-1)
+            if mask.any():
+                w = (top_w * (top_i == i).float()).sum(-1, keepdim=True)[mask]
+                out[mask] += w * exp(x_flat[mask])
+        return out.view(B, L, D), torch.tensor(0.0, device=x.device)
+
+class DecoderLayer(nn.Module):
+    def __init__(self, config, idx):
+        super().__init__()
+        self.attn = Attention(config, idx)
+        self.moe = MoE(config)
+        self.norm1 = RMSNorm(config.hidden_size, config.rms_norm_eps)
+        self.norm2 = RMSNorm(config.hidden_size, config.rms_norm_eps)
+
+    def forward(self, x, mask=None, pos_ids=None, past_kv=None, use_cache=False):
+        h, kv = self.attn(self.norm1(x), mask, pos_ids, past_kv, use_cache)
+        x = x + h
+        m, aux = self.moe(self.norm2(x))
+        return x + m, kv, aux
+
+class MiniMindPreTrainedModel(PreTrainedModel):
+    config_class = MiniMindConfig
+    base_model_prefix = "model"
+    supports_gradient_checkpointing = True
+
+class MiniMindModel(MiniMindPreTrainedModel):
+    def __init__(self, config):
+        super().__init__(config)
+        self.embed = nn.Embedding(config.vocab_size, config.hidden_size)
+        self.layers = nn.ModuleList([DecoderLayer(config, i) for i in range(config.num_hidden_layers)])
+        self.norm = RMSNorm(config.hidden_size, config.rms_norm_eps)
+        self.post_init()
+
+    def forward(self, input_ids, attention_mask=None, position_ids=None, past_key_values=None, use_cache=False, **kwargs):
+        B, L = input_ids.shape
+        h = self.embed(input_ids)
+        mask = torch.triu(torch.full((L, L), float("-inf"), device=h.device), 1).unsqueeze(0).unsqueeze(0)
+        cache = [] if use_cache else None
+        aux = 0.0
+        for i, layer in enumerate(self.layers):
+            pkv = past_key_values[i] if past_key_values else None
+            h, kv, a = layer(h, mask, position_ids, pkv, use_cache)
+            if use_cache: cache.append(kv)
+            aux += a
+        return self.norm(h), cache, aux
+
+class MiniMindForCausalLM(MiniMindPreTrainedModel):
+    _tied_weights_keys = ["lm_head.weight"]
+    
+    def __init__(self, config):
+        super().__init__(config)
+        self.model = MiniMindModel(config)
+        self.lm_head = nn.Linear(config.hidden_size, config.vocab_size, bias=False)
+        self.post_init()
+
+    def get_input_embeddings(self): return self.model.embed
+    def get_output_embeddings(self): return self.lm_head
+
+    def forward(self, input_ids=None, attention_mask=None, position_ids=None, past_key_values=None, 
+                labels=None, use_cache=None, return_dict=True, **kwargs):
+        h, cache, aux = self.model(input_ids, attention_mask, position_ids, past_key_values, use_cache or False)
+        logits = self.lm_head(h)
+        loss = None
+        if labels is not None:
+            loss = F.cross_entropy(logits[..., :-1, :].reshape(-1, logits.size(-1)), labels[..., 1:].reshape(-1))
+        return CausalLMOutputWithPast(loss=loss, logits=logits, past_key_values=cache)
+
+    def prepare_inputs_for_generation(self, input_ids, past_key_values=None, **kwargs):
+        if past_key_values: input_ids = input_ids[:, -1:]
+        return {"input_ids": input_ids, "past_key_values": past_key_values, "use_cache": True}