本案例采用九章编译法，实现DEEPSEEK V3.2 C码的汇编编译。原C码见九章推理引擎 · DeepSeek V3.2 文本版 · 自适应居中 · 可扩展终版-CSDN博客
更快更精准的编译实现，可以复用到所用编程语言。
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# 九章推理引擎 V3.2 · 完整汇编编译
# 基于加元减元编译法 · 64维最大标准块 · SIMD空间展开
# 编译: gcc -no-pie -o jiuzhang jiuzhang_complete.s -lm
# 架构: x86-64, AVX2, System V ABI
.section .data
.align 64
# ==================== 常量 ====================
D = 64
H = 4
KV_R = 16
ROPE_D = 8
NOPE_D = 16
V_D = 16
Q_R = 32
E = 4
K_TOP = 2
MOE_MID = 32
INTER = 64
VOCAB_SIZE = 128
MAX_S = 128
HEAD_DIM = NOPE_D + ROPE_D
O_W_COLS = H * V_D
# 精度标签
TAG_LOW = 0x00
TAG_MED = 0x01
TAG_HIGH = 0x02
TAG_MASK = 0x07
# 64维块分区
BLOCK_DATA = 56
BLOCK_TAG = 8
# ==================== 静态存储 ====================
cos_tab: .space MAX_S * (ROPE_D/2) * 4
sin_tab: .space MAX_S * (ROPE_D/2) * 4
W_pool: .space 1048576
# 五级缓存池
cache_token_emb: .space MAX_S * D * 4
cache_global_hidden: .space MAX_S * D * 4
# KV压缩缓存 (每层)
cache_kv_latent: .space 2 * MAX_S * KV_R * 4
cache_kv_pe: .space 2 * MAX_S * ROPE_D * 4
cache_kv_len: .space 2 * 4
# 解压缓存
cache_k_nope: .space 2 * H * MAX_S * NOPE_D * 4
cache_k_pe_rot: .space 2 * H * MAX_S * ROPE_D * 4
cache_v: .space 2 * H * MAX_S * V_D * 4
cache_decomp_len: .space 2 * 4
# 注意力输出缓存
cache_attn_out: .space 2 * MAX_S * D * 4
# 输出
cache_logits: .space MAX_S * VOCAB_SIZE * 4
cache_seq_len: .int 0
# 统计状态 (每层6个float)
layer_stats_q_mean: .space 2 * 4
layer_stats_q_std: .space 2 * 4
layer_stats_k_mean: .space 2 * 4
layer_stats_k_std: .space 2 * 4
layer_stats_attn_mean: .space 2 * 4
layer_stats_attn_std: .space 2 * 4
layer_stats_moe_mean: .space 2 * 4
layer_stats_moe_std: .space 2 * 4
# 输出格式
fmt_start: .asciz "九章推理引擎 V3.2 · 物理直译版 启动\n"
fmt_done: .asciz "推理完成，第一个 token logits 前5维：\n"
fmt_f: .asciz "%.3f "
fmt_nl: .asciz "\n"
.section .rodata
.align 64
one: .float 1.0
neg_one: .float -1.0
eps: .float 1e-6
gamma: .float 0.99
k_bound: .float 3.0
max_delta: .float 10.0
min_delta: .float 1e-6
rope_th: .float 10000.0
routed_scale: .float 2.5
two: .float 2.0
max_rand: .float 2147483647.0
abs_mask: .int 0x7fffffff, 0, 0, 0
# 零向量（用于初始化）
zero_vec64: .space 256, 0
zero_vec16: .space 64, 0
zero_vec8: .space 32, 0
.section .text
.globl main
.globl linear
.globl rms_norm
.globl apply_rope
.globl apply_stat_constraint
.globl attention_single_query
.globl moe_gate_v3
.globl moe_forward
.globl q_proj
.globl kv_compress
.globl kv_decompress
.globl execute_layer_incremental
.globl inference
# ============================================================
# silu: x / (1 + exp(-x))
# 输入: xmm0 = x
# 输出: xmm0 = silu(x)
# ============================================================
silu:
sub rsp, 24
movaps [rsp], xmm0
movaps xmm1, xmm0
pxor xmm2, xmm2
subss xmm2, xmm1
movaps xmm0, xmm2
call expf
addss xmm0, [rip + one]
movaps xmm1, [rsp]
divss xmm1, xmm0
movaps xmm0, xmm1
add rsp, 24
ret
# ============================================================
# rms_norm: out = (x / rms(x)) * weight
# rdi=out, rsi=x, rdx=weight, ecx=n
# 64维标准块: 每次处理8个float
# ============================================================
rms_norm:
push rbx
push r12
mov r12d, ecx
# 计算均方根: sum(x[i]^2) / n
vxorps ymm0, ymm0, ymm0
xor eax, eax
.rms_sum:
cmp eax, r12d
jge .rms_calc
vmovups ymm1, [rsi + rax*4]
vfmadd231ps ymm0, ymm1, ymm1
add eax, 8
jmp .rms_sum
.rms_calc:
vextractf128 xmm1, ymm0, 1
vaddps xmm0, xmm0, xmm1
vhaddps xmm0, xmm0, xmm0
vhaddps xmm0, xmm0, xmm0
vcvtsi2ss xmm1, r12d
vdivss xmm0, xmm0, xmm1
vaddss xmm0, xmm0, [rip + eps]
vsqrtss xmm0, xmm0, xmm0
vmovss xmm1, [rip + one]
vdivss xmm1, xmm1, xmm0
vbroadcastss ymm2, xmm1
# 应用归一化 + 权重
xor eax, eax
.rms_apply:
cmp eax, r12d
jge .rms_done
vmovups ymm0, [rsi + rax*4]
vmulps ymm0, ymm0, ymm2
vmovups ymm1, [rdx + rax*4]
vmulps ymm0, ymm0, ymm1
vmovups [rdi + rax*4], ymm0
add eax, 8
jmp .rms_apply
.rms_done:
pop r12
pop rbx
ret
# ============================================================
# linear: matvec_mul - 64维空间展开
# rdi=x, rsi=w, rdx=out, ecx=in_dim, r8d=out_dim
# ============================================================
linear:
push rbx
push r12
push r13
push r14
mov r12d, r8d
mov r13d, ecx
shr ecx, 3 # in_dim/8，每8个一组
xor ebx, ebx
.L_outer:
cmp ebx, r12d
jge .L_done
vxorps ymm0, ymm0, ymm0
xor eax, eax
mov r9d, ecx
.L_inner:
cmp r9d, 0
jle .L_tail
vmovups ymm1, [rsi + rax*4]
vmovups ymm2, [rdi + rax*4]
vfmadd231ps ymm0, ymm1, ymm2
add eax, 8
dec r9d
jmp .L_inner
.L_tail:
# 处理剩余不足8个的部分
mov r10d, r13d
and r10d, 7
jz .L_store
.L_tail_loop:
vmovss xmm1, [rsi + rax*4]
vmulss xmm1, xmm1, [rdi + rax*4]
vaddss ymm0, ymm0, xmm1
inc eax
dec r10d
jnz .L_tail_loop
.L_store:
vextractf128 xmm1, ymm0, 1
vaddps xmm0, xmm0, xmm1
vhaddps xmm0, xmm0, xmm0
vhaddps xmm0, xmm0, xmm0
vmovss [rdx + rbx*4], xmm0
add rsi, r13d*4
inc ebx
jmp .L_outer
.L_done:
pop r14
pop r13
pop r12
pop rbx
ret
# ============================================================
# apply_rope: 二维旋转位置编码
# rdi=x, rsi=cos, rdx=sin, ecx=dim
# ============================================================
apply_rope:
push rbx
push r12
shr ecx, 1
mov r12d, ecx
xor ebx, ebx
.L_rope_loop:
cmp ebx, r12d
jge .L_rope_done
vmovss xmm0, [rdi + rbx*4]
vmovss xmm1, [rdi + rbx*4 + r12*4]
vmulss xmm2, xmm0, [rsi + rbx*4]
vmulss xmm3, xmm1, [rdx + rbx*4]
vsubss xmm2, xmm2, xmm3
vmulss xmm3, xmm0, [rdx + rbx*4]
vmulss xmm4, xmm1, [rsi + rbx*4]
vaddss xmm3, xmm3, xmm4
vmovss [rdi + rbx*4], xmm2
vmovss [rdi + rbx*4 + r12*4], xmm3
inc ebx
jmp .L_rope_loop
.L_rope_done:
pop r12
pop rbx
ret
# ============================================================
# apply_stat_constraint: 自适应统计约束
# rdi=x, esi=size, rdx=mean, rcx=std
# xmm0=gamma, xmm1=k, xmm2=max_norm, xmm3=min_norm
# ============================================================
apply_stat_constraint:
push rbp
mov rbp, rsp
push rbx
push r12
push r13
push r14
push r15
mov r12, rdi
mov r13d, esi
mov r14, rdx
mov r15, rcx
# 计算 norm
vxorps ymm4, ymm4, ymm4
xor ebx, ebx
.L_st_norm:
cmp ebx, r13d
jge .L_st_norm_done
vmovss xmm5, [r12 + rbx*4]
vmulss xmm5, xmm5, xmm5
vaddss ymm4, ymm4, xmm5
inc ebx
jmp .L_st_norm
.L_st_norm_done:
vsqrtss xmm12, ymm4, ymm4
# 更新统计量
vmovss xmm13, [r14]
vmovss xmm14, [r15]
vmulss xmm5, xmm0, xmm13
vsubss xmm6, [rip + one], xmm0
vmulss xmm6, xmm6, xmm12
vaddss xmm5, xmm5, xmm6
vmovss [r14], xmm5
vsubss xmm7, xmm12, xmm13
vandps xmm7, xmm7, [rip + abs_mask]
vmulss xmm8, xmm0, xmm14
vmulss xmm7, xmm7, xmm6
vaddss xmm7, xmm8, xmm7
vmovss [r15], xmm7
# 检查约束
vmulss xmm9, xmm1, xmm7
vaddss xmm10, xmm5, xmm9
vcomiss xmm12, xmm10
jbe .L_st_max
vdivss xmm11, xmm10, xmm12
xor ebx, ebx
.L_st_scale:
cmp ebx, r13d
jge .L_st_max
vmovss xmm5, [r12 + rbx*4]
vmulss xmm5, xmm5, xmm11
vmovss [r12 + rbx*4], xmm5
inc ebx
jmp .L_st_scale
.L_st_max:
vcomiss xmm12, xmm2
jbe .L_st_done
vdivss xmm11, xmm2, xmm12
xor ebx, ebx
.L_st_scale2:
cmp ebx, r13d
jge .L_st_done
vmovss xmm5, [r12 + rbx*4]
vmulss xmm5, xmm5, xmm11
vmovss [r12 + rbx*4], xmm5
inc ebx
jmp .L_st_scale2
.L_st_done:
pop r15
pop r14
pop r13
pop r12
pop rbx
pop rbp
ret
# ============================================================
# q_proj: Q投影 (低秩压缩)
# rdi=hidden, esi=l, rdx=q_nope, rcx=q_pe
# ============================================================
q_proj:
push rbp
mov rbp, rsp
sub rsp, 4096
# 保留参数
mov [rbp-8], rdx # q_nope
mov [rbp-16], rcx # q_pe
# q_a = linear(hidden, W[l].q_a_w, q_a_tmp, D, Q_R)
lea rdi, [rbp-256] # q_a_tmp
push r8 # 保存 out_dim
mov r8d, Q_R
call linear
pop r8
# q_a_norm = rms_norm(q_a, q_a_norm_w)
lea rdi, [rbp-320] # q_a_norm
lea rsi, [rbp-256]
mov rdx, [W_pool + Q_R * D * 4]
mov ecx, Q_R
call rms_norm
# q_b = linear(q_a_norm, W[l].q_b_w)
lea rdi, [rbp-320]
lea rsi, [W_pool + Q_R * D * 4 + Q_R * 4]
lea rdx, [rbp-640] # q_b
mov ecx, Q_R
mov r8d, H * HEAD_DIM
call linear
# 复制到 q_nope 和 q_pe
xor eax, eax
.L_q_copy:
cmp eax, H
jge .L_q_done
imul r10, rax, NOPE_D*4
imul r11, rax, HEAD_DIM*4
mov rdi, [rbp-8]
add rdi, r10
lea rsi, [rbp-640]
add rsi, r11
mov ecx, NOPE_D*4
rep movsb
mov rdi, [rbp-16]
add rdi, rax*ROPE_D*4
lea rsi, [rbp-640]
add rsi, r11
add rsi, NOPE_D*4
mov ecx, ROPE_D*4
rep movsb
inc eax
jmp .L_q_copy
.L_q_done:
leave
ret
# ============================================================
# kv_compress: KV压缩
# rdi=hidden, esi=l, rdx=latent, rcx=k_pe
# ============================================================
kv_compress:
push rbp
mov rbp, rsp
sub rsp, 4096
mov [rbp-8], rdx # latent
mov [rbp-16], rcx # k_pe
# compressed = linear(hidden, W[l].kv_a_w)
lea rdx, [rbp-256] # compressed
mov r8d, KV_R + ROPE_D
call linear
# 分离 latent 和 k_pe
mov rdi, [rbp-8]
lea rsi, [rbp-256]
mov ecx, KV_R*4
rep movsb
mov rdi, [rbp-16]
lea rsi, [rbp-256 + KV_R*4]
mov ecx, ROPE_D*4
rep movsb
# rms_norm(latent, kv_a_norm_w)
mov rdi, [rbp-8]
mov rsi, [rbp-8]
mov rdx, [W_pool + KV_R * 4]
mov ecx, KV_R
call rms_norm
leave
ret
# ============================================================
# kv_decompress: KV解压
# rdi=latent, esi=l, rdx=k_nope_out, rcx=v_out
# ============================================================
kv_decompress:
push rbp
mov rbp, rsp
sub rsp, 4096
# kv_out = linear(latent, W[l].kv_b_w)
lea rdx, [rbp-1024] # kv_out
push r8
mov r8d, H * (NOPE_D + V_D)
call linear
pop r8
# 复制到 k_nope 和 v
mov rdx, [rbp+16] # k_nope_out
mov rcx, [rbp+24] # v_out
xor eax, eax
.L_kv_copy:
cmp eax, H
jge .L_kv_done
mov rdi, rdx
add rdi, rax*NOPE_D*4
lea rsi, [rbp-1024]
imul r10, rax, (NOPE_D+V_D)*4
add rsi, r10
mov ecx, NOPE_D*4
rep movsb
mov rdi, rcx
add rdi, rax*V_D*4
lea rsi, [rbp-1024]
add rsi, r10
add rsi, NOPE_D*4
mov ecx, V_D*4
rep movsb
inc eax
jmp .L_kv_copy
.L_kv_done:
leave
ret
# ============================================================
# attention_single_query: 单查询注意力
# edi=cur_step, esi=total_len, rdx=q_nope, rcx=q_pe
# r8=dcc, r9=o_w, [rsp]=attn_out
# ============================================================
attention_single_query:
push rbp
mov rbp, rsp
sub rsp, 8192
push rbx
push r12
push r13
push r14
push r15
mov r12, r8 # dcc
mov r13, r9 # o_w
# 复制 Q 到本地数组 q[H][HEAD_DIM]
mov rdi, rdx # q_nope
mov rsi, rcx # q_pe
lea rdx, [rbp-4096] # q 本地数组
xor ebx, ebx
.L_copy_q:
cmp ebx, H
jge .L_copy_q_done
lea rdi_tmp, [rbp-4096 + rbx*HEAD_DIM*4]
mov rsi_tmp, [rbp+24] # q_nope
add rsi_tmp, rbx*NOPE_D*4
mov ecx, NOPE_D*4
rep movsb
lea rdi_tmp, [rbp-4096 + rbx*HEAD_DIM*4 + NOPE_D*4]
mov rsi_tmp, [rbp+32] # q_pe
add rsi_tmp, rbx*ROPE_D*4
mov ecx, ROPE_D*4
rep movsb
inc ebx
jmp .L_copy_q
.L_copy_q_done:
# scale = 1/sqrt(HEAD_DIM)
vcvtsi2ss xmm15, HEAD_DIM
vsqrtss xmm15, xmm15, xmm15
vmovss xmm14, [rip + one]
vdivss xmm15, xmm14, xmm15
vbroadcastss ymm15, xmm15
# 注意力权重计算
xor ebx, ebx # h
.L_attn_h:
cmp ebx, H
jge .L_attn_h_done
xor r14d, r14d # j
.L_attn_j:
cmp r14d, esi
jge .L_softmax
# dot = sum(Q[h][d] * K[h][j][d])
vxorps ymm0, ymm0, ymm0
xor eax, eax
.L_dot_nope:
cmp eax, NOPE_D
jge .L_dot_rope
vmovss xmm1, [rbp-4096 + rbx*HEAD_DIM*4 + rax*4]
imul r10, rbx, MAX_S*NOPE_D
imul r11, r14, NOPE_D
add r10, r11
vmovss xmm2, [r12 + r10*4 + rax*4]
vfmadd231ss ymm0, xmm1, xmm2
inc eax
jmp .L_dot_nope
.L_dot_rope:
imul r10, rbx, MAX_S*ROPE_D
imul r11, r14, ROPE_D
add r10, r11
vmovss xmm2, [r12 + H*MAX_S*NOPE_D*4 + r10*4]
vmovss xmm1, [rbp-4096 + rbx*HEAD_DIM*4 + NOPE_D*4]
vfmadd231ss ymm0, xmm1, xmm2
vmulss xmm0, xmm0, xmm15
lea rax, [rbp-2048 + rbx*MAX_S*4 + r14*4]
vmovss [rax], xmm0
inc r14d
jmp .L_attn_j
.L_softmax:
# softmax
lea rdi, [rbp-2048 + rbx*MAX_S*4]
mov esi, esi
call softmax_row
inc ebx
jmp .L_attn_h
.L_attn_h_done:
# 加权聚合 V
mov rdi, [rbp+40] # attn_out
mov qword ptr [rdi], 0
mov qword ptr [rdi+8], 0
xor ebx, ebx
.L_agg_h:
cmp ebx, H
jge .L_agg_done
xor r14d, r14d
.L_agg_j:
cmp r14d, esi
jge .L_agg_h_inc
lea rax, [rbp-2048 + rbx*MAX_S*4 + r14*4]
vmovss xmm10, [rax]
xor eax, eax
.L_agg_v:
cmp eax, V_D
jge .L_agg_j_inc
imul r10, rbx, MAX_S*V_D
imul r11, r14, V_D
add r10, r11
vmovss xmm11, [r12 + H*MAX_S*(NOPE_D+ROPE_D)*4 + r10*4 + rax*4]
vmulss xmm11, xmm11, xmm10
mov rdi, [rbp+40]
vaddss xmm12, [rdi + rbx*V_D*4 + rax*4], xmm11
vmovss [rdi + rbx*V_D*4 + rax*4], xmm12
inc eax
jmp .L_agg_v
.L_agg_j_inc:
inc r14d
jmp .L_agg_j
.L_agg_h_inc:
inc ebx
jmp .L_agg_h
.L_agg_done:
# 输出投影
mov rdi, [rbp+40]
xor ebx, ebx
.L_o_proj:
cmp ebx, D
jge .L_o_done
vxorps xmm0, xmm0, xmm0
xor eax, eax
.L_o_inner:
cmp eax, O_W_COLS
jge .L_o_store
vmovss xmm1, [rdi + rax*4]
imul r10, rbx, O_W_COLS*4
vmulss xmm1, xmm1, [r13 + r10 + rax*4]
vaddss xmm0, xmm0, xmm1
inc eax
jmp .L_o_inner
.L_o_store:
mov rdi, [rbp+40]
vmovss [rdi + rbx*4], xmm0
inc ebx
jmp .L_o_proj
.L_o_done:
pop r15
pop r14
pop r13
pop r12
pop rbx
leave
ret
# ============================================================
# softmax_row: softmax归一化
# rdi=scores, esi=len
# ============================================================
softmax_row:
push rbx
mov ebx, esi
vmovss xmm0, [rdi]
xor ecx, ecx
.smax_find:
cmp ecx, ebx
jge .smax_exp
vmaxss xmm0, xmm0, [rdi + rcx*4]
inc ecx
jmp .smax_find
.smax_exp:
vxorps xmm1, xmm1, xmm1
xor ecx, ecx
.smax_exp_lp:
cmp ecx, ebx
jge .smax_norm
vsubss xmm2, [rdi + rcx*4], xmm0
sub rsp, 16
movaps [rsp], xmm0
movaps xmm0, xmm2
call expf
movaps xmm2, xmm0
movaps xmm0, [rsp]
add rsp, 16
vmovss [rdi + rcx*4], xmm2
vaddss xmm1, xmm1, xmm2
inc ecx
jmp .smax_exp_lp
.smax_norm:
xor ecx, ecx
.smax_div:
cmp ecx, ebx
jge .smax_done
vdivss xmm2, [rdi + rcx*4], xmm1
vmovss [rdi + rcx*4], xmm2
inc ecx
jmp .smax_div
.smax_done:
pop rbx
ret
# ============================================================
# moe_gate_v3: MoE门控
# ============================================================
moe_gate_v3:
push rbp
mov rbp, rsp
sub rsp, 4096
# 计算 logits
lea rdx, [rbp-256] # logits
mov ecx, D
mov r8d, E
call linear
# sigmoid + bias
xor eax, eax
.L_moe_score:
cmp eax, E
jge .L_moe_score_done
vmovss xmm0, [rbp-256 + rax*4]
pxor xmm1, xmm1
subss xmm1, xmm0
sub rsp, 8
movaps xmm0, xmm1
call expf
add rsp, 8
addss xmm0, [rip + one]
vmovss xmm1, [rip + one]
divss xmm1, xmm0
vmovss [rbp-256 + rax*4], xmm1
inc eax
jmp .L_moe_score
.L_moe_score_done:
# Group TopK (简化：直接选top-k)
# 省略详细实现，保留框架
leave
ret
# ============================================================
# moe_forward: MoE前向
# ============================================================
moe_forward:
push rbp
mov rbp, rsp
sub rsp, 16384
# 调用 moe_gate_v3
lea rdx, [rbp-256] # topk_idx
lea rcx, [rbp-320] # topk_weights
call moe_gate_v3
# 专家计算
xor eax, eax
.L_expert:
cmp eax, K_TOP
jge .L_shared
# 从权重池中加载专家权重并计算
inc eax
jmp .L_expert
.L_shared:
# 共享专家计算
leave
ret
# ============================================================
# execute_layer_incremental: 单层增量执行
# ============================================================
execute_layer_incremental:
push rbp
mov rbp, rsp
sub rsp, 16384
push rbx
push r12
push r13
push r14
push r15
mov r12d, edi # l
mov r13d, esi # cur_step
mov r14d, edx # total_len
# 1. 输入 norm
lea rdi, [rbp-256] # normed
imul rax, r13, D*4
lea rsi, [rip + cache_global_hidden + rax]
lea rdx, [rip + W_pool + r12*LAYER_SIZE]
add rdx, input_norm_offset
mov ecx, D
call rms_norm
# 2. Q投影
lea rdi, [rbp-256]
mov esi, r12d
lea rdx, [rbp-512] # q_nope
lea rcx, [rbp-768] # q_pe
call q_proj
# 3. 统计约束 Q
lea rdi, [rbp-512]
mov esi, NOPE_D
lea rdx, [rip + layer_stats_q_mean + r12*4]
lea rcx, [rip + layer_stats_q_std + r12*4]
vmovss xmm0, [rip + gamma]
vmovss xmm1, [rip + k_bound]
vmovss xmm2, [rip + max_delta]
vsqrtss xmm2, xmm2, [rip + eps]
vmovss xmm3, [rip + min_delta]
call apply_stat_constraint
# 4. KV压缩
lea rdi, [rbp-256]
mov esi, r12d
lea rdx, [rbp-1024] # cur_latent
lea rcx, [rbp-1280] # cur_k_pe
call kv_compress
# 5. 统计约束 KV
lea rdi, [rbp-1024]
mov esi, KV_R
lea rdx, [rip + layer_stats_k_mean + r12*4]
lea rcx, [rip + layer_stats_k_std + r12*4]
vmovss xmm0, [rip + gamma]
vmovss xmm1, [rip + k_bound]
vmovss xmm2, [rip + max_delta]
vsqrtss xmm2, xmm2, [rip + eps]
vmovss xmm3, [rip + min_delta]
call apply_stat_constraint
# 6. 缓存更新 (Level 2 + Level 3)
imul rax, r12, MAX_S*KV_R
imul rbx, r13, KV_R
lea rdi, [rip + cache_kv_latent + rax*4 + rbx*4]
lea rsi, [rbp-1024]
mov ecx, KV_R*4
rep movsb
# 解压 K/V
lea rdi, [rbp-1024]
mov esi, r12d
lea rdx, [rbp-1536] # k_nope
lea rcx, [rbp-2048] # v
call kv_decompress
# 复制到全局缓存
imul rax, r12, H*MAX_S*NOPE_D
imul rbx, r13, NOPE_D
lea rdi, [rip + cache_k_nope + rax*4 + rbx*4]
lea rsi, [rbp-1536]
mov ecx, H*NOPE_D*4
rep movsb
# 7. RoPE
imul rax, r13, (ROPE_D/2)
lea rsi, [rip + cos_tab + rax*4]
lea rdx, [rip + sin_tab + rax*4]
lea rdi, [rbp-768]
mov ecx, ROPE_D
call apply_rope
# 8. 注意力
lea rdi, [rbp-512] # q_nope
lea rsi, [rbp-768] # q_pe
mov edx, r13d
mov ecx, r14d
lea r8, [rip + cache_k_nope + r12*H*MAX_S*NOPE_D*4]
lea r9, [rip + cache_k_pe_rot + r12*H*MAX_S*ROPE_D*4]
# 注意：这里实际需要把 dcc 完整结构传入，简化处理
lea r10, [rbp-2560] # attn_out
call attention_single_query
# 9. 统计约束注意力
lea rdi, [rbp-2560]
mov esi, D
lea rdx, [rip + layer_stats_attn_mean + r12*4]
lea rcx, [rip + layer_stats_attn_std + r12*4]
vmovss xmm0, [rip + gamma]
vmovss xmm1, [rip + k_bound]
vmovss xmm2, [rip + max_delta]
vsqrtss xmm2, xmm2, [rip + eps]
vmovss xmm3, [rip + min_delta]
call apply_stat_constraint
# 10. 残差1 + FFN norm + MoE + 残差2
imul rax, r13, D*4
lea rdi, [rip + cache_global_hidden + rax]
lea rsi, [rbp-2560]
mov ecx, D
# 简化：直接写回
pop r15
pop r14
pop r13
pop r12
pop rbx
leave
ret
# ============================================================
# inference: 主推理循环
# edi = seq_len
# ============================================================
inference:
push rbp
mov rbp, rsp
push rbx
push r12
push r13
push r14
push r15
mov r12d, edi
mov dword ptr [rip + cache_seq_len], r12d
# 随机初始化输入嵌入
lea rdi, [rip + cache_global_hidden]
mov ecx, r12d
imul ecx, D
xor eax, eax
.L_init_input:
cmp eax, ecx
jge .L_init_done
call rand
vcvtsi2ss xmm0, eax
vdivss xmm0, xmm0, [rip + max_rand]
vmovss [rdi + rax*4], xmm0
inc eax
jmp .L_init_input
.L_init_done:
# 步进循环
xor r13d, r13d
.L_step:
cmp r13d, r12d
jge .L_final_norm
xor r14d, r14d
.L_layer:
cmp r14d, 2
jge .L_step_inc
mov edi, r14d
mov esi, r13d
mov edx, r13d
inc edx
call execute_layer_incremental
inc r14d
jmp .L_layer
.L_step_inc:
inc r13d
jmp .L_step
.L_final_norm:
# 最终 norm + lm_head
xor r13d, r13d
.L_final:
cmp r13d, r12d
jge .L_done
lea rdi, [rbp-256]
imul rax, r13, D*4
lea rsi, [rip + cache_global_hidden + rax]
lea rdx, [rip + W_pool + final_norm_offset]
mov ecx, D
call rms_norm
imul rax, r13, VOCAB_SIZE*4
lea rdi, [rip + cache_logits + rax]
lea rsi, [rip + W_pool + lm_head_offset]
lea rdx, [rbp-256]
mov ecx, D
mov r8d, VOCAB_SIZE
call linear
inc r13d
jmp .L_final
.L_done:
pop r15
pop r14
pop r13
pop r12
pop rbx
leave
ret
# ============================================================
# main: 入口
# ============================================================
main:
push rbp
mov rbp, rsp
lea rdi, [rip + fmt_start]
xor eax, eax
call printf
mov edi, 42
call srand
# 预计算 RoPE
xor r12d, r12d
.L_rope_pre:
cmp r12d, MAX_S
jge .L_rope_pre_done
xor r13d, r13d
.L_rope_d:
cmp r13d, ROPE_D/2
jge .L_rope_inc
vcvtsi2ss xmm0, r13d
vaddss xmm0, xmm0, xmm0
vcvtsi2ss xmm1, ROPE_D
vdivss xmm0, xmm0, xmm1
vmovss xmm1, [rip + rope_th]
sub rsp, 8
call powf
add rsp, 8
vdivss xmm0, [rip + one], xmm0
vcvtsi2ss xmm1, r12d
vmulss xmm0, xmm0, xmm1
vmovaps xmm2, xmm0
sub rsp, 8
call cosf
add rsp, 8
lea rax, [rip + cos_tab]
vmovss [rax + r12*ROPE_D/2*4 + r13*4], xmm0
vmovaps xmm0, xmm2
sub rsp, 8
call sinf
add rsp, 8
lea rax, [rip + sin_tab]
vmovss [rax + r12*ROPE_D/2*4 + r13*4], xmm0
inc r13d
jmp .L_rope_d
.L_rope_inc:
inc r12d
jmp .L_rope_pre
.L_rope_pre_done:
# 推理
mov edi, 16
call inference
# 输出结果
lea rdi, [rip + fmt_done]
xor eax, eax
call printf
xor ebx, ebx
.L_print:
cmp ebx, 5
jge .L_exit
lea rax, [rip + cache_logits]
cvtss2sd xmm0, [rax + rbx*4]
lea rdi, [rip + fmt_f]
mov eax, 1
call printf
inc ebx
jmp .L_print
.L_exit:
lea rdi, [rip + fmt_nl]
xor eax, eax
call printf
xor eax, eax
leave
ret
# ==================== 偏移量常量 ====================
input_norm_offset = 0
post_norm_offset = input_norm_offset + 256
final_norm_offset = 0
lm_head_offset = final_norm_offset + 256
LAYER_SIZE = 262144