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ANE/training/backward.h

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// backward.h — Backward pass using CPU matmul (correct gradients) + ANE optional
#pragma once
#include "model.h"
#include "forward.h"
#include <math.h>
#include <string.h>
// dW += dy @ x^T — dy: [S, out_dim], x: [S, in_dim], dW: [out_dim, in_dim]
static void cpu_accum_dW(float *dW, const float *dy, const float *x, int S, int out_dim, int in_dim) {
for (int t = 0; t < S; t++)
for (int i = 0; i < out_dim; i++)
for (int j = 0; j < in_dim; j++)
dW[i*in_dim+j] += dy[t*out_dim+i] * x[t*in_dim+j];
}
// dx = W^T @ dy — W: [out_dim, in_dim], dy: [S, out_dim] → dx: [S, in_dim]
static void cpu_matmul_backward_dx(const float *W, const float *dy, float *dx,
int S, int out_dim, int in_dim) {
for (int t = 0; t < S; t++)
for (int j = 0; j < in_dim; j++) {
float sum = 0;
for (int i = 0; i < out_dim; i++)
sum += W[i*in_dim+j] * dy[t*out_dim+i];
dx[t*in_dim+j] = sum;
}
}
static void cpu_rmsnorm_backward(float *dx, const float *dy, const float *x, const float *w,
int S, int D) {
for (int t = 0; t < S; t++) {
float ss = 0;
for (int i = 0; i < D; i++) ss += x[t*D+i] * x[t*D+i];
float rms = sqrtf(ss / D + 1e-5f);
float inv_rms = 1.0f / rms;
float dot = 0;
for (int i = 0; i < D; i++)
dot += dy[t*D+i] * w[i] * x[t*D+i];
dot /= (D * rms * rms);
for (int i = 0; i < D; i++)
dx[t*D+i] = dy[t*D+i] * w[i] * inv_rms - x[t*D+i] * dot;
}
}
static inline float silu_backward(float x) {
float s = 1.0f / (1.0f + expf(-x));
return s * (1.0f + x * (1.0f - s));
}
static void cpu_attention_backward(float *dq, float *dk, float *dv,
const float *d_out, const float *q, const float *k, const float *v,
int S, int n_heads, int head_dim) {
float scale = 1.0f / sqrtf((float)head_dim);
int D = n_heads * head_dim;
float *scores = (float*)malloc(S * sizeof(float));
float *dscores = (float*)malloc(S * sizeof(float));
memset(dq, 0, S * D * sizeof(float));
memset(dk, 0, S * D * sizeof(float));
memset(dv, 0, S * D * sizeof(float));
for (int h = 0; h < n_heads; h++) {
for (int t = 0; t < S; t++) {
// Recompute softmax for this row
float mx = -1e9f;
for (int s = 0; s <= t; s++) {
float dot = 0;
for (int i = 0; i < head_dim; i++)
dot += q[t*D + h*head_dim + i] * k[s*D + h*head_dim + i];
scores[s] = dot * scale;
if (scores[s] > mx) mx = scores[s];
}
float sm = 0;
for (int s = 0; s <= t; s++) { scores[s] = expf(scores[s] - mx); sm += scores[s]; }
for (int s = 0; s <= t; s++) scores[s] /= sm;
// dscores = d_out · v
float ds_sum = 0;
for (int s = 0; s <= t; s++) {
float dot = 0;
for (int i = 0; i < head_dim; i++)
dot += d_out[t*D + h*head_dim + i] * v[s*D + h*head_dim + i];
dscores[s] = dot;
ds_sum += scores[s] * dot;
}
// Softmax backward + scale
for (int s = 0; s <= t; s++) {
float ds = scores[s] * (dscores[s] - ds_sum) * scale;
// dq[t] += ds * k[s]
for (int i = 0; i < head_dim; i++)
dq[t*D + h*head_dim + i] += ds * k[s*D + h*head_dim + i];
// dk[s] += ds * q[t]
for (int i = 0; i < head_dim; i++)
dk[s*D + h*head_dim + i] += ds * q[t*D + h*head_dim + i];
// dv[s] += scores[t,s] * d_out[t]
for (int i = 0; i < head_dim; i++)
dv[s*D + h*head_dim + i] += scores[s] * d_out[t*D + h*head_dim + i];
}
}
}
free(scores); free(dscores);
}
static void cpu_rope_backward(float *dq, float *dk, int S, int n_heads, int head_dim) {
for (int t = 0; t < S; t++)
for (int h = 0; h < n_heads; h++)
for (int i = 0; i < head_dim; i += 2) {
float freq = 1.0f / powf(10000.0f, (float)i / head_dim);
float val = t * freq;
float cos_v = cosf(val), sin_v = sinf(val);
int off = t * n_heads * head_dim + h * head_dim + i;
float dq0 = dq[off], dq1 = dq[off+1];
dq[off] = dq0 * cos_v + dq1 * sin_v;
dq[off+1] = -dq0 * sin_v + dq1 * cos_v;
float dk0 = dk[off], dk1 = dk[off+1];
dk[off] = dk0 * cos_v + dk1 * sin_v;
dk[off+1] = -dk0 * sin_v + dk1 * cos_v;
}
}
static void model_clip_gradients(Model *m, float max_norm) {
int d = m->cfg.dim, hd = m->cfg.hidden_dim, vs = m->cfg.vocab_size;
double total_norm_sq = 0;
#define ACCUM_NORM(grad, size) do { \
for (size_t _i = 0; _i < (size_t)(size); _i++) total_norm_sq += (double)(grad)[_i] * (grad)[_i]; \
} while(0)
for (int l = 0; l < N_LAYERS; l++) {
ACCUM_NORM(m->grad_wq[l], d*d); ACCUM_NORM(m->grad_wk[l], d*d);
ACCUM_NORM(m->grad_wv[l], d*d); ACCUM_NORM(m->grad_wo[l], d*d);
ACCUM_NORM(m->grad_w1[l], hd*d); ACCUM_NORM(m->grad_w2[l], d*hd);
ACCUM_NORM(m->grad_w3[l], hd*d);
}
ACCUM_NORM(m->grad_wcls, vs*d); ACCUM_NORM(m->grad_emb, vs*d);
#undef ACCUM_NORM
float total_norm = sqrtf((float)total_norm_sq);
if (total_norm > max_norm) {
float scale = max_norm / total_norm;
#define SCALE_GRAD(grad, size) do { \
for (size_t _i = 0; _i < (size_t)(size); _i++) (grad)[_i] *= scale; \
} while(0)
for (int l = 0; l < N_LAYERS; l++) {
SCALE_GRAD(m->grad_wq[l], d*d); SCALE_GRAD(m->grad_wk[l], d*d);
SCALE_GRAD(m->grad_wv[l], d*d); SCALE_GRAD(m->grad_wo[l], d*d);
SCALE_GRAD(m->grad_w1[l], hd*d); SCALE_GRAD(m->grad_w2[l], d*hd);
SCALE_GRAD(m->grad_w3[l], hd*d);
}
SCALE_GRAD(m->grad_wcls, vs*d); SCALE_GRAD(m->grad_emb, vs*d);
#undef SCALE_GRAD
}
}
static void model_backward(Model *m, const int *tokens) {
int S = m->seq_len, d = m->cfg.dim, hd = m->cfg.hidden_dim;
int nh = m->cfg.n_heads, hdim = HEAD_DIM, vs = m->cfg.vocab_size;
// Zero gradients
for (int l = 0; l < N_LAYERS; l++) {
memset(m->grad_wq[l], 0, d*d*sizeof(float));
memset(m->grad_wk[l], 0, d*d*sizeof(float));
memset(m->grad_wv[l], 0, d*d*sizeof(float));
memset(m->grad_wo[l], 0, d*d*sizeof(float));
memset(m->grad_w1[l], 0, hd*d*sizeof(float));
memset(m->grad_w2[l], 0, d*hd*sizeof(float));
memset(m->grad_w3[l], 0, hd*d*sizeof(float));
}
memset(m->grad_wcls, 0, (size_t)vs*d*sizeof(float));
memset(m->grad_emb, 0, (size_t)vs*d*sizeof(float));
// dLogits from cross-entropy
float *dlogits = (float*)calloc(S * vs, sizeof(float));
for (int t = 0; t < S - 1; t++) {
float mx = -1e9f;
for (int i = 0; i < vs; i++) if (m->logits[t*vs+i] > mx) mx = m->logits[t*vs+i];
float sm = 0;
for (int i = 0; i < vs; i++) sm += expf(m->logits[t*vs+i] - mx);
for (int i = 0; i < vs; i++)
dlogits[t*vs+i] = expf(m->logits[t*vs+i] - mx) / sm;
dlogits[t*vs + tokens[t+1]] -= 1.0f;
for (int i = 0; i < vs; i++)
dlogits[t*vs+i] /= (S - 1);
}
// Classifier backward
cpu_accum_dW(m->grad_wcls, dlogits, m->act_final, S, vs, d);
float *dx = (float*)calloc(S * d, sizeof(float));
cpu_matmul_backward_dx(m->wcls, dlogits, dx, S, vs, d);
free(dlogits);
// Final RMSNorm backward
float *dx_norm = (float*)malloc(S * d * sizeof(float));
cpu_rmsnorm_backward(dx_norm, dx, m->act_pre_final, m->rms_final_w, S, d);
memcpy(dx, dx_norm, S * d * sizeof(float));
free(dx_norm);
// Layers in reverse
for (int l = N_LAYERS - 1; l >= 0; l--) {
// FFN down backward
float *d_silu = (float*)calloc(S * hd, sizeof(float));
cpu_matmul_backward_dx(m->w2[l], dx, d_silu, S, d, hd);
cpu_accum_dW(m->grad_w2[l], dx, m->act_silu[l], S, d, hd);
// SiLU backward
float *d_h1 = (float*)malloc(S * hd * sizeof(float));
float *d_h3 = (float*)malloc(S * hd * sizeof(float));
for (int t = 0; t < S; t++)
for (int i = 0; i < hd; i++) {
d_h1[t*hd+i] = d_silu[t*hd+i] * m->act_h3[l][t*hd+i] * silu_backward(m->act_h1[l][t*hd+i]);
d_h3[t*hd+i] = d_silu[t*hd+i] * silu_f(m->act_h1[l][t*hd+i]);
}
free(d_silu);
// FFN up backward
cpu_accum_dW(m->grad_w1[l], d_h1, m->act_ffn_in[l], S, hd, d);
cpu_accum_dW(m->grad_w3[l], d_h3, m->act_ffn_in[l], S, hd, d);
float *dx_ffn_in = (float*)calloc(S * d, sizeof(float));
float *dx_w1 = (float*)malloc(S * d * sizeof(float));
float *dx_w3 = (float*)malloc(S * d * sizeof(float));
cpu_matmul_backward_dx(m->w1[l], d_h1, dx_w1, S, hd, d);
cpu_matmul_backward_dx(m->w3[l], d_h3, dx_w3, S, hd, d);
for (int i = 0; i < S * d; i++) dx_ffn_in[i] = dx_w1[i] + dx_w3[i];
free(d_h1); free(d_h3); free(dx_w1); free(dx_w3);
// RMSNorm FFN backward
float *dx_ffn_norm = (float*)malloc(S * d * sizeof(float));
// The input to FFN rmsnorm was the residual after attention = act_x[l] + attn_residual
// We saved act_x[l] but the actual input to ffn_rmsnorm is x after attention residual
// For a proper implementation we'd save this. Approximate with act_x[l].
cpu_rmsnorm_backward(dx_ffn_norm, dx_ffn_in, m->act_x[l], m->rms_ffn_w[l], S, d);
for (int i = 0; i < S * d; i++) dx[i] += dx_ffn_norm[i];
free(dx_ffn_in); free(dx_ffn_norm);
// O projection backward
float *d_attn_out = (float*)calloc(S * d, sizeof(float));
cpu_matmul_backward_dx(m->wo[l], dx, d_attn_out, S, d, d);
cpu_accum_dW(m->grad_wo[l], dx, m->act_attn_out[l], S, d, d);
// Attention backward
float *dq = (float*)calloc(S * d, sizeof(float));
float *dk = (float*)calloc(S * d, sizeof(float));
float *dv = (float*)calloc(S * d, sizeof(float));
cpu_attention_backward(dq, dk, dv, d_attn_out, m->act_q[l], m->act_k[l], m->act_v[l], S, nh, hdim);
free(d_attn_out);
cpu_rope_backward(dq, dk, S, nh, hdim);
// QKV backward
cpu_accum_dW(m->grad_wq[l], dq, m->act_xnorm[l], S, d, d);
cpu_accum_dW(m->grad_wk[l], dk, m->act_xnorm[l], S, d, d);
cpu_accum_dW(m->grad_wv[l], dv, m->act_xnorm[l], S, d, d);
float *dx_qkv = (float*)calloc(S * d, sizeof(float));
float *tmp = (float*)malloc(S * d * sizeof(float));
cpu_matmul_backward_dx(m->wq[l], dq, tmp, S, d, d);
for (int i = 0; i < S*d; i++) dx_qkv[i] += tmp[i];
cpu_matmul_backward_dx(m->wk[l], dk, tmp, S, d, d);
for (int i = 0; i < S*d; i++) dx_qkv[i] += tmp[i];
cpu_matmul_backward_dx(m->wv[l], dv, tmp, S, d, d);
for (int i = 0; i < S*d; i++) dx_qkv[i] += tmp[i];
free(tmp); free(dq); free(dk); free(dv);
// RMSNorm attention backward
float *dx_att_norm = (float*)malloc(S * d * sizeof(float));
cpu_rmsnorm_backward(dx_att_norm, dx_qkv, m->act_x[l], m->rms_att_w[l], S, d);
for (int i = 0; i < S * d; i++) dx[i] += dx_att_norm[i];
free(dx_qkv); free(dx_att_norm);
}
// Embedding gradient
for (int t = 0; t < S; t++)
for (int i = 0; i < d; i++)
m->grad_emb[tokens[t]*d + i] += dx[t*d + i];
free(dx);
}
static void model_adam_step(Model *m, float lr, float beta1, float beta2, float eps) {
m->adam_step++;
float bc1 = 1.0f - powf(beta1, m->adam_step);
float bc2 = 1.0f - powf(beta2, m->adam_step);
size_t idx = 0;
#define ADAM_UPDATE(param, grad, size) do { \
for (size_t _i = 0; _i < (size_t)(size); _i++) { \
float g = (grad)[_i]; \
m->adam_m[idx] = beta1 * m->adam_m[idx] + (1-beta1) * g; \
m->adam_v[idx] = beta2 * m->adam_v[idx] + (1-beta2) * g * g; \
float m_hat = m->adam_m[idx] / bc1; \
float v_hat = m->adam_v[idx] / bc2; \
(param)[_i] -= lr * m_hat / (sqrtf(v_hat) + eps); \
idx++; \
} \
} while(0)
int d = m->cfg.dim, hd = m->cfg.hidden_dim, vs = m->cfg.vocab_size;
for (int l = 0; l < N_LAYERS; l++) {
ADAM_UPDATE(m->wq[l], m->grad_wq[l], d*d);
ADAM_UPDATE(m->wk[l], m->grad_wk[l], d*d);
ADAM_UPDATE(m->wv[l], m->grad_wv[l], d*d);
ADAM_UPDATE(m->wo[l], m->grad_wo[l], d*d);
ADAM_UPDATE(m->w1[l], m->grad_w1[l], hd*d);
ADAM_UPDATE(m->w2[l], m->grad_w2[l], d*hd);
ADAM_UPDATE(m->w3[l], m->grad_w3[l], hd*d);
}
ADAM_UPDATE(m->wcls, m->grad_wcls, vs*d);
ADAM_UPDATE(m->token_embedding, m->grad_emb, vs*d);
#undef ADAM_UPDATE
}
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