[System] You are DeepSeek V4 Flash running locally. Answer accurately, preserve technical details, and use tools only when the prompt asks for tool use.

[User]
Suggest a minimal correctness-preserving optimization for this excerpt.

File: ds4.c

```c
81908081908,
    0x2b08081908192b08, 0x2b08081919080808, 0x2b08082b08190819, 0x2b08190808080819,
    0x2b08190808081908, 0x2b08190808190808, 0x2b08190808191919, 0x2b08190819080808,
    0x2b081908192b0808, 0x2b08191908080808, 0x2b0819191908192b, 0x2b0819192b191908,
    0x2b08192b08082b19, 0x2b08192b19080808, 0x2b08192b192b0808, 0x2b082b080808082b,
    0x2b082b1908081908, 0x2b082b2b08190819, 0x2b19080808081908, 0x2b19080808190808,
    0x2b190808082b1908, 0x2b19080819080808, 0x2b1908082b2b0819, 0x2b1908190819192b,
    0x2b1908192b080808, 0x2b19082b19081919, 0x2b19190808080808, 0x2b191908082b082b,
    0x2b19190819081908, 0x2b19191919190819, 0x2b192b082b080819, 0x2b192b19082b0808,
    0x2b2b08080808082b, 0x2b2b080819190808, 0x2b2b08082b081919, 0x2b2b081908082b19,
    0x2b2b082b08080808, 0x2b2b190808192b08, 0x2b2b2b0819190808, 0x2b2b2b1908081908,
};

static int8_t iq2xxs_signed_grid[256][128][8];
static int8_t iq2xxs_signs[128][8];
static pthread_once_t iq2xxs_signed_grid_once = PTHREAD_ONCE_INIT;

static void iq2xxs_signed_grid_init(void) {
    for (uint32_t s = 0; s < 128; s++) {
        const uint8_t signs = ksigns_iq2xs[s];
        for (uint32_t j = 0; j < 8; j++) {
            iq2xxs_signs[s][j] = (int8_t)((signs & kmask_iq2xs[j]) ? -1 : 1);
        }
    }

    for (uint32_t g = 0; g < 256; g++) {
        const uint8_t *grid = (const uint8_t *)(iq2xxs_grid + g);
        for (uint32_t s = 0; s < 128; s++) {
            const uint8_t signs = ksigns_iq2xs[s];
            for (uint32_t j = 0; j < 8; j++) {
                const int v = (int)grid[j];
                iq2xxs_signed_grid[g][s][j] = (int8_t)((signs & kmask_iq2xs[j]) ? -v : v);
            }
        }
    }
}

static inline DS4_MAYBE_UNUSED int32_t dot_iq2_pair_16(const int8_t *grid0, const int8_t *grid1, const int8_t *q8) {
#if defined(__ARM_NEON) && defined(__ARM_FEATURE_DOTPROD)
    const int8x16_t gv = vcombine_s8(vld1_s8(grid0), vld1_s8(grid1));
    const int32x4_t acc = vdotq_s32(vdupq_n_s32(0), gv, vld1q_s8(q8));
    return vaddvq_s32(acc);
#elif defined(__ARM_NEON)
    const int8x16_t gv = vcombine_s8(vld1_s8(grid0), vld1_s8(grid1));
    const int8x16_t qv = vld1q_s8(q8);
    const int16x8_t p0 = vmull_s8(vget_low_s8(gv), vget_low_s8(qv));
    const int16x8_t p1 = vmull_s8(vget_high_s8(gv), vget_high_s8(qv));
    return vaddvq_s32(vaddq_s32(vpaddlq_s16(p0), vpaddlq_s16(p1)));
#else
    int32_t sum = 0;
    for (uint32_t i = 0; i < 8; i++) sum += (int32_t)grid0[i] * (int32_t)q8[i];
    for (uint32_t i = 0; i < 8; i++) sum += (int32_t)grid1[i] * (int32_t)q8[8 + i];
    return sum;
#endif
}
```


[System] You are DeepSeek V4 Flash running locally. Answer accurately, preserve technical details, and use tools only when the prompt asks for tool use.

[User]
Trova bug sottili e spiega quali invarianti dell'inferenza possono rompersi.

File: ds4.c

```c
10 + 16), xv01);
            int32x4_t d11 = vdupq_n_s32(0);
            d11 = vdotq_s32(d11, vld1q_s8(q11),      xv10);
            d11 = vdotq_s32(d11, vld1q_s8(q11 + 16), xv11);

            acc00 = vfmaq_n_f32(acc00, vcvtq_f32_s32(d00), f16_to_f32(s00) * xscale[b]);
            acc01 = vfmaq_n_f32(acc01, vcvtq_f32_s32(d01), f16_to_f32(s01) * xscale[b + 1]);
            acc10 = vfmaq_n_f32(acc10, vcvtq_f32_s32(d10), f16_to_f32(s10) * xscale[b]);
            acc11 = vfmaq_n_f32(acc11, vcvtq_f32_s32(d11), f16_to_f32(s11) * xscale[b + 1]);
        }

        if (b < blocks) {
            uint16_t s0, s1;
            memcpy(&s0, row0 + b * 34, sizeof(s0));
            memcpy(&s1, row1 + b * 34, sizeof(s1));
            const int8_t *xqb = xq + b * 32;
            const int8x16_t xv0 = vld1q_s8(xqb);
            const int8x16_t xv1 = vld1q_s8(xqb + 16);
            const int8_t *q0 = (const int8_t *)(row0 + b * 34 + 2);
            const int8_t *q1 = (const int8_t *)(row1 + b * 34 + 2);
            int32x4_t d0 = vdupq_n_s32(0);
            d0 = vdotq_s32(d0, vld1q_s8(q0),      xv0);
            d0 = vdotq_s32(d0, vld1q_s8(q0 + 16), xv1);
            int32x4_t d1 = vdupq_n_s32(0);
            d1 = vdotq_s32(d1, vld1q_s8(q1),      xv0);
            d1 = vdotq_s32(d1, vld1q_s8(q1 + 16), xv1);
            acc00 = vfmaq_n_f32(acc00, vcvtq_f32_s32(d0), f16_to_f32(s0) * xscale[b]);
            acc10 = vfmaq_n_f32(acc10, vcvtq_f32_s32(d1), f16_to_f32(s1) * xscale[b]);
        }

        *out0 = vaddvq_f32(vaddq_f32(acc00, acc01));
        *out1 = vaddvq_f32(vaddq_f32(acc10, acc11));
        return;
    }
#endif

    float acc0 = 0.0f;
    float acc1 = 0.0f;
    for (uint64_t b = 0; b < blocks; b++) {
        uint16_t s0_bits;
        uint16_t s1_bits;
        memcpy(&s0_bits, row0 + b * 34, sizeof(s0_bits));
        memcpy(&s1_bits, row1 + b * 34, sizeof(s1_bits));
        const int8_t *q0 = (const int8_t *)(row0 + b * 34 + 2);
        const int8_t *q1 = (const int8_t *)(row1 + b * 34 + 2);
        const uint64_t i0 = b * 32;
        const uint64_t n = in_dim - i0 < 32 ? in_dim - i0 : 32;
        acc0 += f16_to_f32(s0_bits) * xscale[b] * (float)dot_i8_32(q0, xq + i0, n);
        acc1 += f16_to_f32(s1_bits) * xscale[b] * (float)dot_i8_32(q1, xq + i0, n);
    }
    *out0 = acc0;
    *out1 = acc1;
}

static void quantize_q8_0_activation(const float *x, int8_t *xq, float *scale, uint64_t n) {
    const uint64_t blocks = (n + 31) / 32;
    for (uint64_t b = 0; b < blocks; b++) {
        const uint64_t i0 = b * 32;
```

<｜begin▁of▁sentence｜>You are DeepSeek V4 Flash running locally. Answer accurately, preserve technical details, and use tools only when the prompt asks for tool use.<｜User｜>Trova bug sottili e spiega quali invarianti dell'inferenza possono rompersi.

File: ds4.c

```c
10 + 16), xv01);
            int32x4_t d11 = vdupq_n_s32(0);
            d11 = vdotq_s32(d11, vld1q_s8(q11),      xv10);
            d11 = vdotq_s32(d11, vld1q_s8(q11 + 16), xv11);

            acc00 = vfmaq_n_f32(acc00, vcvtq_f32_s32(d00), f16_to_f32(s00) * xscale[b]);
            acc01 = vfmaq_n_f32(acc01, vcvtq_f32_s32(d01), f16_to_f32(s01) * xscale[b + 1]);
            acc10 = vfmaq_n_f32(acc10, vcvtq_f32_s32(d10), f16_to_f32(s10) * xscale[b]);
            acc11 = vfmaq_n_f32(acc11, vcvtq_f32_s32(d11), f16_to_f32(s11) * xscale[b + 1]);
        }

        if (b < blocks) {
            uint16_t s0, s1;
            memcpy(&s0, row0 + b * 34, sizeof(s0));
            memcpy(&s1, row1 + b * 34, sizeof(s1));
            const int8_t *xqb = xq + b * 32;
            const int8x16_t xv0 = vld1q_s8(xqb);
            const int8x16_t xv1 = vld1q_s8(xqb + 16);
            const int8_t *q0 = (const int8_t *)(row0 + b * 34 + 2);
            const int8_t *q1 = (const int8_t *)(row1 + b * 34 + 2);
            int32x4_t d0 = vdupq_n_s32(0);
            d0 = vdotq_s32(d0, vld1q_s8(q0),      xv0);
            d0 = vdotq_s32(d0, vld1q_s8(q0 + 16), xv1);
            int32x4_t d1 = vdupq_n_s32(0);
            d1 = vdotq_s32(d1, vld1q_s8(q1),      xv0);
            d1 = vdotq_s32(d1, vld1q_s8(q1 + 16), xv1);
            acc00 = vfmaq_n_f32(acc00, vcvtq_f32_s32(d0), f16_to_f32(s0) * xscale[b]);
            acc10 = vfmaq_n_f32(acc10, vcvtq_f32_s32(d1), f16_to_f32(s1) * xscale[b]);
        }

        *out0 = vaddvq_f32(vaddq_f32(acc00, acc01));
        *out1 = vaddvq_f32(vaddq_f32(acc10, acc11));
        return;
    }
#endif

    float acc0 = 0.0f;
    float acc1 = 0.0f;
    for (uint64_t b = 0; b < blocks; b++) {
        uint16_t s0_bits;
        uint16_t s1_bits;
        memcpy(&s0_bits, row0 + b * 34, sizeof(s0_bits));
        memcpy(&s1_bits, row1 + b * 34, sizeof(s1_bits));
        const int8_t *q0 = (const int8_t *)(row0 + b * 34 + 2);
        const int8_t *q1 = (const int8_t *)(row1 + b * 34 + 2);
        const uint64_t i0 = b * 32;
        const uint64_t n = in_dim - i0 < 32 ? in_dim - i0 : 32;
        acc0 += f16_to_f32(s0_bits) * xscale[b] * (float)dot_i8_32(q0, xq + i0, n);
        acc1 += f16_to_f32(s1_bits) * xscale[b] * (float)dot_i8_32(q1, xq + i0, n);
    }
    *out0 = acc0;
    *out1 = acc1;
}

static void quantize_q8_0_activation(const float *x, int8_t *xq, float *scale, uint64_t n) {
    const uint64_t blocks = (n + 31) / 32;
    for (uint64_t b = 0; b < blocks; b++) {
        const uint64_t i0 = b * 32;
```<｜Assistant｜></think>


[System] You are DeepSeek V4 Flash running locally. Answer accurately, preserve technical details, and use tools only when the prompt asks for tool use.

[User]
Trova bug sottili e spiega quali invarianti dell'inferenza possono rompersi.

File: ds4.c

```c
10 + 16), xv01);
            int32x4_t d11 = vdupq_n_s32(0);
            d11 = vdotq_s32(d11, vld1q_s8(q11),      xv10);
            d11 = vdotq_s32(d11, vld1q_s8(q11 + 16), xv11);

            acc00 = vfmaq_n_f32(acc00, vcvtq_f32_s32(d00), f16_to_f32(s00) * xscale[b]);
            acc01 = vfmaq_n_f32(acc01, vcvtq_f32_s32(d01), f16_to_f32(s01) * xscale[b + 1]);
            acc10 = vfmaq_n_f32(acc10, vcvtq_f32_s32(d10), f16_to_f32(s10) * xscale[b]);
            acc11 = vfmaq_n_f32(acc11, vcvtq_f32_s32(d11), f16_to_f32(s11) * xscale[b + 1]);
        }

        if (b < blocks) {
            uint16_t s0, s1;
            memcpy(&s0, row0 + b * 34, sizeof(s0));
            memcpy(&s1, row1 + b * 34, sizeof(s1));
            const int8_t *xqb = xq + b * 32;
            const int8x16_t xv0 = vld1q_s8(xqb);
            const int8x16_t xv1 = vld1q_s8(xqb + 16);
            const int8_t *q0 = (const int8_t *)(row0 + b * 34 + 2);
            const int8_t *q1 = (const int8_t *)(row1 + b * 34 + 2);
            int32x4_t d0 = vdupq_n_s32(0);
            d0 = vdotq_s32(d0, vld1q_s8(q0),      xv0);
            d0 = vdotq_s32(d0, vld1q_s8(q0 + 16), xv1);
            int32x4_t d1 = vdupq_n_s32(0);
            d1 = vdotq_s32(d1, vld1q_s8(q1),      xv0);
            d1 = vdotq_s32(d1, vld1q_s8(q1 + 16), xv1);
            acc00 = vfmaq_n_f32(acc00, vcvtq_f32_s32(d0), f16_to_f32(s0) * xscale[b]);
            acc10 = vfmaq_n_f32(acc10, vcvtq_f32_s32(d1), f16_to_f32(s1) * xscale[b]);
        }

        *out0 = vaddvq_f32(vaddq_f32(acc00, acc01));
        *out1 = vaddvq_f32(vaddq_f32(acc10, acc11));
        return;
    }
#endif

    float acc0 = 0.0f;
    float acc1 = 0.0f;
    for (uint64_t b = 0; b < blocks; b++) {
        uint16_t s0_bits;
        uint16_t s1_bits;
        memcpy(&s0_bits, row0 + b * 34, sizeof(s0_bits));
        memcpy(&s1_bits, row1 + b * 34, sizeof(s1_bits));
        const int8_t *q0 = (const int8_t *)(row0 + b * 34 + 2);
        const int8_t *q1 = (const int8_t *)(row1 + b * 34 + 2);
        const uint64_t i0 = b * 32;
        const uint64_t n = in_dim - i0 < 32 ? in_dim - i0 : 32;
        acc0 += f16_to_f32(s0_bits) * xscale[b] * (float)dot_i8_32(q0, xq + i0, n);
        acc1 += f16_to_f32(s1_bits) * xscale[b] * (float)dot_i8_32(q1, xq + i0, n);
    }
    *out0 = acc0;
    *out1 = acc1;
}

static void quantize_q8_0_activation(const float *x, int8_t *xq, float *scale, uint64_t n) {
    const uint64_t blocks = (n + 31) / 32;
    for (uint64_t b = 0; b < blocks; b++) {
        const uint64_t i0 = b * 32;
```

<｜begin▁of▁sentence｜>You are DeepSeek V4 Flash running locally. Answer accurately, preserve technical details, and use tools only when the prompt asks for tool use.<｜User｜>Trova bug sottili e spiega quali invarianti dell'inferenza possono rompersi.

File: ds4.c

```c
10 + 16), xv01);
            int32x4_t d11 = vdupq_n_s32(0);
            d11 = vdotq_s32(d11, vld1q_s8(q11),      xv10);
            d11 = vdotq_s32(d11, vld1q_s8(q11 + 16), xv11);

            acc00 = vfmaq_n_f32(acc00, vcvtq_f32_s32(d00), f16_to_f32(s00) * xscale[b]);
            acc01 = vfmaq_n_f32(acc01, vcvtq_f32_s32(d01), f16_to_f32(s01) * xscale[b + 1]);
            acc10 = vfmaq_n_f32(acc10, vcvtq_f32_s32(d10), f16_to_f32(s10) * xscale[b]);
            acc11 = vfmaq_n_f32(acc11, vcvtq_f32_s32(d11), f16_to_f32(s11) * xscale[b + 1]);
        }

        if (b < blocks) {
            uint16_t s0, s1;
            memcpy(&s0, row0 + b * 34, sizeof(s0));
            memcpy(&s1, row1 + b * 34, sizeof(s1));
            const int8_t *xqb = xq + b * 32;
            const int8x16_t xv0 = vld1q_s8(xqb);
            const int8x16_t xv1 = vld1q_s8(xqb + 16);
            const int8_t *q0 = (const int8_t *)(row0 + b * 34 + 2);
            const int8_t *q1 = (const int8_t *)(row1 + b * 34 + 2);
            int32x4_t d0 = vdupq_n_s32(0);
            d0 = vdotq_s32(d0, vld1q_s8(q0),      xv0);
            d0 = vdotq_s32(d0, vld1q_s8(q0 + 16), xv1);
            int32x4_t d1 = vdupq_n_s32(0);
            d1 = vdotq_s32(d1, vld1q_s8(q1),      xv0);
            d1 = vdotq_s32(d1, vld1q_s8(q1 + 16), xv1);
            acc00 = vfmaq_n_f32(acc00, vcvtq_f32_s32(d0), f16_to_f32(s0) * xscale[b]);
            acc10 = vfmaq_n_f32(acc10, vcvtq_f32_s32(d1), f16_to_f32(s1) * xscale[b]);
        }

        *out0 = vaddvq_f32(vaddq_f32(acc00, acc01));
        *out1 = vaddvq_f32(vaddq_f32(acc10, acc11));
        return;
    }
#endif

    float acc0 = 0.0f;
    float acc1 = 0.0f;
    for (uint64_t b = 0; b < blocks; b++) {
        uint16_t s0_bits;
        uint16_t s1_bits;
        memcpy(&s0_bits, row0 + b * 34, sizeof(s0_bits));
        memcpy(&s1_bits, row1 + b * 34, sizeof(s1_bits));
        const int8_t *q0 = (const int8_t *)(row0 + b * 34 + 2);
        const int8_t *q1 = (const int8_t *)(row1 + b * 34 + 2);
        const uint64_t i0 = b * 32;
        const uint64_t n = in_dim - i0 < 32 ? in_dim - i0 : 32;
        acc0 += f16_to_f32(s0_bits) * xscale[b] * (float)dot_i8_32(q0, xq + i0, n);
        acc1 += f16_to_f32(s1_bits) * xscale[b] * (float)dot_i8_32(q1, xq + i0, n);
    }
    *out0 = acc0;
    *out1 = acc1;
}

static void quantize_q8_0_activation(const float *x, int8_t *xq, float *scale, uint64_t n) {
    const uint64_t blocks = (n + 31) / 32;
    for (uint64_t b = 0; b < blocks; b++) {
        const uint64_t i0 = b * 32;
```<｜Assistant｜><think>


[System] You are DeepSeek V4 Flash running locally. Answer accurately, preserve technical details, and use tools only when the prompt asks for tool use.

[User]
Review this excerpt for correctness risks, memory lifetime issues, and performance bottlenecks.

File: ds4.c

```c
n(const float *x, int8_t *xq, float *scale, uint64_t n) {
    const uint64_t blocks = (n + 31) / 32;
    for (uint64_t b = 0; b < blocks; b++) {
        const uint64_t i0 = b * 32;
        const uint64_t bn = n - i0 < 32 ? n - i0 : 32;
        float amax = 0.0f;
        for (uint64_t i = 0; i < bn; i++) {
            const float ax = fabsf(x[i0 + i]);
            if (ax > amax) amax = ax;
        }
        const float d = amax / 127.0f;
        const float id = d != 0.0f ? 1.0f / d : 0.0f;
        scale[b] = d;
        for (uint64_t i = 0; i < bn; i++) {
            int v = (int)lrintf(x[i0 + i] * id);
            if (v > 127) v = 127;
            if (v < -128) v = -128;
            xq[i0 + i] = (int8_t)v;
        }
        for (uint64_t i = bn; i < 32 && i0 + i < blocks * 32; i++) {
            xq[i0 + i] = 0;
        }
    }
}

static void quantize_q8_0_batch_worker(void *vctx, uint64_t t0, uint64_t t1) {
    quantize_q8_0_batch_ctx *ctx = vctx;
    for (uint64_t t = t0; t < t1; t++) {
        quantize_q8_0_activation(ctx->x + t * ctx->in_dim,
                                 ctx->xq + t * ctx->blocks * 32,
                                 ctx->xscale + t * ctx->blocks,
                                 ctx->in_dim);
    }
}

static void quantize_q8_0_activation_batch(
        const float *x,
        int8_t      *xq,
        float       *xscale,
        uint64_t     n_tok,
        uint64_t     in_dim) {
    quantize_q8_0_batch_ctx ctx = {
        .x = x,
        .xq = xq,
        .xscale = xscale,
        .in_dim = in_dim,
        .blocks = (in_dim + 31) / 32,
    };
    ds4_parallel_for(n_tok, quantize_q8_0_batch_worker, &ctx);
}

static void matvec_q8_0_worker(void *vctx, uint64_t r0, uint64_t r1) {
    matvec_q8_0_ctx *ctx = vctx;

    for (uint64_t r = r0; r < r1; r++) {
        const uint64_t o = ctx->row0 + r;
        const uint8_t *row = ctx->data + o * ctx->blocks * 34;
        ctx->out[r] = dot_q8_0_row(row, ctx->xq, ctx->xscale, ctx->in_dim, ctx->blocks);
    }
}

static void matvec_q8_0_pair_worker(void *vctx, uint64_t r0, uint64_t r1) {
    matvec_q8_0_pair_ctx *ctx = vctx;

    for (uint64_t r = r0; r < r1; r++) {
        const uint8_t *row0 = ctx->data0 + r * ctx->blocks * 34;
        const uint8_t *row1 = ctx->data1 + r * ctx->blocks * 34;
        dot_q8_0_row_pair(row0, row1, ctx->xq, ctx->xscale, ctx->in_dim, ctx->blocks,
                          ctx->out0 + r, ctx->out1 + r);
    }
}

static void matvec_q8_0_grouped_worker(void *vctx, uint64_t r0, uint64_t r1) {
    matvec_q8_0_grouped_ctx *ctx = vctx;
```

<｜begin▁of▁sentence｜>You are DeepSeek V4 Flash running locally. Answer accurately, preserve technical details, and use tools only when the prompt asks for tool use.<｜User｜>Review this excerpt for correctness risks, memory lifetime issues, and performance bottlenecks.

File: ds4.c

```c
n(const float *x, int8_t *xq, float *scale, uint64_t n) {
    const uint64_t blocks = (n + 31) / 32;
    for (uint64_t b = 0; b < blocks; b++) {
        const uint64_t i0 = b * 32;
        const uint64_t bn = n - i0 < 32 ? n - i0 : 32;
        float amax = 0.0f;
        for (uint64_t i = 0; i < bn; i++) {
            const float ax = fabsf(x[i0 + i]);
            if (ax > amax) amax = ax;
        }
        const float d = amax / 127.0f;
        const float id = d != 0.0f ? 1.0f / d : 0.0f;
        scale[b] = d;
        for (uint64_t i = 0; i < bn; i++) {
            int v = (int)lrintf(x[i0 + i] * id);
            if (v > 127) v = 127;
            if (v < -128) v = -128;
            xq[i0 + i] = (int8_t)v;
        }
        for (uint64_t i = bn; i < 32 && i0 + i < blocks * 32; i++) {
            xq[i0 + i] = 0;
        }
    }
}

static void quantize_q8_0_batch_worker(void *vctx, uint64_t t0, uint64_t t1) {
    quantize_q8_0_batch_ctx *ctx = vctx;
    for (uint64_t t = t0; t < t1; t++) {
        quantize_q8_0_activation(ctx->x + t * ctx->in_dim,
                                 ctx->xq + t * ctx->blocks * 32,
                                 ctx->xscale + t * ctx->blocks,
                                 ctx->in_dim);
    }
}

static void quantize_q8_0_activation_batch(
        const float *x,
        int8_t      *xq,
        float       *xscale,
        uint64_t     n_tok,
        uint64_t     in_dim) {
    quantize_q8_0_batch_ctx ctx = {
        .x = x,
        .xq = xq,
        .xscale = xscale,
        .in_dim = in_dim,
        .blocks = (in_dim + 31) / 32,
    };
    ds4_parallel_for(n_tok, quantize_q8_0_batch_worker, &ctx);
}

static void matvec_q8_0_worker(void *vctx, uint64_t r0, uint64_t r1) {
    matvec_q8_0_ctx *ctx = vctx;

    for (uint64_t r = r0; r < r1; r++) {
        const uint64_t o = ctx->row0 + r;
        const uint8_t *row = ctx->data + o * ctx->blocks * 34;
        ctx->out[r] = dot_q8_0_row(row, ctx->xq, ctx->xscale, ctx->in_dim, ctx->blocks);
    }
}

static void matvec_q8_0_pair_worker(void *vctx, uint64_t r0, uint64_t r1) {
    matvec_q8_0_pair_ctx *ctx = vctx;

    for (uint64_t r = r0; r < r1; r++) {
        const uint8_t *row0 = ctx->data0 + r * ctx->blocks * 34;
        const uint8_t *row1 = ctx->data1 + r * ctx->blocks * 34;
        dot_q8_0_row_pair(row0, row1, ctx->xq, ctx->xscale, ctx->in_dim, ctx->blocks,
                          ctx->out0 + r, ctx->out1 + r);
    }
}

static void matvec_q8_0_grouped_worker(void *vctx, uint64_t r0, uint64_t r1) {
    matvec_q8_0_grouped_ctx *ctx = vctx;
```<｜Assistant｜></think>


[System] You are DeepSeek V4 Flash running locally. Answer accurately, preserve technical details, and use tools only when the prompt asks for tool use.

[User]
Review this excerpt for correctness risks, memory lifetime issues, and performance bottlenecks.

File: ds4.c

```c
n(const float *x, int8_t *xq, float *scale, uint64_t n) {
    const uint64_t blocks = (n + 31) / 32;
    for (uint64_t b = 0; b < blocks; b++) {
        const uint64_t i0 = b * 32;
        const uint64_t bn = n - i0 < 32 ? n - i0 : 32;
        float amax = 0.0f;
        for (uint64_t i = 0; i < bn; i++) {
            const float ax = fabsf(x[i0 + i]);
            if (ax > amax) amax = ax;
        }
        const float d = amax / 127.0f;
        const float id = d != 0.0f ? 1.0f / d : 0.0f;
        scale[b] = d;
        for (uint64_t i = 0; i < bn; i++) {
            int v = (int)lrintf(x[i0 + i] * id);
            if (v > 127) v = 127;
            if (v < -128) v = -128;
            xq[i0 + i] = (int8_t)v;
        }
        for (uint64_t i = bn; i < 32 && i0 + i < blocks * 32; i++) {
            xq[i0 + i] = 0;
        }
    }
}

static void quantize_q8_0_batch_worker(void *vctx, uint64_t t0, uint64_t t1) {
    quantize_q8_0_batch_ctx *ctx = vctx;
    for (uint64_t t = t0; t < t1; t++) {
        quantize_q8_0_activation(ctx->x + t * ctx->in_dim,
                                 ctx->xq + t * ctx->blocks * 32,
                                 ctx->xscale + t * ctx->blocks,
                                 ctx->in_dim);
    }
}

static void quantize_q8_0_activation_batch(
        const float *x,
        int8_t      *xq,
        float       *xscale,
        uint64_t     n_tok,
        uint64_t     in_dim) {
    quantize_q8_0_batch_ctx ctx = {
        .x = x,
        .xq = xq,
        .xscale = xscale,
        .in_dim = in_dim,
        .blocks = (in_dim + 31) / 32,
    };
    ds4_parallel_for(n_tok, quantize_q8_0_batch_worker, &ctx);
}

static void matvec_q8_0_worker(void *vctx, uint64_t r0, uint64_t r1) {
    matvec_q8_0_ctx *ctx = vctx;

    for (uint64_t r = r0; r < r1; r++) {
        const uint64_t o = ctx->row0 + r;
        const uint8_t *row = ctx->data + o * ctx->blocks * 34;
        ctx->out[r] = dot_q8_0_row(row, ctx->xq, ctx->xscale, ctx->in_dim, ctx->blocks);
    }
}

static void matvec_q8_0_pair_worker(void *vctx, uint64_t r0, uint64_t r1) {
    matvec_q8_0_pair_ctx *ctx = vctx;

    for (uint64_t r = r0; r < r1; r++) {
        const uint8_t *row0 = ctx->data0 + r * ctx->blocks * 34;
        const uint8_t *row1 = ctx->data1 + r * ctx->blocks * 34;
        dot_q8_0_row_pair(row0, row1, ctx->xq, ctx->xscale, ctx->in_dim, ctx->blocks,
                          ctx->out0 + r, ctx->out1 + r);
    }
}

static void matvec_q8_0_grouped_worker(void *vctx, uint64_t r0, uint64_t r1) {
    matvec_q8_0_grouped_ctx *ctx = vctx;
```

<｜begin▁of▁sentence｜>You are DeepSeek V4 Flash running locally. Answer accurately, preserve technical details, and use tools only when the prompt asks for tool use.<｜User｜>Review this excerpt for correctness risks, memory lifetime issues, and performance bottlenecks.

File: ds4.c

```c
n(const float *x, int8_t *xq, float *scale, uint64_t n) {
    const uint64_t blocks = (n + 31) / 32;
    for (uint64_t b = 0; b < blocks; b++) {
        const uint64_t i0 = b * 32;
        const uint64_t bn = n - i0 < 32 ? n - i0 : 32;
        float amax = 0.0f;
        for (uint64_t i = 0; i < bn; i++) {
            const float ax = fabsf(x[i0 + i]);
            if (ax > amax) amax = ax;
        }
        const float d = amax / 127.0f;
        const float id = d != 0.0f ? 1.0f / d : 0.0f;
        scale[b] = d;
        for (uint64_t i = 0; i < bn; i++) {
            int v = (int)lrintf(x[i0 + i] * id);
            if (v > 127) v = 127;
            if (v < -128) v = -128;
            xq[i0 + i] = (int8_t)v;
        }
        for (uint64_t i = bn; i < 32 && i0 + i < blocks * 32; i++) {
            xq[i0 + i] = 0;
        }
    }
}

static void quantize_q8_0_batch_worker(void *vctx, uint64_t t0, uint64_t t1) {
    quantize_q8_0_batch_ctx *ctx = vctx;
    for (uint64_t t = t0; t < t1; t++) {
        quantize_q8_0_activation(ctx->x + t * ctx->in_dim,
                                 ctx->xq + t * ctx->blocks * 32,
                                 ctx->xscale + t * ctx->blocks,
                                 ctx->in_dim);
    }
}

static void quantize_q8_0_activation_batch(
        const float *x,
        int8_t      *xq,
        float       *xscale,
        uint64_t     n_tok,
        uint64_t     in_dim) {
    quantize_q8_0_batch_ctx ctx = {
        .x = x,
        .xq = xq,
        .xscale = xscale,
        .in_dim = in_dim,
        .blocks = (in_dim + 31) / 32,
    };
    ds4_parallel_for(n_tok, quantize_q8_0_batch_worker, &ctx);
}

static void matvec_q8_0_worker(void *vctx, uint64_t r0, uint64_t r1) {
    matvec_q8_0_ctx *ctx = vctx;

    for (uint64_t r = r0; r < r1; r++) {
        const uint64_t o = ctx->row0 + r;
        const uint8_t *row = ctx->data + o * ctx->blocks * 34;
        ctx->out[r] = dot_q8_0_row(row, ctx->xq, ctx->xscale, ctx->in_dim, ctx->blocks);
    }
}

static void matvec_q8_0_pair_worker(void *vctx, uint64_t r0, uint64_t r1) {
    matvec_q8_0_pair_ctx *ctx = vctx;

    for (uint64_t r = r0; r < r1; r++) {
        const uint8_t *row0 = ctx->data0 + r * ctx->blocks * 34;
        const uint8_t *row1 = ctx->data1 + r * ctx->blocks * 34;
        dot_q8_0_row_pair(row0, row1, ctx->xq, ctx->xscale, ctx->in_dim, ctx->blocks,
                          ctx->out0 + r, ctx->out1 + r);
    }
}

static void matvec_q8_0_grouped_worker(void *vctx, uint64_t r0, uint64_t r1) {
    matvec_q8_0_grouped_ctx *ctx = vctx;
```<｜Assistant｜><think>


[System] You are DeepSeek V4 Flash running locally. Answer accurately, preserve technical details, and use tools only when the prompt asks for tool use.

[User]
Explain what this code does and identify the inference stage it belongs to.

File: ds4.c

```c
ctx->out0 + r, ctx->out1 + r);
    }
}

static void matvec_q8_0_grouped_worker(void *vctx, uint64_t r0, uint64_t r1) {
    matvec_q8_0_grouped_ctx *ctx = vctx;

    for (uint64_t idx = r0; idx < r1; idx++) {
        const uint64_t group = idx / ctx->rank;
        const uint64_t row_in_group = idx - group * ctx->rank;
        const uint64_t tensor_row = group * ctx->rank + row_in_group;
        const uint8_t *row = ctx->data + tensor_row * ctx->blocks * 34;
        const int8_t *xq = ctx->xq + group * ctx->blocks * 32;
        const float *xscale = ctx->xscale + group * ctx->blocks;
        ctx->out[idx] = dot_q8_0_row(row, xq, xscale, ctx->in_dim, ctx->blocks);
    }
}

static void matmul_q8_0_grouped_batch_worker(void *vctx, uint64_t r0, uint64_t r1) {
    matmul_q8_0_grouped_batch_ctx *ctx = vctx;

    for (uint64_t idx = r0; idx < r1; idx++) {
        const uint64_t group = idx / ctx->rank;
        const uint64_t row_in_group = idx - group * ctx->rank;
        const uint64_t tensor_row = group * ctx->rank + row_in_group;
        const uint8_t *row = ctx->data + tensor_row * ctx->blocks * 34;

        uint64_t t = 0;
        for (; t + 1 < ctx->n_tok; t += 2) {
            const uint64_t xbase0 = (t * ctx->n_groups + group) * ctx->blocks;
            const uint64_t xbase1 = ((t + 1) * ctx->n_groups + group) * ctx->blocks;
            dot_q8_0_row_2(row,
                           ctx->xq + xbase0 * 32,
                           ctx->xscale + xbase0,
                           ctx->xq + xbase1 * 32,
                           ctx->xscale + xbase1,
                           ctx->group_dim,
                           ctx->blocks,
                           ctx->out + t * ctx->n_groups * ctx->rank + group * ctx->rank + row_in_group,
                           ctx->out + (t + 1) * ctx->n_groups * ctx->rank + group * ctx->rank + row_in_group);
        }
        for (; t < ctx->n_tok; t++) {
            const uint64_t xbase = (t * ctx->n_groups + group) * ctx->blocks;
            ctx->out[t * ctx->n_groups * ctx->rank + group * ctx->rank + row_in_group] =
                dot_q8_0_row(row,
                             ctx->xq + xbase * 32,
                             ctx->xscale + xbase,
                             ctx->group_dim,
                             ctx->blocks);
        }
    }
}

static void matmul_q8_0_batch_worker(void *vctx, uint64_t r0, uint64_t r1) {
    matmul_q8_0_batch_ctx *ctx = vctx;

    for (uint64_t r = r0; r < r1; r++) {
        const uint8_t *row = ctx->data + r * ctx->blocks * 34;
        uint64_t t = 0;
```

<｜begin▁of▁sentence｜>You are DeepSeek V4 Flash running locally. Answer accurately, preserve technical details, and use tools only when the prompt asks for tool use.<｜User｜>Explain what this code does and identify the inference stage it belongs to.

File: ds4.c

```c
ctx->out0 + r, ctx->out1 + r);
    }
}

static void matvec_q8_0_grouped_worker(void *vctx, uint64_t r0, uint64_t r1) {
    matvec_q8_0_grouped_ctx *ctx = vctx;

    for (uint64_t idx = r0; idx < r1; idx++) {
        const uint64_t group = idx / ctx->rank;
        const uint64_t row_in_group = idx - group * ctx->rank;
        const uint64_t tensor_row = group * ctx->rank + row_in_group;
        const uint8_t *row = ctx->data + tensor_row * ctx->blocks * 34;
        const int8_t *xq = ctx->xq + group * ctx->blocks * 32;
        const float *xscale = ctx->xscale + group * ctx->blocks;
        ctx->out[idx] = dot_q8_0_row(row, xq, xscale, ctx->in_dim, ctx->blocks);
    }
}

static void matmul_q8_0_grouped_batch_worker(void *vctx, uint64_t r0, uint64_t r1) {
    matmul_q8_0_grouped_batch_ctx *ctx = vctx;

    for (uint64_t idx = r0; idx < r1; idx++) {
        const uint64_t group = idx / ctx->rank;
        const uint64_t row_in_group = idx - group * ctx->rank;
        const uint64_t tensor_row = group * ctx->rank + row_in_group;
        const uint8_t *row = ctx->data + tensor_row * ctx->blocks * 34;

        uint64_t t = 0;
        for (; t + 1 < ctx->n_tok; t += 2) {
            const uint64_t xbase0 = (t * ctx->n_groups + group) * ctx->blocks;
            const uint64_t xbase1 = ((t + 1) * ctx->n_groups + group) * ctx->blocks;
            dot_q8_0_row_2(row,
                           ctx->xq + xbase0 * 32,
                           ctx->xscale + xbase0,
                           ctx->xq + xbase1 * 32,
                           ctx->xscale + xbase1,
                           ctx->group_dim,
                           ctx->blocks,
                           ctx->out + t * ctx->n_groups * ctx->rank + group * ctx->rank + row_in_group,
                           ctx->out + (t + 1) * ctx->n_groups * ctx->rank + group * ctx->rank + row_in_group);
        }
        for (; t < ctx->n_tok; t++) {
            const uint64_t xbase = (t * ctx->n_groups + group) * ctx->blocks;
            ctx->out[t * ctx->n_groups * ctx->rank + group * ctx->rank + row_in_group] =
                dot_q8_0_row(row,
                             ctx->xq + xbase * 32,
                             ctx->xscale + xbase,
                             ctx->group_dim,
                             ctx->blocks);
        }
    }
}

static void matmul_q8_0_batch_worker(void *vctx, uint64_t r0, uint64_t r1) {
    matmul_q8_0_batch_ctx *ctx = vctx;

    for (uint64_t r = r0; r < r1; r++) {
        const uint8_t *row = ctx->data + r * ctx->blocks * 34;
        uint64_t t = 0;
```<｜Assistant｜></think>


[System] You are DeepSeek V4 Flash running locally. Answer accurately, preserve technical details, and use tools only when the prompt asks for tool use.

[User]
Explain what this code does and identify the inference stage it belongs to.

File: ds4.c

```c
ctx->out0 + r, ctx->out1 + r);
    }
}

static void matvec_q8_0_grouped_worker(void *vctx, uint64_t r0, uint64_t r1) {
    matvec_q8_0_grouped_ctx *ctx = vctx;

    for (uint64_t idx = r0; idx < r1; idx++) {
        const uint64_t group = idx / ctx->rank;
        const uint64_t row_in_group = idx - group * ctx->rank;
        const uint64_t tensor_row = group * ctx->rank + row_in_group;
        const uint8_t *row = ctx->data + tensor_row * ctx->blocks * 34;
        const int8_t *xq = ctx->xq + group * ctx->blocks * 32;
        const float *xscale = ctx->xscale + group * ctx->blocks;
        ctx->out[idx] = dot_q8_0_row(row, xq, xscale, ctx->in_dim, ctx->blocks);
    }
}

static void matmul_q8_0_grouped_batch_worker(void *vctx, uint64_t r0, uint64_t r1) {
    matmul_q8_0_grouped_batch_ctx *ctx = vctx;

    for (uint64_t idx = r0; idx < r1; idx++) {
        const uint64_t group = idx / ctx->rank;
        const uint64_t row_in_group = idx - group * ctx->rank;
        const uint64_t tensor_row = group * ctx->rank + row_in_group;
        const uint8_t *row = ctx->data + tensor_row * ctx->blocks * 34;

        uint64_t t = 0;
        for (; t + 1 < ctx->n_tok; t += 2) {
            const uint64_t xbase0 = (t * ctx->n_groups + group) * ctx->blocks;
            const uint64_t xbase1 = ((t + 1) * ctx->n_groups + group) * ctx->blocks;
            dot_q8_0_row_2(row,
                           ctx->xq + xbase0 * 32,
                           ctx->xscale + xbase0,
                           ctx->xq + xbase1 * 32,
                           ctx->xscale + xbase1,
                           ctx->group_dim,
                           ctx->blocks,
                           ctx->out + t * ctx->n_groups * ctx->rank + group * ctx->rank + row_in_group,
                           ctx->out + (t + 1) * ctx->n_groups * ctx->rank + group * ctx->rank + row_in_group);
        }
        for (; t < ctx->n_tok; t++) {
            const uint64_t xbase = (t * ctx->n_groups + group) * ctx->blocks;
            ctx->out[t * ctx->n_groups * ctx->rank + group * ctx->rank + row_in_group] =
                dot_q8_0_row(row,
                             ctx->xq + xbase * 32,
                             ctx->xscale + xbase,
                             ctx->group_dim,
                             ctx->blocks);
        }
    }
}

static void matmul_q8_0_batch_worker(void *vctx, uint64_t r0, uint64_t r1) {
    matmul_q8_0_batch_ctx *ctx = vctx;

    for (uint64_t r = r0; r < r1; r++) {
        const uint8_t *row = ctx->data + r * ctx->blocks * 34;
        uint64_t t = 0;
```

<｜begin▁of▁sentence｜>You are DeepSeek V4 Flash running locally. Answer accurately, preserve technical details, and use tools only when the prompt asks for tool use.<｜User｜>Explain what this code does and identify the inference stage it belongs to.

File: ds4.c

```c
ctx->out0 + r, ctx->out1 + r);
    }
}

static void matvec_q8_0_grouped_worker(void *vctx, uint64_t r0, uint64_t r1) {
    matvec_q8_0_grouped_ctx *ctx = vctx;

    for (uint64_t idx = r0; idx < r1; idx++) {
        const uint64_t group = idx / ctx->rank;
        const uint64_t row_in_group = idx - group * ctx->rank;
        const uint64_t tensor_row = group * ctx->rank + row_in_group;
        const uint8_t *row = ctx->data + tensor_row * ctx->blocks * 34;
        const int8_t *xq = ctx->xq + group * ctx->blocks * 32;
        const float *xscale = ctx->xscale + group * ctx->blocks;
        ctx->out[idx] = dot_q8_0_row(row, xq, xscale, ctx->in_dim, ctx->blocks);
    }
}

static void matmul_q8_0_grouped_batch_worker(void *vctx, uint64_t r0, uint64_t r1) {
    matmul_q8_0_grouped_batch_ctx *ctx = vctx;

    for (uint64_t idx = r0; idx < r1; idx++) {
        const uint64_t group = idx / ctx->rank;
        const uint64_t row_in_group = idx - group * ctx->rank;
        const uint64_t tensor_row = group * ctx->rank + row_in_group;
        const uint8_t *row = ctx->data + tensor_row * ctx->blocks * 34;

        uint64_t t = 0;
        for (; t + 1 < ctx->n_tok; t += 2) {
            const uint64_t xbase0 = (t * ctx->n_groups + group) * ctx->blocks;
            const uint64_t xbase1 = ((t + 1) * ctx->n_groups + group) * ctx->blocks;
            dot_q8_0_row_2(row,
                           ctx->xq + xbase0 * 32,
                           ctx->xscale + xbase0,
                           ctx->xq + xbase1 * 32,
                           ctx->xscale + xbase1,
                           ctx->group_dim,
                           ctx->blocks,
                           ctx->out + t * ctx->n_groups * ctx->rank + group * ctx->rank + row_in_group,
                           ctx->out + (t + 1) * ctx->n_groups * ctx->rank + group * ctx->rank + row_in_group);
        }
        for (; t < ctx->n_tok; t++) {
            const uint64_t xbase = (t * ctx->n_groups + group) * ctx->blocks;
            ctx->out[t * ctx->n_groups * ctx->rank + group * ctx->rank + row_in_group] =
                dot_q8_0_row(row,
                             ctx->xq + xbase * 32,
                             ctx->xscale + xbase,
                             ctx->group_dim,
                             ctx->blocks);
        }
    }
}

static void matmul_q8_0_batch_worker(void *vctx, uint64_t r0, uint64_t r1) {
    matmul_q8_0_batch_ctx *ctx = vctx;

    for (uint64_t r = r0; r < r1; r++) {
        const uint8_t *row = ctx->data + r * ctx->blocks * 34;
        uint64_t t = 0;
```<｜Assistant｜><think>


[System] You are DeepSeek V4 Flash running locally. Answer accurately, preserve technical details, and use tools only when the prompt asks for tool use.

[User]
Suggest a minimal correctness-preserving optimization for this excerpt.

File: ds4.c

```c
nt64_t r1) {
    matmul_q8_0_batch_ctx *ctx = vctx;

    for (uint64_t r = r0; r < r1; r++) {
        const uint8_t *row = ctx->data + r * ctx->blocks * 34;
        uint64_t t = 0;
        for (; t + 1 < ctx->n_tok; t += 2) {
            dot_q8_0_row_2(row,
                           ctx->xq + t * ctx->blocks * 32,
                           ctx->xscale + t * ctx->blocks,
                           ctx->xq + (t + 1) * ctx->blocks * 32,
                           ctx->xscale + (t + 1) * ctx->blocks,
                           ctx->in_dim,
                           ctx->blocks,
                           ctx->out + t * ctx->out_dim + r,
                           ctx->out + (t + 1) * ctx->out_dim + r);
        }
        for (; t < ctx->n_tok; t++) {
            ctx->out[t * ctx->out_dim + r] =
                dot_q8_0_row(row,
                             ctx->xq + t * ctx->blocks * 32,
                             ctx->xscale + t * ctx->blocks,
                             ctx->in_dim,
                             ctx->blocks);
        }
    }
}

static void matmul_q8_0_pair_batch_worker(void *vctx, uint64_t r0, uint64_t r1) {
    matmul_q8_0_pair_batch_ctx *ctx = vctx;

    for (uint64_t r = r0; r < r1; r++) {
        const uint8_t *row0 = ctx->data0 + r * ctx->blocks * 34;
        const uint8_t *row1 = ctx->data1 + r * ctx->blocks * 34;
        uint64_t t = 0;
        for (; t + 1 < ctx->n_tok; t += 2) {
            const int8_t *xq0 = ctx->xq + t * ctx->blocks * 32;
            const float *xscale0 = ctx->xscale + t * ctx->blocks;
            const int8_t *xq1 = ctx->xq + (t + 1) * ctx->blocks * 32;
            const float *xscale1 = ctx->xscale + (t + 1) * ctx->blocks;
            dot_q8_0_row_2(row0, xq0, xscale0, xq1, xscale1, ctx->in_dim, ctx->blocks,
                           ctx->out0 + t * ctx->out_dim + r,
                           ctx->out0 + (t + 1) * ctx->out_dim + r);
            dot_q8_0_row_2(row1, xq0, xscale0, xq1, xscale1, ctx->in_dim, ctx->blocks,
                           ctx->out1 + t * ctx->out_dim + r,
                           ctx->out1 + (t + 1) * ctx->out_dim + r);
        }
        for (; t < ctx->n_tok; t++) {
            const int8_t *xq = ctx->xq + t * ctx->blocks * 32;
            const float *xscale = ctx->xscale + t * ctx->blocks;
            dot_q8_0_row_pair(row0, row1, xq, xscale, ctx->in_dim, ctx->blocks,
                              ctx->out0 + t * ctx->out_dim + r,
                              ctx->out1 + t * ctx->out_dim + r);
        }
    }
}
```

<｜begin▁of▁sentence｜>You are DeepSeek V4 Flash running locally. Answer accurately, preserve technical details, and use tools only when the prompt asks for tool use.<｜User｜>Suggest a minimal correctness-preserving optimization for this excerpt.

File: ds4.c

```c
nt64_t r1) {
    matmul_q8_0_batch_ctx *ctx = vctx;

    for (uint64_t r = r0; r < r1; r++) {
        const uint8_t *row = ctx->data + r * ctx->blocks * 34;
        uint64_t t = 0;
        for (; t + 1 < ctx->n_tok; t += 2) {
            dot_q8_0_row_2(row,
                           ctx->xq + t * ctx->blocks * 32,
                           ctx->xscale + t * ctx->blocks,
                           ctx->xq + (t + 1) * ctx->blocks * 32,
                           ctx->xscale + (t + 1) * ctx->blocks,
                           ctx->in_dim,
                           ctx->blocks,
                           ctx->out + t * ctx->out_dim + r,
                           ctx->out + (t + 1) * ctx->out_dim + r);
        }
        for (; t < ctx->n_tok; t++) {
            ctx->out[t * ctx->out_dim + r] =
                dot_q8_0_row(row,
                             ctx->xq + t * ctx->blocks * 32,
                             ctx->xscale + t * ctx->blocks,
                             ctx->in_dim,
                             ctx->blocks);
        }
    }
}

static void matmul_q8_0_pair_batch_worker(void *vctx, uint64_t r0, uint64_t r1) {
    matmul_q8_0_pair_batch_ctx *ctx = vctx;

    for (uint64_t r = r0; r < r1; r++) {
        const uint8_t *row0 = ctx->data0 + r * ctx->blocks * 34;
        const uint8_t *row1 = ctx->data1 + r * ctx->blocks * 34;
        uint64_t t = 0;
        for (; t + 1 < ctx->n_tok; t += 2) {
            const int8_t *xq0 = ctx->xq + t * ctx->blocks * 32;
            const float *xscale0 = ctx->xscale + t * ctx->blocks;
            const int8_t *xq1 = ctx->xq + (t + 1) * ctx->blocks * 32;
            const float *xscale1 = ctx->xscale + (t + 1) * ctx->blocks;
            dot_q8_0_row_2(row0, xq0, xscale0, xq1, xscale1, ctx->in_dim, ctx->blocks,
                           ctx->out0 + t * ctx->out_dim + r,
                           ctx->out0 + (t + 1) * ctx->out_dim + r);
            dot_q8_0_row_2(row1, xq0, xscale0, xq1, xscale1, ctx->in_dim, ctx->blocks,
                           ctx->out1 + t * ctx->out_dim + r,
                           ctx->out1 + (t + 1) * ctx->out_dim + r);
        }
        for (; t < ctx->n_tok; t++) {
            const int8_t *xq = ctx->xq + t * ctx->blocks * 32;
            const float *xscale = ctx->xscale + t * ctx->blocks;
            dot_q8_0_row_pair(row0, row1, xq, xscale, ctx->in_dim, ctx->blocks,
                              ctx->out0 + t * ctx->out_dim + r,
                              ctx->out1 + t * ctx->out_dim + r);
        }
    }
}
```<｜Assistant｜></think>


[System] You are DeepSeek V4 Flash running locally. Answer accurately, preserve technical details, and use tools only when the prompt asks for tool use.

[User]
Suggest a minimal correctness-preserving optimization for this excerpt.

File: ds4.c

```c
nt64_t r1) {
    matmul_q8_0_batch_ctx *ctx = vctx;

    for (uint64_t r = r0; r < r1; r++) {
        const uint8_t *row = ctx->data + r * ctx->blocks * 34;
        uint64_t t = 0;
        for (; t + 1 < ctx->n_tok; t += 2) {
            dot_q8_0_row_2(row,
                           ctx->xq + t * ctx->blocks * 32,
                           ctx->xscale + t * ctx->blocks,
                           ctx->xq + (t + 1) * ctx->blocks * 32,
                           ctx->xscale + (t + 1) * ctx->blocks,
                           ctx->in_dim,
                           ctx->blocks,
                           ctx->out + t * ctx->out_dim + r,
                           ctx->out + (t + 1) * ctx->out_dim + r);
        }
        for (; t < ctx->n_tok; t++) {
            ctx->out[t * ctx->out_dim + r] =
                dot_q8_0_row(row,
                             ctx->xq + t * ctx->blocks * 32,
                             ctx->xscale + t * ctx->blocks,
                             ctx->in_dim,
                             ctx->blocks);
        }
    }
}

static void matmul_q8_0_pair_batch_worker(void *vctx, uint64_t r0, uint64_t r1) {
    matmul_q8_0_pair_batch_ctx *ctx = vctx;

    for (uint64_t r = r0; r < r1; r++) {
        const uint8_t *row0 = ctx->data0 + r * ctx->blocks * 34;
        const uint8_t *row1 = ctx->data1 + r * ctx->blocks * 34;
        uint64_t t = 0;
        for (; t + 1 < ctx->n_tok; t += 2) {
            const int8_t *xq0 = ctx->xq + t * ctx->blocks * 32;
            const float *xscale0 = ctx->xscale + t * ctx->blocks;
            const int8_t *xq1 = ctx->xq + (t + 1) * ctx->blocks * 32;
            const float *xscale1 = ctx->xscale + (t + 1) * ctx->blocks;
            dot_q8_0_row_2(row0, xq0, xscale0, xq1, xscale1, ctx->in_dim, ctx->blocks,
                           ctx->out0 + t * ctx->out_dim + r,
                           ctx->out0 + (t + 1) * ctx->out_dim + r);
            dot_q8_0_row_2(row1, xq0, xscale0, xq1, xscale1, ctx->in_dim, ctx->blocks,
                           ctx->out1 + t * ctx->out_dim + r,
                           ctx->out1 + (t + 1) * ctx->out_dim + r);
        }
        for (; t < ctx->n_tok; t++) {
            const int8_t *xq = ctx->xq + t * ctx->blocks * 32;
            const float *xscale = ctx->xscale + t * ctx->blocks;
            dot_q8_0_row_pair(row0, row1, xq, xscale, ctx->in_dim, ctx->blocks,
                              ctx->out0 + t * ctx->out_dim + r,
                              ctx->out1 + t * ctx->out_dim + r);
        }
    }
}
```

<｜begin▁of▁sentence｜>You are DeepSeek V4 Flash running locally. Answer accurately, preserve technical details, and use tools only when the prompt asks for tool use.<｜User｜>Suggest a minimal correctness-preserving optimization for this excerpt.

File: ds4.c

```c
nt64_t r1) {
    matmul_q8_0_batch_ctx *ctx = vctx;

    for (uint64_t r = r0; r < r1; r++) {
        const uint8_t *row = ctx->data + r * ctx->blocks * 34;
        uint64_t t = 0;
        for (; t + 1 < ctx->n_tok; t += 2) {
            dot_q8_0_row_2(row,
                           ctx->xq + t * ctx->blocks * 32,
                           ctx->xscale + t * ctx->blocks,
                           ctx->xq + (t + 1) * ctx->blocks * 32,
                           ctx->xscale + (t + 1) * ctx->blocks,
                           ctx->in_dim,
                           ctx->blocks,
                           ctx->out + t * ctx->out_dim + r,
                           ctx->out + (t + 1) * ctx->out_dim + r);
        }
        for (; t < ctx->n_tok; t++) {
            ctx->out[t * ctx->out_dim + r] =
                dot_q8_0_row(row,
                             ctx->xq + t * ctx->blocks * 32,
                             ctx->xscale + t * ctx->blocks,
                             ctx->in_dim,
                             ctx->blocks);
        }
    }
}

static void matmul_q8_0_pair_batch_worker(void *vctx, uint64_t r0, uint64_t r1) {
    matmul_q8_0_pair_batch_ctx *ctx = vctx;

    for (uint64_t r = r0; r < r1; r++) {
        const uint8_t *row0 = ctx->data0 + r * ctx->blocks * 34;
        const uint8_t *row1 = ctx->data1 + r * ctx->blocks * 34;
        uint64_t t = 0;
        for (; t + 1 < ctx->n_tok; t += 2) {
            const int8_t *xq0 = ctx->xq + t * ctx->blocks * 32;
            const float *xscale0 = ctx->xscale + t * ctx->blocks;
            const int8_t *xq1 = ctx->xq + (t + 1) * ctx->blocks * 32;
            const float *xscale1 = ctx->xscale + (t + 1) * ctx->blocks;
            dot_q8_0_row_2(row0, xq0, xscale0, xq1, xscale1, ctx->in_dim, ctx->blocks,
                           ctx->out0 + t * ctx->out_dim + r,
                           ctx->out0 + (t + 1) * ctx->out_dim + r);
            dot_q8_0_row_2(row1, xq0, xscale0, xq1, xscale1, ctx->in_dim, ctx->blocks,
                           ctx->out1 + t * ctx->out_dim + r,
                           ctx->out1 + (t + 1) * ctx->out_dim + r);
        }
        for (; t < ctx->n_tok; t++) {
            const int8_t *xq = ctx->xq + t * ctx->blocks * 32;
            const float *xscale = ctx->xscale + t * ctx->blocks;
            dot_q8_0_row_pair(row0, row1, xq, xscale, ctx->in_dim, ctx->blocks,
                              ctx->out0 + t * ctx->out_dim + r,
                              ctx->out1 + t * ctx->out_dim + r);
        }
    }
}
```<｜Assistant｜><think>


[System] You are DeepSeek V4 Flash running locally. Answer accurately, preserve technical details, and use tools only when the prompt asks for tool use.

[User]
Trova bug sottili e spiega quali invarianti dell'inferenza possono rompersi.

File: ds4.c

```c
scale, ctx->in_dim, ctx->blocks,
                              ctx->out0 + t * ctx->out_dim + r,
                              ctx->out1 + t * ctx->out_dim + r);
        }
    }
}

/* Multiply selected Q8_0 rows by an activation that has already been quantized
 * once.  This avoids repeated activation quantization for paired projections. */
static void matvec_q8_0_rows_prequant(
        float           * out,
        const ds4_model * m,
        const ds4_tensor * w,
        const int8_t    * xq,
        const float     * xscale,
        uint64_t          row0,
        uint64_t          n_rows) {
    if (w->type != 8 || w->ndim != 2) ds4_die("expected a 2D Q8_0 tensor");

    const uint64_t in_dim = w->dim[0];
    const uint64_t out_dim = w->dim[1];
    if (row0 > out_dim || n_rows > out_dim - row0) ds4_die("Q8_0 row range is outside tensor");
    const uint64_t ctx_blocks = (in_dim + 31) / 32;

    matvec_q8_0_ctx ctx = {
        .out = out,
        .data = tensor_data(m, w),
        .xq = xq,
        .xscale = xscale,
        .in_dim = in_dim,
        .row0 = row0,
        .blocks = ctx_blocks,
    };
    ds4_parallel_for(n_rows, matvec_q8_0_worker, &ctx);
}

static DS4_MAYBE_UNUSED void matvec_q8_0_prequant(
        float           * out,
        const ds4_model * m,
        const ds4_tensor * w,
        const int8_t    * xq,
        const float     * xscale) {
    matvec_q8_0_rows_prequant(out, m, w, xq, xscale, 0, w->dim[1]);
}

/* Compute two Q8_0 projections from the same input, used by gate/up and
 * compressor kv/score pairs. */
static void matvec_q8_0_pair_prequant(
        float           * out0,
        float           * out1,
        const ds4_model * m,
        const ds4_tensor * w0,
        const ds4_tensor * w1,
        const int8_t    * xq,
        const float     * xscale) {
    if (w0->type != 8 || w1->type != 8 || w0->ndim != 2 || w1->ndim != 2) {
        ds4_die("expected two 2D Q8_0 tensors");
    }
    if (w0->dim[0] != w1->dim[0] || w0->dim[1] != w1->dim[1]) {
        ds4_die("paired Q8_0 tensors do not have the same shape");
    }

    const uint64_t in_dim = w0->dim[0];
    matvec_q8_0_pair_ctx ctx = {
        .out0 = out0,
        .out1 = out1,
        .data0 = tensor_data(m, w0),
        .data1 = tensor_data(m, w1),
        .xq = xq,
        .xscale = xscale,
        .in_dim = in_dim,
        .blocks = (in_dim + 31) / 32,
    };
    ds4_parallel_for(w0->dim[1], matvec_q8_0_pair_worker, &ctx);
}

static void matmul_q8_0_batch_prequant(
        float           * out,
        const ds4_model * m,
        const ds4_tensor * w,
```

<｜begin▁of▁sentence｜>You are DeepSeek V4 Flash running locally. Answer accurately, preserve technical details, and use tools only when the prompt asks for tool use.<｜User｜>Trova bug sottili e spiega quali invarianti dell'inferenza possono rompersi.

File: ds4.c

```c
scale, ctx->in_dim, ctx->blocks,
                              ctx->out0 + t * ctx->out_dim + r,
                              ctx->out1 + t * ctx->out_dim + r);
        }
    }
}

/* Multiply selected Q8_0 rows by an activation that has already been quantized
 * once.  This avoids repeated activation quantization for paired projections. */
static void matvec_q8_0_rows_prequant(
        float           * out,
        const ds4_model * m,
        const ds4_tensor * w,
        const int8_t    * xq,
        const float     * xscale,
        uint64_t          row0,
        uint64_t          n_rows) {
    if (w->type != 8 || w->ndim != 2) ds4_die("expected a 2D Q8_0 tensor");

    const uint64_t in_dim = w->dim[0];
    const uint64_t out_dim = w->dim[1];
    if (row0 > out_dim || n_rows > out_dim - row0) ds4_die("Q8_0 row range is outside tensor");
    const uint64_t ctx_blocks = (in_dim + 31) / 32;

    matvec_q8_0_ctx ctx = {
        .out = out,
        .data = tensor_data(m, w),
        .xq = xq,
        .xscale = xscale,
        .in_dim = in_dim,
        .row0 = row0,
        .blocks = ctx_blocks,
    };
    ds4_parallel_for(n_rows, matvec_q8_0_worker, &ctx);
}

static DS4_MAYBE_UNUSED void matvec_q8_0_prequant(
        float           * out,
        const ds4_model * m,
        const ds4_tensor * w,
        const int8_t    * xq,
        const float     * xscale) {
    matvec_q8_0_rows_prequant(out, m, w, xq, xscale, 0, w->dim[1]);
}

/* Compute two Q8_0 projections from the same input, used by gate/up and
 * compressor kv/score pairs. */
static void matvec_q8_0_pair_prequant(
        float           * out0,
        float           * out1,
        const ds4_model * m,
        const ds4_tensor * w0,
        const ds4_tensor * w1,
        const int8_t    * xq,
        const float     * xscale) {
    if (w0->type != 8 || w1->type != 8 || w0->ndim != 2 || w1->ndim != 2) {
        ds4_die("expected two 2D Q8_0 tensors");
    }
    if (w0->dim[0] != w1->dim[0] || w0->dim[1] != w1->dim[1]) {
        ds4_die("paired Q8_0 tensors do not have the same shape");
    }

    const uint64_t in_dim = w0->dim[0];
    matvec_q8_0_pair_ctx ctx = {
        .out0 = out0,
        .out1 = out1,
        .data0 = tensor_data(m, w0),
        .data1 = tensor_data(m, w1),
        .xq = xq,
        .xscale = xscale,
        .in_dim = in_dim,
        .blocks = (in_dim + 31) / 32,
    };
    ds4_parallel_for(w0->dim[1], matvec_q8_0_pair_worker, &ctx);
}

static void matmul_q8_0_batch_prequant(
        float           * out,
        const ds4_model * m,
        const ds4_tensor * w,
```<｜Assistant｜></think>


[System] You are DeepSeek V4 Flash running locally. Answer accurately, preserve technical details, and use tools only when the prompt asks for tool use.

[User]
Trova bug sottili e spiega quali invarianti dell'inferenza possono rompersi.

File: ds4.c

```c
scale, ctx->in_dim, ctx->blocks,
                              ctx->out0 + t * ctx->out_dim + r,
                              ctx->out1 + t * ctx->out_dim + r);
        }
    }
}

/* Multiply selected Q8_0 rows by an activation that has already been quantized
 * once.  This avoids repeated activation quantization for paired projections. */
static void matvec_q8_0_rows_prequant(
        float           * out,
        const ds4_model * m,
        const ds4_tensor * w,
        const int8_t    * xq,
        const float     * xscale,
        uint64_t          row0,
        uint64_t          n_rows) {
    if (w->type != 8 || w->ndim != 2) ds4_die("expected a 2D Q8_0 tensor");

    const uint64_t in_dim = w->dim[0];
    const uint64_t out_dim = w->dim[1];
    if (row0 > out_dim || n_rows > out_dim - row0) ds4_die("Q8_0 row range is outside tensor");
    const uint64_t ctx_blocks = (in_dim + 31) / 32;

    matvec_q8_0_ctx ctx = {
        .out = out,
        .data = tensor_data(m, w),
        .xq = xq,
        .xscale = xscale,
        .in_dim = in_dim,
        .row0 = row0,
        .blocks = ctx_blocks,
    };
    ds4_parallel_for(n_rows, matvec_q8_0_worker, &ctx);
}

static DS4_MAYBE_UNUSED void matvec_q8_0_prequant(
        float           * out,
        const ds4_model * m,
        const ds4_tensor * w,
        const int8_t    * xq,
        const float     * xscale) {
    matvec_q8_0_rows_prequant(out, m, w, xq, xscale, 0, w->dim[1]);
}

/* Compute two Q8_0 projections from the same input, used by gate/up and
 * compressor kv/score pairs. */
static void matvec_q8_0_pair_prequant(
        float           * out0,
        float           * out1,
        const ds4_model * m,
        const ds4_tensor * w0,
        const ds4_tensor * w1,
        const int8_t    * xq,
        const float     * xscale) {
    if (w0->type != 8 || w1->type != 8 || w0->ndim != 2 || w1->ndim != 2) {
        ds4_die("expected two 2D Q8_0 tensors");
    }
    if (w0->dim[0] != w1->dim[0] || w0->dim[1] != w1->dim[1]) {
        ds4_die("paired Q8_0 tensors do not have the same shape");
    }

    const uint64_t in_dim = w0->dim[0];
    matvec_q8_0_pair_ctx ctx = {
        .out0 = out0,
        .out1 = out1,
        .data0 = tensor_data(m, w0),
        .data1 = tensor_data(m, w1),
        .xq = xq,
        .xscale = xscale,
        .in_dim = in_dim,
        .blocks = (in_dim + 31) / 32,
    };
    ds4_parallel_for(w0->dim[1], matvec_q8_0_pair_worker, &ctx);
}

static void matmul_q8_0_batch_prequant(
        float           * out,
        const ds4_model * m,
        const ds4_tensor * w,
```

<｜begin▁of▁sentence｜>You are DeepSeek V4 Flash running locally. Answer accurately, preserve technical details, and use tools only when the prompt asks for tool use.<｜User｜>Trova bug sottili e spiega quali invarianti dell'inferenza possono rompersi.

File: ds4.c

```c
scale, ctx->in_dim, ctx->blocks,
                              ctx->out0 + t * ctx->out_dim + r,
                              ctx->out1 + t * ctx->out_dim + r);
        }
    }
}

/* Multiply selected Q8_0 rows by an activation that has already been quantized
 * once.  This avoids repeated activation quantization for paired projections. */
static void matvec_q8_0_rows_prequant(
        float           * out,
        const ds4_model * m,
        const ds4_tensor * w,
        const int8_t    * xq,
        const float     * xscale,
        uint64_t          row0,
        uint64_t          n_rows) {
    if (w->type != 8 || w->ndim != 2) ds4_die("expected a 2D Q8_0 tensor");

    const uint64_t in_dim = w->dim[0];
    const uint64_t out_dim = w->dim[1];
    if (row0 > out_dim || n_rows > out_dim - row0) ds4_die("Q8_0 row range is outside tensor");
    const uint64_t ctx_blocks = (in_dim + 31) / 32;

    matvec_q8_0_ctx ctx = {
        .out = out,
        .data = tensor_data(m, w),
        .xq = xq,
        .xscale = xscale,
        .in_dim = in_dim,
        .row0 = row0,
        .blocks = ctx_blocks,
    };
    ds4_parallel_for(n_rows, matvec_q8_0_worker, &ctx);
}

static DS4_MAYBE_UNUSED void matvec_q8_0_prequant(
        float           * out,
        const ds4_model * m,
        const ds4_tensor * w,
        const int8_t    * xq,
        const float     * xscale) {
    matvec_q8_0_rows_prequant(out, m, w, xq, xscale, 0, w->dim[1]);
}

/* Compute two Q8_0 projections from the same input, used by gate/up and
 * compressor kv/score pairs. */
static void matvec_q8_0_pair_prequant(
        float           * out0,
        float           * out1,
        const ds4_model * m,
        const ds4_tensor * w0,
        const ds4_tensor * w1,
        const int8_t    * xq,
        const float     * xscale) {
    if (w0->type != 8 || w1->type != 8 || w0->ndim != 2 || w1->ndim != 2) {
        ds4_die("expected two 2D Q8_0 tensors");
    }
    if (w0->dim[0] != w1->dim[0] || w0->dim[1] != w1->dim[1]) {
        ds4_die("paired Q8_0 tensors do not have the same shape");
    }

    const uint64_t in_dim = w0->dim[0];
    matvec_q8_0_pair_ctx ctx = {
        .out0 = out0,
        .out1 = out1,
        .data0 = tensor_data(m, w0),
        .data1 = tensor_data(m, w1),
        .xq = xq,
        .xscale = xscale,
        .in_dim = in_dim,
        .blocks = (in_dim + 31) / 32,
    };
    ds4_parallel_for(w0->dim[1], matvec_q8_0_pair_worker, &ctx);
}

static void matmul_q8_0_batch_prequant(
        float           * out,
        const ds4_model * m,
        const ds4_tensor * w,
```<｜Assistant｜><think>


[System] You are DeepSeek V4 Flash running locally. Answer accurately, preserve technical details, and use tools only when the prompt asks for tool use.

[User]
Review this excerpt for correctness risks, memory lifetime issues, and performance bottlenecks.

File: ds4.c

```c
for(w0->dim[1], matvec_q8_0_pair_worker, &ctx);
}

static void matmul_q8_0_batch_prequant(
        float           * out,
        const ds4_model * m,
        const ds4_tensor * w,
        const int8_t    * xq,
        const float     * xscale,
        uint64_t          n_tok) {
    if (w->type != 8 || w->ndim != 2) ds4_die("expected a 2D Q8_0 tensor");

    matmul_q8_0_batch_ctx ctx = {
        .out = out,
        .data = tensor_data(m, w),
        .xq = xq,
        .xscale = xscale,
        .n_tok = n_tok,
        .in_dim = w->dim[0],
        .out_dim = w->dim[1],
        .blocks = (w->dim[0] + 31) / 32,
    };
    ds4_parallel_for(ctx.out_dim, matmul_q8_0_batch_worker, &ctx);
}

static void matmul_q8_0_pair_batch_prequant(
        float           * out0,
        float           * out1,
        const ds4_model * m,
        const ds4_tensor * w0,
        const ds4_tensor * w1,
        const int8_t    * xq,
        const float     * xscale,
        uint64_t          n_tok) {
    if (w0->type != 8 || w1->type != 8 || w0->ndim != 2 || w1->ndim != 2) {
        ds4_die("expected two 2D Q8_0 tensors");
    }
    if (w0->dim[0] != w1->dim[0] || w0->dim[1] != w1->dim[1]) {
        ds4_die("paired Q8_0 tensors do not have the same shape");
    }

    matmul_q8_0_pair_batch_ctx ctx = {
        .out0 = out0,
        .out1 = out1,
        .data0 = tensor_data(m, w0),
        .data1 = tensor_data(m, w1),
        .xq = xq,
        .xscale = xscale,
        .n_tok = n_tok,
        .in_dim = w0->dim[0],
        .out_dim = w0->dim[1],
        .blocks = (w0->dim[0] + 31) / 32,
    };
    ds4_parallel_for(ctx.out_dim, matmul_q8_0_pair_batch_worker, &ctx);
}

/* Batched Q8_0 matmul for prefill: quantize all token activations, then scan
 * weight rows once per output channel. */
static void matmul_q8_0_batch(
        float           * out,
        const ds4_model * m,
        const ds4_tensor * w,
        const float     * x,
        uint64_t          n_tok) {
    if (w->type != 8 || w->ndim != 2) ds4_die("expected a 2D Q8_0 tensor");

    const uint64_t in_dim = w->dim[0];
    const uint64_t blocks = (in_dim + 31) / 32;
    int8_t *xq = xmalloc((size_t)n_tok * blocks * 32);
    float *xscale = xmalloc((size_t)n_tok * blocks * sizeof(xscale[0]));

    quantize_q8_0_activation_batch(x, xq, xscale, n_tok, in_dim);
    matmul_q8_0_batch_prequant(out, m, w, xq, xscale, n_tok);

    free(xscale);
    free(xq);
}

static void matmul_q8_0_pair_batch(
        float           * out0,
        float           * out1,
        const ds4_model * m,
        const ds4_tensor * w0,
```

<｜begin▁of▁sentence｜>You are DeepSeek V4 Flash running locally. Answer accurately, preserve technical details, and use tools only when the prompt asks for tool use.<｜User｜>Review this excerpt for correctness risks, memory lifetime issues, and performance bottlenecks.

File: ds4.c

```c
for(w0->dim[1], matvec_q8_0_pair_worker, &ctx);
}

static void matmul_q8_0_batch_prequant(
        float           * out,
        const ds4_model * m,
        const ds4_tensor * w,
        const int8_t    * xq,
        const float     * xscale,
        uint64_t          n_tok) {
    if (w->type != 8 || w->ndim != 2) ds4_die("expected a 2D Q8_0 tensor");

    matmul_q8_0_batch_ctx ctx = {
        .out = out,
        .data = tensor_data(m, w),
        .xq = xq,
        .xscale = xscale,
        .n_tok = n_tok,
        .in_dim = w->dim[0],
        .out_dim = w->dim[1],
        .blocks = (w->dim[0] + 31) / 32,
    };
    ds4_parallel_for(ctx.out_dim, matmul_q8_0_batch_worker, &ctx);
}

static void matmul_q8_0_pair_batch_prequant(
        float           * out0,
        float           * out1,
        const ds4_model * m,
        const ds4_tensor * w0,
        const ds4_tensor * w1,
        const int8_t    * xq,
        const float     * xscale,
        uint64_t          n_tok) {
    if (w0->type != 8 || w1->type != 8 || w0->ndim != 2 || w1->ndim != 2) {
        ds4_die("expected two 2D Q8_0 tensors");
    }
    if (w0->dim[0] != w1->dim[0] || w0->dim[1] != w1->dim[1]) {
        ds4_die("paired Q8_0 tensors do not have the same shape");
    }

    matmul_q8_0_pair_batch_ctx ctx = {
        .out0 = out0,
        .out1 = out1,
        .data0 = tensor_data(m, w0),
        .data1 = tensor_data(m, w1),
        .xq = xq,
        .xscale = xscale,
        .n_tok = n_tok,
        .in_dim = w0->dim[0],
        .out_dim = w0->dim[1],
        .blocks = (w0->dim[0] + 31) / 32,
    };
    ds4_parallel_for(ctx.out_dim, matmul_q8_0_pair_batch_worker, &ctx);
}

/* Batched Q8_0 matmul for prefill: quantize all token activations, then scan
 * weight rows once per output channel. */
static void matmul_q8_0_batch(
        float           * out,
        const ds4_model * m,
        const ds4_tensor * w,
        const float     * x,
        uint64_t          n_tok) {
    if (w->type != 8 || w->ndim != 2) ds4_die("expected a 2D Q8_0 tensor");

    const uint64_t in_dim = w->dim[0];
    const uint64_t blocks = (in_dim + 31) / 32;
    int8_t *xq = xmalloc((size_t)n_tok * blocks * 32);
    float *xscale = xmalloc((size_t)n_tok * blocks * sizeof(xscale[0]));

    quantize_q8_0_activation_batch(x, xq, xscale, n_tok, in_dim);
    matmul_q8_0_batch_prequant(out, m, w, xq, xscale, n_tok);

    free(xscale);
    free(xq);
}

static void matmul_q8_0_pair_batch(
        float           * out0,
        float           * out1,
        const ds4_model * m,
        const ds4_tensor * w0,
```<｜Assistant｜></think>


[System] You are DeepSeek V4 Flash running locally. Answer accurately, preserve technical details, and use tools only when the prompt asks for tool use.

[User]
Review this excerpt for correctness risks, memory lifetime issues, and performance bottlenecks.

File: ds4.c

```c
for(w0->dim[1], matvec_q8_0_pair_worker, &ctx);
}

static void matmul_q8_0_batch_prequant(
        float           * out,
        const ds4_model * m,
        const ds4_tensor * w,
        const int8_t    * xq,
        const float     * xscale,
        uint64_t          n_tok) {
    if (w->type != 8 || w->ndim != 2) ds4_die("expected a 2D Q8_0 tensor");

    matmul_q8_0_batch_ctx ctx = {
        .out = out,
        .data = tensor_data(m, w),
        .xq = xq,
        .xscale = xscale,
        .n_tok = n_tok,
        .in_dim = w->dim[0],
        .out_dim = w->dim[1],
        .blocks = (w->dim[0] + 31) / 32,
    };
    ds4_parallel_for(ctx.out_dim, matmul_q8_0_batch_worker, &ctx);
}

static void matmul_q8_0_pair_batch_prequant(
        float           * out0,
        float           * out1,
        const ds4_model * m,
        const ds4_tensor * w0,
        const ds4_tensor * w1,
        const int8_t    * xq,
        const float     * xscale,
        uint64_t          n_tok) {
    if (w0->type != 8 || w1->type != 8 || w0->ndim != 2 || w1->ndim != 2) {
        ds4_die("expected two 2D Q8_0 tensors");
    }
    if (w0->dim[0] != w1->dim[0] || w0->dim[1] != w1->dim[1]) {
        ds4_die("paired Q8_0 tensors do not have the same shape");
    }

    matmul_q8_0_pair_batch_ctx ctx = {
        .out0 = out0,
        .out1 = out1,
        .data0 = tensor_data(m, w0),
        .data1 = tensor_data(m, w1),
        .xq = xq,
        .xscale = xscale,
        .n_tok = n_tok,
        .in_dim = w0->dim[0],
        .out_dim = w0->dim[1],
        .blocks = (w0->dim[0] + 31) / 32,
    };
    ds4_parallel_for(ctx.out_dim, matmul_q8_0_pair_batch_worker, &ctx);
}

/* Batched Q8_0 matmul for prefill: quantize all token activations, then scan
 * weight rows once per output channel. */
static void matmul_q8_0_batch(
        float           * out,
        const ds4_model * m,
        const ds4_tensor * w,
        const float     * x,
        uint64_t          n_tok) {
    if (w->type != 8 || w->ndim != 2) ds4_die("expected a 2D Q8_0 tensor");

    const uint64_t in_dim = w->dim[0];
    const uint64_t blocks = (in_dim + 31) / 32;
    int8_t *xq = xmalloc((size_t)n_tok * blocks * 32);
    float *xscale = xmalloc((size_t)n_tok * blocks * sizeof(xscale[0]));

    quantize_q8_0_activation_batch(x, xq, xscale, n_tok, in_dim);
    matmul_q8_0_batch_prequant(out, m, w, xq, xscale, n_tok);

    free(xscale);
    free(xq);
}

static void matmul_q8_0_pair_batch(
        float           * out0,
        float           * out1,
        const ds4_model * m,
        const ds4_tensor * w0,
```

<｜begin▁of▁sentence｜>You are DeepSeek V4 Flash running locally. Answer accurately, preserve technical details, and use tools only when the prompt asks for tool use.<｜User｜>Review this excerpt for correctness risks, memory lifetime issues, and performance bottlenecks.

File: ds4.c

```c
for(w0->dim[1], matvec_q8_0_pair_worker, &ctx);
}

static void matmul_q8_0_batch_prequant(
        float           * out,
        const ds4_model * m,
        const ds4_tensor * w,
        const int8_t    * xq,
        const float     * xscale,
        uint64_t          n_tok) {
    if (w->type != 8 || w->ndim != 2) ds4_die("expected a 2D Q8_0 tensor");

    matmul_q8_0_batch_ctx ctx = {
        .out = out,
        .data = tensor_data(m, w),
        .xq = xq,
        .xscale = xscale,
        .n_tok = n_tok,
        .in_dim = w->dim[0],
        .out_dim = w->dim[1],
        .blocks = (w->dim[0] + 31) / 32,
    };
    ds4_parallel_for(ctx.out_dim, matmul_q8_0_batch_worker, &ctx);
}

static void matmul_q8_0_pair_batch_prequant(
        float           * out0,
        float           * out1,
        const ds4_model * m,
        const ds4_tensor * w0,
        const ds4_tensor * w1,
        const int8_t    * xq,
        const float     * xscale,
        uint64_t          n_tok) {
    if (w0->type != 8 || w1->type != 8 || w0->ndim != 2 || w1->ndim != 2) {
        ds4_die("expected two 2D Q8_0 tensors");
    }
    if (w0->dim[0] != w1->dim[0] || w0->dim[1] != w1->dim[1]) {
        ds4_die("paired Q8_0 tensors do not have the same shape");
    }

    matmul_q8_0_pair_batch_ctx ctx = {
        .out0 = out0,
        .out1 = out1,
        .data0 = tensor_data(m, w0),
        .data1 = tensor_data(m, w1),
        .xq = xq,
        .xscale = xscale,
        .n_tok = n_tok,
        .in_dim = w0->dim[0],
        .out_dim = w0->dim[1],
        .blocks = (w0->dim[0] + 31) / 32,
    };
    ds4_parallel_for(ctx.out_dim, matmul_q8_0_pair_batch_worker, &ctx);
}

/* Batched Q8_0 matmul for prefill: quantize all token activations, then scan
 * weight rows once per output channel. */
static void matmul_q8_0_batch(
        float           * out,
        const ds4_model * m,
        const ds4_tensor * w,
        const float     * x,
        uint64_t          n_tok) {
    if (w->type != 8 || w->ndim != 2) ds4_die("expected a 2D Q8_0 tensor");

    const uint64_t in_dim = w->dim[0];
    const uint64_t blocks = (in_dim + 31) / 32;
    int8_t *xq = xmalloc((size_t)n_tok * blocks * 32);
    float *xscale = xmalloc((size_t)n_tok * blocks * sizeof(xscale[0]));

    quantize_q8_0_activation_batch(x, xq, xscale, n_tok, in_dim);
    matmul_q8_0_batch_prequant(out, m, w, xq, xscale, n_tok);

    free(xscale);
    free(xq);
}

static void matmul_q8_0_pair_batch(
        float           * out0,
        float           * out1,
        const ds4_model * m,
        const ds4_tensor * w0,
```<｜Assistant｜><think>


[System] You are DeepSeek V4 Flash running locally. Answer accurately, preserve technical details, and use tools only when the prompt asks for tool use.

[User]
Explain what this code does and identify the inference stage it belongs to.

File: ds4.c

```c
e);
    free(xq);
}

static void matmul_q8_0_pair_batch(
        float           * out0,
        float           * out1,
        const ds4_model * m,
        const ds4_tensor * w0,
        const ds4_tensor * w1,
        const float     * x,
        uint64_t          n_tok) {
    if (w0->type != 8 || w1->type != 8 || w0->ndim != 2 || w1->ndim != 2) {
        ds4_die("expected two 2D Q8_0 tensors");
    }
    if (w0->dim[0] != w1->dim[0] || w0->dim[1] != w1->dim[1]) {
        ds4_die("paired Q8_0 tensors do not have the same shape");
    }

    const uint64_t in_dim = w0->dim[0];
    const uint64_t blocks = (in_dim + 31) / 32;
    int8_t *xq = xmalloc((size_t)n_tok * blocks * 32);
    float *xscale = xmalloc((size_t)n_tok * blocks * sizeof(xscale[0]));

    quantize_q8_0_activation_batch(x, xq, xscale, n_tok, in_dim);
    matmul_q8_0_pair_batch_prequant(out0, out1, m, w0, w1, xq, xscale, n_tok);

    free(xscale);
    free(xq);
}

static void matvec_q8_0_rows(
        float           * out,
        const ds4_model * m,
        const ds4_tensor * w,
        const float     * x,
        uint64_t          row0,
        uint64_t          n_rows) {
    if (w->type != 8 || w->ndim != 2) ds4_die("expected a 2D Q8_0 tensor");

    const uint64_t in_dim = w->dim[0];
    const uint64_t ctx_blocks = (in_dim + 31) / 32;
    int8_t *xq = xmalloc((size_t)ctx_blocks * 32);
    float *xscale = xmalloc((size_t)ctx_blocks * sizeof(xscale[0]));

    quantize_q8_0_activation(x, xq, xscale, in_dim);
    matvec_q8_0_rows_prequant(out, m, w, xq, xscale, row0, n_rows);

    free(xscale);
    free(xq);
}

/* Single-token Q8_0 matvec, used heavily in decode. */
static void matvec_q8_0(float *out, const ds4_model *m, const ds4_tensor *w, const float *x) {
    matvec_q8_0_rows(out, m, w, x, 0, w->dim[1]);
}

static void matvec_any(float *out, const ds4_model *m, const ds4_tensor *w, const float *x);

/* Decode scratch owns this temporary activation quantization so generation
 * can assert that the hot path performs no malloc. */
static void cpu_decode_quantize_q8_0(
        ds4_cpu_decode_scratch * scratch,
        const float            * x,
        uint64_t                 in_dim) {
    if (in_dim > scratch->q8_cap) ds4_die("CPU decode Q8_0 scratch buffer is too small");
    quantize_q8_0_activation(x, scratch->q8_xq, scratch->q8_xscale, in_dim);
}

static void matvec_q8_0_decode_scratch(
        float                  * out,
        const ds4_model        * m,
        const ds4_tensor       * w,
        const float            * x,
        ds4_cpu_decode_scratch * scratch) {
```

<｜begin▁of▁sentence｜>You are DeepSeek V4 Flash running locally. Answer accurately, preserve technical details, and use tools only when the prompt asks for tool use.<｜User｜>Explain what this code does and identify the inference stage it belongs to.

File: ds4.c

```c
e);
    free(xq);
}

static void matmul_q8_0_pair_batch(
        float           * out0,
        float           * out1,
        const ds4_model * m,
        const ds4_tensor * w0,
        const ds4_tensor * w1,
        const float     * x,
        uint64_t          n_tok) {
    if (w0->type != 8 || w1->type != 8 || w0->ndim != 2 || w1->ndim != 2) {
        ds4_die("expected two 2D Q8_0 tensors");
    }
    if (w0->dim[0] != w1->dim[0] || w0->dim[1] != w1->dim[1]) {
        ds4_die("paired Q8_0 tensors do not have the same shape");
    }

    const uint64_t in_dim = w0->dim[0];
    const uint64_t blocks = (in_dim + 31) / 32;
    int8_t *xq = xmalloc((size_t)n_tok * blocks * 32);
    float *xscale = xmalloc((size_t)n_tok * blocks * sizeof(xscale[0]));

    quantize_q8_0_activation_batch(x, xq, xscale, n_tok, in_dim);
    matmul_q8_0_pair_batch_prequant(out0, out1, m, w0, w1, xq, xscale, n_tok);

    free(xscale);
    free(xq);
}

static void matvec_q8_0_rows(
        float           * out,
        const ds4_model * m,
        const ds4_tensor * w,
        const float     * x,
        uint64_t          row0,
        uint64_t          n_rows) {
    if (w->type != 8 || w->ndim != 2) ds4_die("expected a 2D Q8_0 tensor");

    const uint64_t in_dim = w->dim[0];
    const uint64_t ctx_blocks = (in_dim + 31) / 32;
    int8_t *xq = xmalloc((size_t)ctx_blocks * 32);
    float *xscale = xmalloc((size_t)ctx_blocks * sizeof(xscale[0]));

    quantize_q8_0_activation(x, xq, xscale, in_dim);
    matvec_q8_0_rows_prequant(out, m, w, xq, xscale, row0, n_rows);

    free(xscale);
    free(xq);
}

/* Single-token Q8_0 matvec, used heavily in decode. */
static void matvec_q8_0(float *out, const ds4_model *m, const ds4_tensor *w, const float *x) {
    matvec_q8_0_rows(out, m, w, x, 0, w->dim[1]);
}

static void matvec_any(float *out, const ds4_model *m, const ds4_tensor *w, const float *x);

/* Decode scratch owns this temporary activation quantization so generation
 * can assert that the hot path performs no malloc. */
static void cpu_decode_quantize_q8_0(
        ds4_cpu_decode_scratch * scratch,
        const float            * x,
        uint64_t                 in_dim) {
    if (in_dim > scratch->q8_cap) ds4_die("CPU decode Q8_0 scratch buffer is too small");
    quantize_q8_0_activation(x, scratch->q8_xq, scratch->q8_xscale, in_dim);
}

static void matvec_q8_0_decode_scratch(
        float                  * out,
        const ds4_model        * m,
        const ds4_tensor       * w,
        const float            * x,
        ds4_cpu_decode_scratch * scratch) {
```<｜Assistant｜></think>


[System] You are DeepSeek V4 Flash running locally. Answer accurately, preserve technical details, and use tools only when the prompt asks for tool use.

[User]
Explain what this code does and identify the inference stage it belongs to.

File: ds4.c

```c
e);
    free(xq);
}

static void matmul_q8_0_pair_batch(
        float           * out0,
        float           * out1,
        const ds4_model * m,
        const ds4_tensor * w0,
        const ds4_tensor * w1,
        const float     * x,
        uint64_t          n_tok) {
    if (w0->type != 8 || w1->type != 8 || w0->ndim != 2 || w1->ndim != 2) {
        ds4_die("expected two 2D Q8_0 tensors");
    }
    if (w0->dim[0] != w1->dim[0] || w0->dim[1] != w1->dim[1]) {
        ds4_die("paired Q8_0 tensors do not have the same shape");
    }

    const uint64_t in_dim = w0->dim[0];
    const uint64_t blocks = (in_dim + 31) / 32;
    int8_t *xq = xmalloc((size_t)n_tok * blocks * 32);
    float *xscale = xmalloc((size_t)n_tok * blocks * sizeof(xscale[0]));

    quantize_q8_0_activation_batch(x, xq, xscale, n_tok, in_dim);
    matmul_q8_0_pair_batch_prequant(out0, out1, m, w0, w1, xq, xscale, n_tok);

    free(xscale);
    free(xq);
}

static void matvec_q8_0_rows(
        float           * out,
        const ds4_model * m,
        const ds4_tensor * w,
        const float     * x,
        uint64_t          row0,
        uint64_t          n_rows) {
    if (w->type != 8 || w->ndim != 2) ds4_die("expected a 2D Q8_0 tensor");

    const uint64_t in_dim = w->dim[0];
    const uint64_t ctx_blocks = (in_dim + 31) / 32;
    int8_t *xq = xmalloc((size_t)ctx_blocks * 32);
    float *xscale = xmalloc((size_t)ctx_blocks * sizeof(xscale[0]));

    quantize_q8_0_activation(x, xq, xscale, in_dim);
    matvec_q8_0_rows_prequant(out, m, w, xq, xscale, row0, n_rows);

    free(xscale);
    free(xq);
}

/* Single-token Q8_0 matvec, used heavily in decode. */
static void matvec_q8_0(float *out, const ds4_model *m, const ds4_tensor *w, const float *x) {
    matvec_q8_0_rows(out, m, w, x, 0, w->dim[1]);
}

static void matvec_any(float *out, const ds4_model *m, const ds4_tensor *w, const float *x);

/* Decode scratch owns this temporary activation quantization so generation
 * can assert that the hot path performs no malloc. */
static void cpu_decode_quantize_q8_0(
        ds4_cpu_decode_scratch * scratch,
        const float            * x,
        uint64_t                 in_dim) {
    if (in_dim > scratch->q8_cap) ds4_die("CPU decode Q8_0 scratch buffer is too small");
    quantize_q8_0_activation(x, scratch->q8_xq, scratch->q8_xscale, in_dim);
}

static void matvec_q8_0_decode_scratch(
        float                  * out,
        const ds4_model        * m,
        const ds4_tensor       * w,
        const float            * x,
        ds4_cpu_decode_scratch * scratch) {
```

<｜begin▁of▁sentence｜>You are DeepSeek V4 Flash running locally. Answer accurately, preserve technical details, and use tools only when the prompt asks for tool use.<｜User｜>Explain what this code does and identify the inference stage it belongs to.

File: ds4.c

```c
e);
    free(xq);
}

static void matmul_q8_0_pair_batch(
        float           * out0,
        float           * out1,
        const ds4_model * m,
        const ds4_tensor * w0,
        const ds4_tensor * w1,
        const float     * x,
        uint64_t          n_tok) {
    if (w0->type != 8 || w1->type != 8 || w0->ndim != 2 || w1->ndim != 2) {
        ds4_die("expected two 2D Q8_0 tensors");
    }
    if (w0->dim[0] != w1->dim[0] || w0->dim[1] != w1->dim[1]) {
        ds4_die("paired Q8_0 tensors do not have the same shape");
    }

    const uint64_t in_dim = w0->dim[0];
    const uint64_t blocks = (in_dim + 31) / 32;
    int8_t *xq = xmalloc((size_t)n_tok * blocks * 32);
    float *xscale = xmalloc((size_t)n_tok * blocks * sizeof(xscale[0]));

    quantize_q8_0_activation_batch(x, xq, xscale, n_tok, in_dim);
    matmul_q8_0_pair_batch_prequant(out0, out1, m, w0, w1, xq, xscale, n_tok);

    free(xscale);
    free(xq);
}

static void matvec_q8_0_rows(
        float           * out,
        const ds4_model * m,
        const ds4_tensor * w,
        const float     * x,
        uint64_t          row0,
        uint64_t          n_rows) {
    if (w->type != 8 || w->ndim != 2) ds4_die("expected a 2D Q8_0 tensor");

    const uint64_t in_dim = w->dim[0];
    const uint64_t ctx_blocks = (in_dim + 31) / 32;
    int8_t *xq = xmalloc((size_t)ctx_blocks * 32);
    float *xscale = xmalloc((size_t)ctx_blocks * sizeof(xscale[0]));

    quantize_q8_0_activation(x, xq, xscale, in_dim);
    matvec_q8_0_rows_prequant(out, m, w, xq, xscale, row0, n_rows);

    free(xscale);
    free(xq);
}

/* Single-token Q8_0 matvec, used heavily in decode. */
static void matvec_q8_0(float *out, const ds4_model *m, const ds4_tensor *w, const float *x) {
    matvec_q8_0_rows(out, m, w, x, 0, w->dim[1]);
}

static void matvec_any(float *out, const ds4_model *m, const ds4_tensor *w, const float *x);

/* Decode scratch owns this temporary activation quantization so generation
 * can assert that the hot path performs no malloc. */
static void cpu_decode_quantize_q8_0(
        ds4_cpu_decode_scratch * scratch,
        const float            * x,
        uint64_t                 in_dim) {
    if (in_dim > scratch->q8_cap) ds4_die("CPU decode Q8_0 scratch buffer is too small");
    quantize_q8_0_activation(x, scratch->q8_xq, scratch->q8_xscale, in_dim);
}

static void matvec_q8_0_decode_scratch(
        float                  * out,
        const ds4_model        * m,
        const ds4_tensor       * w,
        const float            * x,
        ds4_cpu_decode_scratch * scratch) {
```<｜Assistant｜><think>


[System] You are DeepSeek V4 Flash running locally. Answer accurately, preserve technical details, and use tools only when the prompt asks for tool use.

[User]
Suggest a minimal correctness-preserving optimization for this excerpt.

File: ds4.c

```c
loat                  * out,
        const ds4_model        * m,
        const ds4_tensor       * w,
        const float            * x,
        ds4_cpu_decode_scratch * scratch) {
    cpu_decode_quantize_q8_0(scratch, x, w->dim[0]);
    matvec_q8_0_prequant(out, m, w, scratch->q8_xq, scratch->q8_xscale);
}

static void matvec_q8_0_pair_decode_scratch(
        float                  * out0,
        float                  * out1,
        const ds4_model        * m,
        const ds4_tensor       * w0,
        const ds4_tensor       * w1,
        const float            * x,
        ds4_cpu_decode_scratch * scratch) {
    cpu_decode_quantize_q8_0(scratch, x, w0->dim[0]);
    matvec_q8_0_pair_prequant(out0, out1, m, w0, w1, scratch->q8_xq, scratch->q8_xscale);
}

static void matvec_any_decode_scratch(
        float                  * out,
        const ds4_model        * m,
        const ds4_tensor       * w,
        const float            * x,
        ds4_cpu_decode_scratch * scratch) {
    if (w->type == 8) {
        matvec_q8_0_decode_scratch(out, m, w, x, scratch);
    } else {
        matvec_any(out, m, w, x);
    }
}

static void matvec_q8_0_grouped_rows(
        float           * out,
        const ds4_model * m,
        const ds4_tensor * w,
        const float     * x,
        uint32_t          n_groups,
        uint64_t          group_dim,
        uint64_t          rank) {
    if (w->type != 8 || w->ndim != 2) ds4_die("expected a 2D Q8_0 tensor");
    if (w->dim[0] != group_dim || w->dim[1] < (uint64_t)n_groups * rank) {
        ds4_die("grouped Q8_0 tensor has an unexpected layout");
    }

    const uint64_t blocks = (group_dim + 31) / 32;
    int8_t *xq = xmalloc((size_t)n_groups * blocks * 32);
    float *xscale = xmalloc((size_t)n_groups * blocks * sizeof(xscale[0]));

    for (uint32_t g = 0; g < n_groups; g++) {
        quantize_q8_0_activation(x + (uint64_t)g * group_dim,
                                 xq + (uint64_t)g * blocks * 32,
                                 xscale + (uint64_t)g * blocks,
                                 group_dim);
    }

    matvec_q8_0_grouped_ctx ctx = {
        .out = out,
        .data = tensor_data(m, w),
        .xq = xq,
        .xscale = xscale,
        .in_dim = group_dim,
        .blocks = blocks,
        .rank = rank,
    };
    ds4_parallel_for((uint64_t)n_groups * rank, matvec_q8_0_grouped_worker, &ctx);

    free(xscale);
    free(xq);
}

static void matvec_q8_0_grouped_rows_decode_scratch(
        float                  * out,
        const ds4_model        * m,
```

<｜begin▁of▁sentence｜>You are DeepSeek V4 Flash running locally. Answer accurately, preserve technical details, and use tools only when the prompt asks for tool use.<｜User｜>Suggest a minimal correctness-preserving optimization for this excerpt.

File: ds4.c

```c
loat                  * out,
        const ds4_model        * m,
        const ds4_tensor       * w,
        const float            * x,
        ds4_cpu_decode_scratch * scratch) {
    cpu_decode_quantize_q8_0(scratch, x, w->dim[0]);
    matvec_q8_0_prequant(out, m, w, scratch->q8_xq, scratch->q8_xscale);
}

static void matvec_q8_0_pair_decode_scratch(
        float                  * out0,
        float                  * out1,
        const ds4_model        * m,
        const ds4_tensor       * w0,
        const ds4_tensor       * w1,
        const float            * x,
        ds4_cpu_decode_scratch * scratch) {
    cpu_decode_quantize_q8_0(scratch, x, w0->dim[0]);
    matvec_q8_0_pair_prequant(out0, out1, m, w0, w1, scratch->q8_xq, scratch->q8_xscale);
}

static void matvec_any_decode_scratch(
        float                  * out,
        const ds4_model        * m,
        const ds4_tensor       * w,
        const float            * x,
        ds4_cpu_decode_scratch * scratch) {
    if (w->type == 8) {
        matvec_q8_0_decode_scratch(out, m, w, x, scratch);
    } else {
        matvec_any(out, m, w, x);
    }
}

static void matvec_q8_0_grouped_rows(
        float           * out,
        const ds4_model * m,
        const ds4_tensor * w,
        const float     * x,
        uint32_t          n_groups,
        uint64_t          group_dim,
        uint64_t          rank) {
    if (w->type != 8 || w->ndim != 2) ds4_die("expected a 2D Q8_0 tensor");
    if (w->dim[0] != group_dim || w->dim[1] < (uint64_t)n_groups * rank) {
        ds4_die("grouped Q8_0 tensor has an unexpected layout");
    }

    const uint64_t blocks = (group_dim + 31) / 32;
    int8_t *xq = xmalloc((size_t)n_groups * blocks * 32);
    float *xscale = xmalloc((size_t)n_groups * blocks * sizeof(xscale[0]));

    for (uint32_t g = 0; g < n_groups; g++) {
        quantize_q8_0_activation(x + (uint64_t)g * group_dim,
                                 xq + (uint64_t)g * blocks * 32,
                                 xscale + (uint64_t)g * blocks,
                                 group_dim);
    }

    matvec_q8_0_grouped_ctx ctx = {
        .out = out,
        .data = tensor_data(m, w),
        .xq = xq,
        .xscale = xscale,
        .in_dim = group_dim,
        .blocks = blocks,
        .rank = rank,
    };
    ds4_parallel_for((uint64_t)n_groups * rank, matvec_q8_0_grouped_worker, &ctx);

    free(xscale);
    free(xq);
}

static void matvec_q8_0_grouped_rows_decode_scratch(
        float                  * out,
        const ds4_model        * m,
```<｜Assistant｜></think>


[System] You are DeepSeek V4 Flash running locally. Answer accurately, preserve technical details, and use tools only when the prompt asks for tool use.

[User]
Suggest a minimal correctness-preserving optimization for this excerpt.

File: ds4.c

```c
loat                  * out,
        const ds4_model        * m,
        const ds4_tensor       * w,
        const float            * x,
        ds4_cpu_decode_scratch * scratch) {
    cpu_decode_quantize_q8_0(scratch, x, w->dim[0]);
    matvec_q8_0_prequant(out, m, w, scratch->q8_xq, scratch->q8_xscale);
}

static void matvec_q8_0_pair_decode_scratch(
        float                  * out0,
        float                  * out1,
        const ds4_model        * m,
        const ds4_tensor       * w0,
        const ds4_tensor       * w1,
        const float            * x,
        ds4_cpu_decode_scratch * scratch) {
    cpu_decode_quantize_q8_0(scratch, x, w0->dim[0]);
    matvec_q8_0_pair_prequant(out0, out1, m, w0, w1, scratch->q8_xq, scratch->q8_xscale);
}

static void matvec_any_decode_scratch(
        float                  * out,
        const ds4_model        * m,
        const ds4_tensor       * w,
        const float            * x,
        ds4_cpu_decode_scratch * scratch) {
    if (w->type == 8) {
        matvec_q8_0_decode_scratch(out, m, w, x, scratch);
    } else {
        matvec_any(out, m, w, x);
    }
}

static void matvec_q8_0_grouped_rows(
        float           * out,
        const ds4_model * m,
        const ds4_tensor * w,
        const float     * x,
        uint32_t          n_groups,
        uint64_t          group_dim,
        uint64_t          rank) {
    if (w->type != 8 || w->ndim != 2) ds4_die("expected a 2D Q8_0 tensor");
    if (w->dim[0] != group_dim || w->dim[1] < (uint64_t)n_groups * rank) {
        ds4_die("grouped Q8_0 tensor has an unexpected layout");
    }

    const uint64_t blocks = (group_dim + 31) / 32;
    int8_t *xq = xmalloc((size_t)n_groups * blocks * 32);
    float *xscale = xmalloc((size_t)n_groups * blocks * sizeof(xscale[0]));

    for (uint32_t g = 0; g < n_groups; g++) {
        quantize_q8_0_activation(x + (uint64_t)g * group_dim,
                                 xq + (uint64_t)g * blocks * 32,
                                 xscale + (uint64_t)g * blocks,
                                 group_dim);
    }

    matvec_q8_0_grouped_ctx ctx = {
        .out = out,
        .data = tensor_data(m, w),
        .xq = xq,
        .xscale = xscale,
        .in_dim = group_dim,
        .blocks = blocks,
        .rank = rank,
    };
    ds4_parallel_for((uint64_t)n_groups * rank, matvec_q8_0_grouped_worker, &ctx);

    free(xscale);
    free(xq);
}

static void matvec_q8_0_grouped_rows_decode_scratch(
        float                  * out,
        const ds4_model        * m,
```

<｜begin▁of▁sentence｜>You are DeepSeek V4 Flash running locally. Answer accurately, preserve technical details, and use tools only when the prompt asks for tool use.<｜User｜>Suggest a minimal correctness-preserving optimization for this excerpt.

File: ds4.c

```c
loat                  * out,
        const ds4_model        * m,
        const ds4_tensor       * w,
        const float            * x,
        ds4_cpu_decode_scratch * scratch) {
    cpu_decode_quantize_q8_0(scratch, x, w->dim[0]);
    matvec_q8_0_prequant(out, m, w, scratch->q8_xq, scratch->q8_xscale);
}

static void matvec_q8_0_pair_decode_scratch(
        float                  * out0,
        float                  * out1,
        const ds4_model        * m,
        const ds4_tensor       * w0,
        const ds4_tensor       * w1,
        const float            * x,
        ds4_cpu_decode_scratch * scratch) {
    cpu_decode_quantize_q8_0(scratch, x, w0->dim[0]);
    matvec_q8_0_pair_prequant(out0, out1, m, w0, w1, scratch->q8_xq, scratch->q8_xscale);
}

static void matvec_any_decode_scratch(
        float                  * out,
        const ds4_model        * m,
        const ds4_tensor       * w,
        const float            * x,
        ds4_cpu_decode_scratch * scratch) {
    if (w->type == 8) {
        matvec_q8_0_decode_scratch(out, m, w, x, scratch);
    } else {
        matvec_any(out, m, w, x);
    }
}

static void matvec_q8_0_grouped_rows(
        float           * out,
        const ds4_model * m,
        const ds4_tensor * w,
        const float     * x,
        uint32_t          n_groups,
        uint64_t          group_dim,
        uint64_t          rank) {
    if (w->type != 8 || w->ndim != 2) ds4_die("expected a 2D Q8_0 tensor");
    if (w->dim[0] != group_dim || w->dim[1] < (uint64_t)n_groups * rank) {
        ds4_die("grouped Q8_0 tensor has an unexpected layout");
    }

    const uint64_t blocks = (group_dim + 31) / 32;
    int8_t *xq = xmalloc((size_t)n_groups * blocks * 32);
    float *xscale = xmalloc((size_t)n_groups * blocks * sizeof(xscale[0]));

    for (uint32_t g = 0; g < n_groups; g++) {
        quantize_q8_0_activation(x + (uint64_t)g * group_dim,
                                 xq + (uint64_t)g * blocks * 32,
                                 xscale + (uint64_t)g * blocks,
                                 group_dim);
    }

    matvec_q8_0_grouped_ctx ctx = {
        .out = out,
        .data = tensor_data(m, w),
        .xq = xq,
        .xscale = xscale,
        .in_dim = group_dim,
        .blocks = blocks,
        .rank = rank,
    };
    ds4_parallel_for((uint64_t)n_groups * rank, matvec_q8_0_grouped_worker, &ctx);

    free(xscale);
    free(xq);
}

static void matvec_q8_0_grouped_rows_decode_scratch(
        float                  * out,
        const ds4_model        * m,
```<｜Assistant｜><think>


[System] You are DeepSeek V4 Flash running locally. Answer accurately, preserve technical details, and use tools only when the prompt asks for tool use.

[User]
Trova bug sottili e spiega quali invarianti dell'inferenza possono rompersi.

File: ds4.c

```c
ed_worker, &ctx);

    free(xscale);
    free(xq);
}

static void matvec_q8_0_grouped_rows_decode_scratch(
        float                  * out,
        const ds4_model        * m,
        const ds4_tensor       * w,
        const float            * x,
        uint32_t                 n_groups,
        uint64_t                 group_dim,
        uint64_t                 rank,
        ds4_cpu_decode_scratch * scratch) {
    if (w->type != 8 || w->ndim != 2) ds4_die("expected a 2D Q8_0 tensor");
    if (w->dim[0] != group_dim || w->dim[1] < (uint64_t)n_groups * rank) {
        ds4_die("grouped Q8_0 tensor has an unexpected layout");
    }
    if ((uint64_t)n_groups * group_dim > scratch->q8_cap) {
        ds4_die("CPU decode grouped Q8_0 scratch buffer is too small");
    }

    const uint64_t blocks = (group_dim + 31) / 32;
    for (uint32_t g = 0; g < n_groups; g++) {
        quantize_q8_0_activation(x + (uint64_t)g * group_dim,
                                 scratch->q8_xq + (uint64_t)g * blocks * 32,
                                 scratch->q8_xscale + (uint64_t)g * blocks,
                                 group_dim);
    }

    matvec_q8_0_grouped_ctx ctx = {
        .out = out,
        .data = tensor_data(m, w),
        .xq = scratch->q8_xq,
        .xscale = scratch->q8_xscale,
        .in_dim = group_dim,
        .blocks = blocks,
        .rank = rank,
    };
    ds4_parallel_for((uint64_t)n_groups * rank, matvec_q8_0_grouped_worker, &ctx);
}

static void matmul_q8_0_grouped_batch(
        float           * out,
        const ds4_model * m,
        const ds4_tensor * w,
        const float     * x,
        uint64_t          n_tok,
        uint32_t          n_groups,
        uint64_t          group_dim,
        uint64_t          rank) {
    if (w->type != 8 || w->ndim != 2) ds4_die("expected a 2D Q8_0 tensor");
    if (w->dim[0] != group_dim || w->dim[1] < (uint64_t)n_groups * rank) {
        ds4_die("grouped Q8_0 tensor has an unexpected layout");
    }

    const uint64_t blocks = (group_dim + 31) / 32;
    int8_t *xq = xmalloc((size_t)n_tok * n_groups * blocks * 32);
    float *xscale = xmalloc((size_t)n_tok * n_groups * blocks * sizeof(xscale[0]));

    for (uint64_t t = 0; t < n_tok; t++) {
        for (uint32_t g = 0; g < n_groups; g++) {
            const uint64_t xbase = (t * n_groups + g) * blocks;
            quantize_q8_0_activation(x + t * n_groups * group_dim + (uint64_t)g * group_dim,
                                     xq + xbase * 32,
                                     xscale + xbase,
```

<｜begin▁of▁sentence｜>You are DeepSeek V4 Flash running locally. Answer accurately, preserve technical details, and use tools only when the prompt asks for tool use.<｜User｜>Trova bug sottili e spiega quali invarianti dell'inferenza possono rompersi.

File: ds4.c

```c
ed_worker, &ctx);

    free(xscale);
    free(xq);
}

static void matvec_q8_0_grouped_rows_decode_scratch(
        float                  * out,
        const ds4_model        * m,
        const ds4_tensor       * w,
        const float            * x,
        uint32_t                 n_groups,
        uint64_t                 group_dim,
        uint64_t                 rank,
        ds4_cpu_decode_scratch * scratch) {
    if (w->type != 8 || w->ndim != 2) ds4_die("expected a 2D Q8_0 tensor");
    if (w->dim[0] != group_dim || w->dim[1] < (uint64_t)n_groups * rank) {
        ds4_die("grouped Q8_0 tensor has an unexpected layout");
    }
    if ((uint64_t)n_groups * group_dim > scratch->q8_cap) {
        ds4_die("CPU decode grouped Q8_0 scratch buffer is too small");
    }

    const uint64_t blocks = (group_dim + 31) / 32;
    for (uint32_t g = 0; g < n_groups; g++) {
        quantize_q8_0_activation(x + (uint64_t)g * group_dim,
                                 scratch->q8_xq + (uint64_t)g * blocks * 32,
                                 scratch->q8_xscale + (uint64_t)g * blocks,
                                 group_dim);
    }

    matvec_q8_0_grouped_ctx ctx = {
        .out = out,
        .data = tensor_data(m, w),
        .xq = scratch->q8_xq,
        .xscale = scratch->q8_xscale,
        .in_dim = group_dim,
        .blocks = blocks,
        .rank = rank,
    };
    ds4_parallel_for((uint64_t)n_groups * rank, matvec_q8_0_grouped_worker, &ctx);
}

static void matmul_q8_0_grouped_batch(
        float           * out,
        const ds4_model * m,
        const ds4_tensor * w,
        const float     * x,
        uint64_t          n_tok,
        uint32_t          n_groups,
        uint64_t          group_dim,
        uint64_t          rank) {
    if (w->type != 8 || w->ndim != 2) ds4_die("expected a 2D Q8_0 tensor");
    if (w->dim[0] != group_dim || w->dim[1] < (uint64_t)n_groups * rank) {
        ds4_die("grouped Q8_0 tensor has an unexpected layout");
    }

    const uint64_t blocks = (group_dim + 31) / 32;
    int8_t *xq = xmalloc((size_t)n_tok * n_groups * blocks * 32);
    float *xscale = xmalloc((size_t)n_tok * n_groups * blocks * sizeof(xscale[0]));

    for (uint64_t t = 0; t < n_tok; t++) {
        for (uint32_t g = 0; g < n_groups; g++) {
            const uint64_t xbase = (t * n_groups + g) * blocks;
            quantize_q8_0_activation(x + t * n_groups * group_dim + (uint64_t)g * group_dim,
                                     xq + xbase * 32,
                                     xscale + xbase,
```<｜Assistant｜></think>