[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
const float *q_a_norm = tensor_data(model, layer->attn_q_a_norm);

    matvec_q8_0(qr, model, layer->attn_q_a, norm);
    rms_norm_weight(qr_norm, qr, q_a_norm, 1024, DS4_RMS_EPS);
    matvec_q8_0(q, model, layer->attn_q_b, qr_norm);
    head_rms_norm_inplace(q, DS4_N_HEAD, DS4_N_HEAD_DIM, DS4_RMS_EPS);

    free(qr_norm);
    free(qr);
}

static void layer_q_projection_with_lora_one(
        const ds4_model   * model,
        const ds4_layer_weights * layer,
        const float       * norm,
        float             * q,
        float             * qr_norm) {
    float *qr = xmalloc(1024 * sizeof(qr[0]));
    const float *q_a_norm = tensor_data(model, layer->attn_q_a_norm);

    matvec_q8_0(qr, model, layer->attn_q_a, norm);
    rms_norm_weight(qr_norm, qr, q_a_norm, 1024, DS4_RMS_EPS);
    matvec_q8_0(q, model, layer->attn_q_b, qr_norm);
    head_rms_norm_inplace(q, DS4_N_HEAD, DS4_N_HEAD_DIM, DS4_RMS_EPS);

    free(qr);
}

/* KV projection has one KV head of width 512, followed by a learned RMSNorm. */
static void layer_kv_projection_normed_one(
        const ds4_model   * model,
        const ds4_layer_weights * layer,
        const float       * normed,
        float             * kv) {
    float *raw = xmalloc((size_t)DS4_N_HEAD_DIM * sizeof(raw[0]));

    const float *kv_norm = tensor_data(model, layer->attn_kv_a_norm);

    matvec_q8_0(raw, model, layer->attn_kv, normed);
    rms_norm_weight(kv, raw, kv_norm, DS4_N_HEAD_DIM, DS4_RMS_EPS);

    free(raw);
}

static void layer_q_projection_with_lora_one_decode_scratch(
        const ds4_model         * model,
        const ds4_layer_weights * layer,
        const float             * norm,
        float                   * q,
        float                   * qr_norm,
        ds4_cpu_decode_scratch  * scratch) {
    const float *q_a_norm = tensor_data(model, layer->attn_q_a_norm);

    matvec_q8_0_decode_scratch(scratch->qr, model, layer->attn_q_a, norm, scratch);
    rms_norm_weight(qr_norm, scratch->qr, q_a_norm, 1024, DS4_RMS_EPS);
    matvec_q8_0_decode_scratch(q, model, layer->attn_q_b, qr_norm, scratch);
    head_rms_norm_inplace(q, DS4_N_HEAD, DS4_N_HEAD_DIM, DS4_RMS_EPS);
}

static void layer_kv_projection_normed_one_decode_scratch(
        const ds4_model         * model,
        const ds4_layer_weights * layer,
        const float             * normed,
        float                   * kv,
        ds4_cpu_decode_scratch  * scratch) {
    const float *kv_norm = tensor_data(model, layer->attn_kv_a_norm);
```

<｜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
const float *q_a_norm = tensor_data(model, layer->attn_q_a_norm);

    matvec_q8_0(qr, model, layer->attn_q_a, norm);
    rms_norm_weight(qr_norm, qr, q_a_norm, 1024, DS4_RMS_EPS);
    matvec_q8_0(q, model, layer->attn_q_b, qr_norm);
    head_rms_norm_inplace(q, DS4_N_HEAD, DS4_N_HEAD_DIM, DS4_RMS_EPS);

    free(qr_norm);
    free(qr);
}

static void layer_q_projection_with_lora_one(
        const ds4_model   * model,
        const ds4_layer_weights * layer,
        const float       * norm,
        float             * q,
        float             * qr_norm) {
    float *qr = xmalloc(1024 * sizeof(qr[0]));
    const float *q_a_norm = tensor_data(model, layer->attn_q_a_norm);

    matvec_q8_0(qr, model, layer->attn_q_a, norm);
    rms_norm_weight(qr_norm, qr, q_a_norm, 1024, DS4_RMS_EPS);
    matvec_q8_0(q, model, layer->attn_q_b, qr_norm);
    head_rms_norm_inplace(q, DS4_N_HEAD, DS4_N_HEAD_DIM, DS4_RMS_EPS);

    free(qr);
}

/* KV projection has one KV head of width 512, followed by a learned RMSNorm. */
static void layer_kv_projection_normed_one(
        const ds4_model   * model,
        const ds4_layer_weights * layer,
        const float       * normed,
        float             * kv) {
    float *raw = xmalloc((size_t)DS4_N_HEAD_DIM * sizeof(raw[0]));

    const float *kv_norm = tensor_data(model, layer->attn_kv_a_norm);

    matvec_q8_0(raw, model, layer->attn_kv, normed);
    rms_norm_weight(kv, raw, kv_norm, DS4_N_HEAD_DIM, DS4_RMS_EPS);

    free(raw);
}

static void layer_q_projection_with_lora_one_decode_scratch(
        const ds4_model         * model,
        const ds4_layer_weights * layer,
        const float             * norm,
        float                   * q,
        float                   * qr_norm,
        ds4_cpu_decode_scratch  * scratch) {
    const float *q_a_norm = tensor_data(model, layer->attn_q_a_norm);

    matvec_q8_0_decode_scratch(scratch->qr, model, layer->attn_q_a, norm, scratch);
    rms_norm_weight(qr_norm, scratch->qr, q_a_norm, 1024, DS4_RMS_EPS);
    matvec_q8_0_decode_scratch(q, model, layer->attn_q_b, qr_norm, scratch);
    head_rms_norm_inplace(q, DS4_N_HEAD, DS4_N_HEAD_DIM, DS4_RMS_EPS);
}

static void layer_kv_projection_normed_one_decode_scratch(
        const ds4_model         * model,
        const ds4_layer_weights * layer,
        const float             * normed,
        float                   * kv,
        ds4_cpu_decode_scratch  * scratch) {
    const float *kv_norm = tensor_data(model, layer->attn_kv_a_norm);
```<｜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
loat             * normed,
        float                   * kv,
        ds4_cpu_decode_scratch  * scratch) {
    const float *kv_norm = tensor_data(model, layer->attn_kv_a_norm);

    matvec_q8_0_decode_scratch(scratch->kv_raw, model, layer->attn_kv, normed, scratch);
    rms_norm_weight(kv, scratch->kv_raw, kv_norm, DS4_N_HEAD_DIM, DS4_RMS_EPS);
}

static float rope_yarn_ramp(float low, float high, int i0) {
    const float y = ((float)(i0 / 2) - low) / fmaxf(0.001f, high - low);
    return 1.0f - fminf(1.0f, fmaxf(0.0f, y));
}

static float rope_yarn_corr_dim(int n_dims, uint64_t n_ctx_orig, float n_rot, float base) {
    return (float)n_dims * logf((float)n_ctx_orig / (n_rot * 2.0f * (float)M_PI)) / (2.0f * logf(base));
}

static void rope_yarn_corr_dims(int n_dims, uint64_t n_ctx_orig, float freq_base, float beta_fast, float beta_slow, float dims[2]) {
    const float start = floorf(rope_yarn_corr_dim(n_dims, n_ctx_orig, beta_fast, freq_base));
    const float end = ceilf(rope_yarn_corr_dim(n_dims, n_ctx_orig, beta_slow, freq_base));
    dims[0] = fmaxf(0.0f, start);
    dims[1] = fminf((float)(n_dims - 1), end);
}

/* Apply DS4 RoPE only to the tail of each head.  Compressed layers use the
 * long-context frequency base and scale; inverse mode rotates attention output
 * back before the grouped output projection. */
static void rope_tail_ext_inplace(
        float    * x,
        uint32_t   n_head,
        uint32_t   head_dim,
        uint32_t   n_rot,
        uint32_t   pos,
        uint64_t   n_ctx_orig,
        float      freq_base,
        float      freq_scale,
        float      ext_factor,
        float      attn_factor,
        float      beta_fast,
        float      beta_slow,
        bool       inverse) {
    const uint32_t n_nope = head_dim - n_rot;
    const float theta_scale = powf(freq_base, -2.0f / (float)n_rot);
    const float sin_sign = inverse ? -1.0f : 1.0f;
    float corr_dims[2] = { 0.0f, 0.0f };
    if (ext_factor != 0.0f) {
        rope_yarn_corr_dims((int)n_rot, n_ctx_orig, freq_base, beta_fast, beta_slow, corr_dims);
    }

    for (uint32_t h = 0; h < n_head; h++) {
        float *tail = x + (uint64_t)h * head_dim + n_nope;
        float theta_extrap = (float)pos;

        for (uint32_t i = 0; i < n_rot; i += 2) {
            const float theta_interp = freq_scale * theta_extrap;
            float theta = theta_interp;
            float mscale = attn_factor;

            if (ext_factor != 0.0f) {
                const float ramp_mix = rope_yarn_ramp(corr_dims[0], corr_dims[1], (int)i) * ext_factor;
```

<｜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
loat             * normed,
        float                   * kv,
        ds4_cpu_decode_scratch  * scratch) {
    const float *kv_norm = tensor_data(model, layer->attn_kv_a_norm);

    matvec_q8_0_decode_scratch(scratch->kv_raw, model, layer->attn_kv, normed, scratch);
    rms_norm_weight(kv, scratch->kv_raw, kv_norm, DS4_N_HEAD_DIM, DS4_RMS_EPS);
}

static float rope_yarn_ramp(float low, float high, int i0) {
    const float y = ((float)(i0 / 2) - low) / fmaxf(0.001f, high - low);
    return 1.0f - fminf(1.0f, fmaxf(0.0f, y));
}

static float rope_yarn_corr_dim(int n_dims, uint64_t n_ctx_orig, float n_rot, float base) {
    return (float)n_dims * logf((float)n_ctx_orig / (n_rot * 2.0f * (float)M_PI)) / (2.0f * logf(base));
}

static void rope_yarn_corr_dims(int n_dims, uint64_t n_ctx_orig, float freq_base, float beta_fast, float beta_slow, float dims[2]) {
    const float start = floorf(rope_yarn_corr_dim(n_dims, n_ctx_orig, beta_fast, freq_base));
    const float end = ceilf(rope_yarn_corr_dim(n_dims, n_ctx_orig, beta_slow, freq_base));
    dims[0] = fmaxf(0.0f, start);
    dims[1] = fminf((float)(n_dims - 1), end);
}

/* Apply DS4 RoPE only to the tail of each head.  Compressed layers use the
 * long-context frequency base and scale; inverse mode rotates attention output
 * back before the grouped output projection. */
static void rope_tail_ext_inplace(
        float    * x,
        uint32_t   n_head,
        uint32_t   head_dim,
        uint32_t   n_rot,
        uint32_t   pos,
        uint64_t   n_ctx_orig,
        float      freq_base,
        float      freq_scale,
        float      ext_factor,
        float      attn_factor,
        float      beta_fast,
        float      beta_slow,
        bool       inverse) {
    const uint32_t n_nope = head_dim - n_rot;
    const float theta_scale = powf(freq_base, -2.0f / (float)n_rot);
    const float sin_sign = inverse ? -1.0f : 1.0f;
    float corr_dims[2] = { 0.0f, 0.0f };
    if (ext_factor != 0.0f) {
        rope_yarn_corr_dims((int)n_rot, n_ctx_orig, freq_base, beta_fast, beta_slow, corr_dims);
    }

    for (uint32_t h = 0; h < n_head; h++) {
        float *tail = x + (uint64_t)h * head_dim + n_nope;
        float theta_extrap = (float)pos;

        for (uint32_t i = 0; i < n_rot; i += 2) {
            const float theta_interp = freq_scale * theta_extrap;
            float theta = theta_interp;
            float mscale = attn_factor;

            if (ext_factor != 0.0f) {
                const float ramp_mix = rope_yarn_ramp(corr_dims[0], corr_dims[1], (int)i) * ext_factor;
```<｜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
loat             * normed,
        float                   * kv,
        ds4_cpu_decode_scratch  * scratch) {
    const float *kv_norm = tensor_data(model, layer->attn_kv_a_norm);

    matvec_q8_0_decode_scratch(scratch->kv_raw, model, layer->attn_kv, normed, scratch);
    rms_norm_weight(kv, scratch->kv_raw, kv_norm, DS4_N_HEAD_DIM, DS4_RMS_EPS);
}

static float rope_yarn_ramp(float low, float high, int i0) {
    const float y = ((float)(i0 / 2) - low) / fmaxf(0.001f, high - low);
    return 1.0f - fminf(1.0f, fmaxf(0.0f, y));
}

static float rope_yarn_corr_dim(int n_dims, uint64_t n_ctx_orig, float n_rot, float base) {
    return (float)n_dims * logf((float)n_ctx_orig / (n_rot * 2.0f * (float)M_PI)) / (2.0f * logf(base));
}

static void rope_yarn_corr_dims(int n_dims, uint64_t n_ctx_orig, float freq_base, float beta_fast, float beta_slow, float dims[2]) {
    const float start = floorf(rope_yarn_corr_dim(n_dims, n_ctx_orig, beta_fast, freq_base));
    const float end = ceilf(rope_yarn_corr_dim(n_dims, n_ctx_orig, beta_slow, freq_base));
    dims[0] = fmaxf(0.0f, start);
    dims[1] = fminf((float)(n_dims - 1), end);
}

/* Apply DS4 RoPE only to the tail of each head.  Compressed layers use the
 * long-context frequency base and scale; inverse mode rotates attention output
 * back before the grouped output projection. */
static void rope_tail_ext_inplace(
        float    * x,
        uint32_t   n_head,
        uint32_t   head_dim,
        uint32_t   n_rot,
        uint32_t   pos,
        uint64_t   n_ctx_orig,
        float      freq_base,
        float      freq_scale,
        float      ext_factor,
        float      attn_factor,
        float      beta_fast,
        float      beta_slow,
        bool       inverse) {
    const uint32_t n_nope = head_dim - n_rot;
    const float theta_scale = powf(freq_base, -2.0f / (float)n_rot);
    const float sin_sign = inverse ? -1.0f : 1.0f;
    float corr_dims[2] = { 0.0f, 0.0f };
    if (ext_factor != 0.0f) {
        rope_yarn_corr_dims((int)n_rot, n_ctx_orig, freq_base, beta_fast, beta_slow, corr_dims);
    }

    for (uint32_t h = 0; h < n_head; h++) {
        float *tail = x + (uint64_t)h * head_dim + n_nope;
        float theta_extrap = (float)pos;

        for (uint32_t i = 0; i < n_rot; i += 2) {
            const float theta_interp = freq_scale * theta_extrap;
            float theta = theta_interp;
            float mscale = attn_factor;

            if (ext_factor != 0.0f) {
                const float ramp_mix = rope_yarn_ramp(corr_dims[0], corr_dims[1], (int)i) * ext_factor;
```

<｜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
loat             * normed,
        float                   * kv,
        ds4_cpu_decode_scratch  * scratch) {
    const float *kv_norm = tensor_data(model, layer->attn_kv_a_norm);

    matvec_q8_0_decode_scratch(scratch->kv_raw, model, layer->attn_kv, normed, scratch);
    rms_norm_weight(kv, scratch->kv_raw, kv_norm, DS4_N_HEAD_DIM, DS4_RMS_EPS);
}

static float rope_yarn_ramp(float low, float high, int i0) {
    const float y = ((float)(i0 / 2) - low) / fmaxf(0.001f, high - low);
    return 1.0f - fminf(1.0f, fmaxf(0.0f, y));
}

static float rope_yarn_corr_dim(int n_dims, uint64_t n_ctx_orig, float n_rot, float base) {
    return (float)n_dims * logf((float)n_ctx_orig / (n_rot * 2.0f * (float)M_PI)) / (2.0f * logf(base));
}

static void rope_yarn_corr_dims(int n_dims, uint64_t n_ctx_orig, float freq_base, float beta_fast, float beta_slow, float dims[2]) {
    const float start = floorf(rope_yarn_corr_dim(n_dims, n_ctx_orig, beta_fast, freq_base));
    const float end = ceilf(rope_yarn_corr_dim(n_dims, n_ctx_orig, beta_slow, freq_base));
    dims[0] = fmaxf(0.0f, start);
    dims[1] = fminf((float)(n_dims - 1), end);
}

/* Apply DS4 RoPE only to the tail of each head.  Compressed layers use the
 * long-context frequency base and scale; inverse mode rotates attention output
 * back before the grouped output projection. */
static void rope_tail_ext_inplace(
        float    * x,
        uint32_t   n_head,
        uint32_t   head_dim,
        uint32_t   n_rot,
        uint32_t   pos,
        uint64_t   n_ctx_orig,
        float      freq_base,
        float      freq_scale,
        float      ext_factor,
        float      attn_factor,
        float      beta_fast,
        float      beta_slow,
        bool       inverse) {
    const uint32_t n_nope = head_dim - n_rot;
    const float theta_scale = powf(freq_base, -2.0f / (float)n_rot);
    const float sin_sign = inverse ? -1.0f : 1.0f;
    float corr_dims[2] = { 0.0f, 0.0f };
    if (ext_factor != 0.0f) {
        rope_yarn_corr_dims((int)n_rot, n_ctx_orig, freq_base, beta_fast, beta_slow, corr_dims);
    }

    for (uint32_t h = 0; h < n_head; h++) {
        float *tail = x + (uint64_t)h * head_dim + n_nope;
        float theta_extrap = (float)pos;

        for (uint32_t i = 0; i < n_rot; i += 2) {
            const float theta_interp = freq_scale * theta_extrap;
            float theta = theta_interp;
            float mscale = attn_factor;

            if (ext_factor != 0.0f) {
                const float ramp_mix = rope_yarn_ramp(corr_dims[0], corr_dims[1], (int)i) * ext_factor;
```<｜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
float mscale = attn_factor;

            if (ext_factor != 0.0f) {
                const float ramp_mix = rope_yarn_ramp(corr_dims[0], corr_dims[1], (int)i) * ext_factor;
                theta = theta_interp * (1.0f - ramp_mix) + theta_extrap * ramp_mix;
                mscale *= 1.0f + 0.1f * logf(1.0f / freq_scale);
            }

            const float c = cosf(theta) * mscale;
            const float s = sin_sign * sinf(theta) * mscale;
            const float x0 = tail[i + 0];
            const float x1 = tail[i + 1];

            tail[i + 0] = x0 * c - x1 * s;
            tail[i + 1] = x0 * s + x1 * c;

            theta_extrap *= theta_scale;
        }
    }
}

/* Dense layers and compressed layers use different RoPE bases. */
static float layer_rope_freq_base(uint32_t il) {
    return ds4_layer_compress_ratio(il) != 0 && DS4_COMPRESS_ROPE_FREQ_BASE > 0.0f
        ? DS4_COMPRESS_ROPE_FREQ_BASE
        : DS4_ROPE_FREQ_BASE;
}

static float layer_rope_freq_scale(uint32_t il) {
    if (ds4_layer_compress_ratio(il) == 0 || DS4_ROPE_SCALE_FACTOR <= 0.0f) {
        return 1.0f;
    }
    return 1.0f / DS4_ROPE_SCALE_FACTOR;
}

static void rope_tail_layer_inplace(
        float            * x,
        uint32_t           n_head,
        uint32_t           head_dim,
        uint32_t           n_rot,
        uint32_t           pos,
        uint32_t           il,
        bool               inverse) {
    const bool compressed = ds4_layer_compress_ratio(il) != 0;
    const float freq_base = layer_rope_freq_base(il);
    const float freq_scale = layer_rope_freq_scale(il);
    const float ext_factor = compressed && DS4_ROPE_SCALE_FACTOR > 1.0f ? 1.0f : 0.0f;
    float attn_factor = 1.0f;
    if (ext_factor != 0.0f && freq_scale > 0.0f) {
        /*
         * This YaRN helper applies magnitude scaling internally. DeepSeek V4
         * reference RoPE uses interpolation without that magnitude change, so
         * pass the inverse factor here and let the helper cancel itself out.
         */
        attn_factor /= 1.0f + 0.1f * logf(1.0f / freq_scale);
    }

    rope_tail_ext_inplace(x, n_head, head_dim, n_rot, pos,
                          compressed ? DS4_ROPE_ORIG_CTX : 0,
                          freq_base,
                          freq_scale,
                          ext_factor,
                          attn_factor,
                          DS4_ROPE_YARN_BETA_FAST,
                          DS4_ROPE_YARN_BETA_SLOW,
                          inverse);
}

typedef struct {
    float            *x;
    uint64_t          stride;
```

<｜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
float mscale = attn_factor;

            if (ext_factor != 0.0f) {
                const float ramp_mix = rope_yarn_ramp(corr_dims[0], corr_dims[1], (int)i) * ext_factor;
                theta = theta_interp * (1.0f - ramp_mix) + theta_extrap * ramp_mix;
                mscale *= 1.0f + 0.1f * logf(1.0f / freq_scale);
            }

            const float c = cosf(theta) * mscale;
            const float s = sin_sign * sinf(theta) * mscale;
            const float x0 = tail[i + 0];
            const float x1 = tail[i + 1];

            tail[i + 0] = x0 * c - x1 * s;
            tail[i + 1] = x0 * s + x1 * c;

            theta_extrap *= theta_scale;
        }
    }
}

/* Dense layers and compressed layers use different RoPE bases. */
static float layer_rope_freq_base(uint32_t il) {
    return ds4_layer_compress_ratio(il) != 0 && DS4_COMPRESS_ROPE_FREQ_BASE > 0.0f
        ? DS4_COMPRESS_ROPE_FREQ_BASE
        : DS4_ROPE_FREQ_BASE;
}

static float layer_rope_freq_scale(uint32_t il) {
    if (ds4_layer_compress_ratio(il) == 0 || DS4_ROPE_SCALE_FACTOR <= 0.0f) {
        return 1.0f;
    }
    return 1.0f / DS4_ROPE_SCALE_FACTOR;
}

static void rope_tail_layer_inplace(
        float            * x,
        uint32_t           n_head,
        uint32_t           head_dim,
        uint32_t           n_rot,
        uint32_t           pos,
        uint32_t           il,
        bool               inverse) {
    const bool compressed = ds4_layer_compress_ratio(il) != 0;
    const float freq_base = layer_rope_freq_base(il);
    const float freq_scale = layer_rope_freq_scale(il);
    const float ext_factor = compressed && DS4_ROPE_SCALE_FACTOR > 1.0f ? 1.0f : 0.0f;
    float attn_factor = 1.0f;
    if (ext_factor != 0.0f && freq_scale > 0.0f) {
        /*
         * This YaRN helper applies magnitude scaling internally. DeepSeek V4
         * reference RoPE uses interpolation without that magnitude change, so
         * pass the inverse factor here and let the helper cancel itself out.
         */
        attn_factor /= 1.0f + 0.1f * logf(1.0f / freq_scale);
    }

    rope_tail_ext_inplace(x, n_head, head_dim, n_rot, pos,
                          compressed ? DS4_ROPE_ORIG_CTX : 0,
                          freq_base,
                          freq_scale,
                          ext_factor,
                          attn_factor,
                          DS4_ROPE_YARN_BETA_FAST,
                          DS4_ROPE_YARN_BETA_SLOW,
                          inverse);
}

typedef struct {
    float            *x;
    uint64_t          stride;
```<｜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
float mscale = attn_factor;

            if (ext_factor != 0.0f) {
                const float ramp_mix = rope_yarn_ramp(corr_dims[0], corr_dims[1], (int)i) * ext_factor;
                theta = theta_interp * (1.0f - ramp_mix) + theta_extrap * ramp_mix;
                mscale *= 1.0f + 0.1f * logf(1.0f / freq_scale);
            }

            const float c = cosf(theta) * mscale;
            const float s = sin_sign * sinf(theta) * mscale;
            const float x0 = tail[i + 0];
            const float x1 = tail[i + 1];

            tail[i + 0] = x0 * c - x1 * s;
            tail[i + 1] = x0 * s + x1 * c;

            theta_extrap *= theta_scale;
        }
    }
}

/* Dense layers and compressed layers use different RoPE bases. */
static float layer_rope_freq_base(uint32_t il) {
    return ds4_layer_compress_ratio(il) != 0 && DS4_COMPRESS_ROPE_FREQ_BASE > 0.0f
        ? DS4_COMPRESS_ROPE_FREQ_BASE
        : DS4_ROPE_FREQ_BASE;
}

static float layer_rope_freq_scale(uint32_t il) {
    if (ds4_layer_compress_ratio(il) == 0 || DS4_ROPE_SCALE_FACTOR <= 0.0f) {
        return 1.0f;
    }
    return 1.0f / DS4_ROPE_SCALE_FACTOR;
}

static void rope_tail_layer_inplace(
        float            * x,
        uint32_t           n_head,
        uint32_t           head_dim,
        uint32_t           n_rot,
        uint32_t           pos,
        uint32_t           il,
        bool               inverse) {
    const bool compressed = ds4_layer_compress_ratio(il) != 0;
    const float freq_base = layer_rope_freq_base(il);
    const float freq_scale = layer_rope_freq_scale(il);
    const float ext_factor = compressed && DS4_ROPE_SCALE_FACTOR > 1.0f ? 1.0f : 0.0f;
    float attn_factor = 1.0f;
    if (ext_factor != 0.0f && freq_scale > 0.0f) {
        /*
         * This YaRN helper applies magnitude scaling internally. DeepSeek V4
         * reference RoPE uses interpolation without that magnitude change, so
         * pass the inverse factor here and let the helper cancel itself out.
         */
        attn_factor /= 1.0f + 0.1f * logf(1.0f / freq_scale);
    }

    rope_tail_ext_inplace(x, n_head, head_dim, n_rot, pos,
                          compressed ? DS4_ROPE_ORIG_CTX : 0,
                          freq_base,
                          freq_scale,
                          ext_factor,
                          attn_factor,
                          DS4_ROPE_YARN_BETA_FAST,
                          DS4_ROPE_YARN_BETA_SLOW,
                          inverse);
}

typedef struct {
    float            *x;
    uint64_t          stride;
```

<｜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
float mscale = attn_factor;

            if (ext_factor != 0.0f) {
                const float ramp_mix = rope_yarn_ramp(corr_dims[0], corr_dims[1], (int)i) * ext_factor;
                theta = theta_interp * (1.0f - ramp_mix) + theta_extrap * ramp_mix;
                mscale *= 1.0f + 0.1f * logf(1.0f / freq_scale);
            }

            const float c = cosf(theta) * mscale;
            const float s = sin_sign * sinf(theta) * mscale;
            const float x0 = tail[i + 0];
            const float x1 = tail[i + 1];

            tail[i + 0] = x0 * c - x1 * s;
            tail[i + 1] = x0 * s + x1 * c;

            theta_extrap *= theta_scale;
        }
    }
}

/* Dense layers and compressed layers use different RoPE bases. */
static float layer_rope_freq_base(uint32_t il) {
    return ds4_layer_compress_ratio(il) != 0 && DS4_COMPRESS_ROPE_FREQ_BASE > 0.0f
        ? DS4_COMPRESS_ROPE_FREQ_BASE
        : DS4_ROPE_FREQ_BASE;
}

static float layer_rope_freq_scale(uint32_t il) {
    if (ds4_layer_compress_ratio(il) == 0 || DS4_ROPE_SCALE_FACTOR <= 0.0f) {
        return 1.0f;
    }
    return 1.0f / DS4_ROPE_SCALE_FACTOR;
}

static void rope_tail_layer_inplace(
        float            * x,
        uint32_t           n_head,
        uint32_t           head_dim,
        uint32_t           n_rot,
        uint32_t           pos,
        uint32_t           il,
        bool               inverse) {
    const bool compressed = ds4_layer_compress_ratio(il) != 0;
    const float freq_base = layer_rope_freq_base(il);
    const float freq_scale = layer_rope_freq_scale(il);
    const float ext_factor = compressed && DS4_ROPE_SCALE_FACTOR > 1.0f ? 1.0f : 0.0f;
    float attn_factor = 1.0f;
    if (ext_factor != 0.0f && freq_scale > 0.0f) {
        /*
         * This YaRN helper applies magnitude scaling internally. DeepSeek V4
         * reference RoPE uses interpolation without that magnitude change, so
         * pass the inverse factor here and let the helper cancel itself out.
         */
        attn_factor /= 1.0f + 0.1f * logf(1.0f / freq_scale);
    }

    rope_tail_ext_inplace(x, n_head, head_dim, n_rot, pos,
                          compressed ? DS4_ROPE_ORIG_CTX : 0,
                          freq_base,
                          freq_scale,
                          ext_factor,
                          attn_factor,
                          DS4_ROPE_YARN_BETA_FAST,
                          DS4_ROPE_YARN_BETA_SLOW,
                          inverse);
}

typedef struct {
    float            *x;
    uint64_t          stride;
```<｜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
PE_YARN_BETA_FAST,
                          DS4_ROPE_YARN_BETA_SLOW,
                          inverse);
}

typedef struct {
    float            *x;
    uint64_t          stride;
    uint32_t          n_head;
    uint32_t          head_dim;
    uint32_t          n_rot;
    uint32_t          pos0;
    uint32_t          il;
    bool              inverse;
} rope_tail_batch_ctx;

static void rope_tail_batch_worker(void *vctx, uint64_t t0, uint64_t t1) {
    rope_tail_batch_ctx *ctx = vctx;
    for (uint64_t tt = t0; tt < t1; tt++) {
        rope_tail_layer_inplace(ctx->x + tt * ctx->stride,
                                ctx->n_head,
                                ctx->head_dim,
                                ctx->n_rot,
                                ctx->pos0 + (uint32_t)tt,
                                ctx->il,
                                ctx->inverse);
    }
}

static void rope_tail_layer_batch_inplace(
        float            *x,
        uint64_t          stride,
        uint32_t          n_head,
        uint32_t          head_dim,
        uint32_t          n_rot,
        uint32_t          pos0,
        uint32_t          il,
        bool              inverse,
        uint32_t          n_tok) {
    rope_tail_batch_ctx ctx = {
        .x = x,
        .stride = stride,
        .n_head = n_head,
        .head_dim = head_dim,
        .n_rot = n_rot,
        .pos0 = pos0,
        .il = il,
        .inverse = inverse,
    };
    ds4_parallel_for_min_rows(n_tok, rope_tail_batch_worker, &ctx, 1);
}

static inline float dot_f32(const float *a, const float *b, uint32_t n) {
#if defined(__ARM_NEON)
    uint32_t i = 0;
    float32x4_t acc0 = vdupq_n_f32(0.0f);
    float32x4_t acc1 = vdupq_n_f32(0.0f);
    for (; i + 8 <= n; i += 8) {
        acc0 = vfmaq_f32(acc0, vld1q_f32(a + i),     vld1q_f32(b + i));
        acc1 = vfmaq_f32(acc1, vld1q_f32(a + i + 4), vld1q_f32(b + i + 4));
    }
    float acc = vaddvq_f32(vaddq_f32(acc0, acc1));
    for (; i < n; i++) acc += a[i] * b[i];
    return acc;
#else
    float acc = 0.0f;
    for (uint32_t i = 0; i < n; i++) acc += a[i] * b[i];
    return acc;
#endif
}

static inline void axpy_f32(float *y, const float *x, float a, uint32_t n) {
#if defined(__ARM_NEON)
    uint32_t i = 0;
    const float32x4_t av = vdupq_n_f32(a);
    for (; i + 8 <= n; i += 8) {
        vst1q_f32(y + i,     vfmaq_f32(vld1q_f32(y + i),     av, vld1q_f32(x + i)));
        vst1q_f32(y + i + 4, vfmaq_f32(vld1q_f32(y + i + 4), av, vld1q_f32(x + i + 4)));
    }
    for (; i < n; i++) y[i] += a * x[i];
#else
```

<｜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
PE_YARN_BETA_FAST,
                          DS4_ROPE_YARN_BETA_SLOW,
                          inverse);
}

typedef struct {
    float            *x;
    uint64_t          stride;
    uint32_t          n_head;
    uint32_t          head_dim;
    uint32_t          n_rot;
    uint32_t          pos0;
    uint32_t          il;
    bool              inverse;
} rope_tail_batch_ctx;

static void rope_tail_batch_worker(void *vctx, uint64_t t0, uint64_t t1) {
    rope_tail_batch_ctx *ctx = vctx;
    for (uint64_t tt = t0; tt < t1; tt++) {
        rope_tail_layer_inplace(ctx->x + tt * ctx->stride,
                                ctx->n_head,
                                ctx->head_dim,
                                ctx->n_rot,
                                ctx->pos0 + (uint32_t)tt,
                                ctx->il,
                                ctx->inverse);
    }
}

static void rope_tail_layer_batch_inplace(
        float            *x,
        uint64_t          stride,
        uint32_t          n_head,
        uint32_t          head_dim,
        uint32_t          n_rot,
        uint32_t          pos0,
        uint32_t          il,
        bool              inverse,
        uint32_t          n_tok) {
    rope_tail_batch_ctx ctx = {
        .x = x,
        .stride = stride,
        .n_head = n_head,
        .head_dim = head_dim,
        .n_rot = n_rot,
        .pos0 = pos0,
        .il = il,
        .inverse = inverse,
    };
    ds4_parallel_for_min_rows(n_tok, rope_tail_batch_worker, &ctx, 1);
}

static inline float dot_f32(const float *a, const float *b, uint32_t n) {
#if defined(__ARM_NEON)
    uint32_t i = 0;
    float32x4_t acc0 = vdupq_n_f32(0.0f);
    float32x4_t acc1 = vdupq_n_f32(0.0f);
    for (; i + 8 <= n; i += 8) {
        acc0 = vfmaq_f32(acc0, vld1q_f32(a + i),     vld1q_f32(b + i));
        acc1 = vfmaq_f32(acc1, vld1q_f32(a + i + 4), vld1q_f32(b + i + 4));
    }
    float acc = vaddvq_f32(vaddq_f32(acc0, acc1));
    for (; i < n; i++) acc += a[i] * b[i];
    return acc;
#else
    float acc = 0.0f;
    for (uint32_t i = 0; i < n; i++) acc += a[i] * b[i];
    return acc;
#endif
}

static inline void axpy_f32(float *y, const float *x, float a, uint32_t n) {
#if defined(__ARM_NEON)
    uint32_t i = 0;
    const float32x4_t av = vdupq_n_f32(a);
    for (; i + 8 <= n; i += 8) {
        vst1q_f32(y + i,     vfmaq_f32(vld1q_f32(y + i),     av, vld1q_f32(x + i)));
        vst1q_f32(y + i + 4, vfmaq_f32(vld1q_f32(y + i + 4), av, vld1q_f32(x + i + 4)));
    }
    for (; i < n; i++) y[i] += a * x[i];
#else
```<｜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
PE_YARN_BETA_FAST,
                          DS4_ROPE_YARN_BETA_SLOW,
                          inverse);
}

typedef struct {
    float            *x;
    uint64_t          stride;
    uint32_t          n_head;
    uint32_t          head_dim;
    uint32_t          n_rot;
    uint32_t          pos0;
    uint32_t          il;
    bool              inverse;
} rope_tail_batch_ctx;

static void rope_tail_batch_worker(void *vctx, uint64_t t0, uint64_t t1) {
    rope_tail_batch_ctx *ctx = vctx;
    for (uint64_t tt = t0; tt < t1; tt++) {
        rope_tail_layer_inplace(ctx->x + tt * ctx->stride,
                                ctx->n_head,
                                ctx->head_dim,
                                ctx->n_rot,
                                ctx->pos0 + (uint32_t)tt,
                                ctx->il,
                                ctx->inverse);
    }
}

static void rope_tail_layer_batch_inplace(
        float            *x,
        uint64_t          stride,
        uint32_t          n_head,
        uint32_t          head_dim,
        uint32_t          n_rot,
        uint32_t          pos0,
        uint32_t          il,
        bool              inverse,
        uint32_t          n_tok) {
    rope_tail_batch_ctx ctx = {
        .x = x,
        .stride = stride,
        .n_head = n_head,
        .head_dim = head_dim,
        .n_rot = n_rot,
        .pos0 = pos0,
        .il = il,
        .inverse = inverse,
    };
    ds4_parallel_for_min_rows(n_tok, rope_tail_batch_worker, &ctx, 1);
}

static inline float dot_f32(const float *a, const float *b, uint32_t n) {
#if defined(__ARM_NEON)
    uint32_t i = 0;
    float32x4_t acc0 = vdupq_n_f32(0.0f);
    float32x4_t acc1 = vdupq_n_f32(0.0f);
    for (; i + 8 <= n; i += 8) {
        acc0 = vfmaq_f32(acc0, vld1q_f32(a + i),     vld1q_f32(b + i));
        acc1 = vfmaq_f32(acc1, vld1q_f32(a + i + 4), vld1q_f32(b + i + 4));
    }
    float acc = vaddvq_f32(vaddq_f32(acc0, acc1));
    for (; i < n; i++) acc += a[i] * b[i];
    return acc;
#else
    float acc = 0.0f;
    for (uint32_t i = 0; i < n; i++) acc += a[i] * b[i];
    return acc;
#endif
}

static inline void axpy_f32(float *y, const float *x, float a, uint32_t n) {
#if defined(__ARM_NEON)
    uint32_t i = 0;
    const float32x4_t av = vdupq_n_f32(a);
    for (; i + 8 <= n; i += 8) {
        vst1q_f32(y + i,     vfmaq_f32(vld1q_f32(y + i),     av, vld1q_f32(x + i)));
        vst1q_f32(y + i + 4, vfmaq_f32(vld1q_f32(y + i + 4), av, vld1q_f32(x + i + 4)));
    }
    for (; i < n; i++) y[i] += a * x[i];
#else
```

<｜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
PE_YARN_BETA_FAST,
                          DS4_ROPE_YARN_BETA_SLOW,
                          inverse);
}

typedef struct {
    float            *x;
    uint64_t          stride;
    uint32_t          n_head;
    uint32_t          head_dim;
    uint32_t          n_rot;
    uint32_t          pos0;
    uint32_t          il;
    bool              inverse;
} rope_tail_batch_ctx;

static void rope_tail_batch_worker(void *vctx, uint64_t t0, uint64_t t1) {
    rope_tail_batch_ctx *ctx = vctx;
    for (uint64_t tt = t0; tt < t1; tt++) {
        rope_tail_layer_inplace(ctx->x + tt * ctx->stride,
                                ctx->n_head,
                                ctx->head_dim,
                                ctx->n_rot,
                                ctx->pos0 + (uint32_t)tt,
                                ctx->il,
                                ctx->inverse);
    }
}

static void rope_tail_layer_batch_inplace(
        float            *x,
        uint64_t          stride,
        uint32_t          n_head,
        uint32_t          head_dim,
        uint32_t          n_rot,
        uint32_t          pos0,
        uint32_t          il,
        bool              inverse,
        uint32_t          n_tok) {
    rope_tail_batch_ctx ctx = {
        .x = x,
        .stride = stride,
        .n_head = n_head,
        .head_dim = head_dim,
        .n_rot = n_rot,
        .pos0 = pos0,
        .il = il,
        .inverse = inverse,
    };
    ds4_parallel_for_min_rows(n_tok, rope_tail_batch_worker, &ctx, 1);
}

static inline float dot_f32(const float *a, const float *b, uint32_t n) {
#if defined(__ARM_NEON)
    uint32_t i = 0;
    float32x4_t acc0 = vdupq_n_f32(0.0f);
    float32x4_t acc1 = vdupq_n_f32(0.0f);
    for (; i + 8 <= n; i += 8) {
        acc0 = vfmaq_f32(acc0, vld1q_f32(a + i),     vld1q_f32(b + i));
        acc1 = vfmaq_f32(acc1, vld1q_f32(a + i + 4), vld1q_f32(b + i + 4));
    }
    float acc = vaddvq_f32(vaddq_f32(acc0, acc1));
    for (; i < n; i++) acc += a[i] * b[i];
    return acc;
#else
    float acc = 0.0f;
    for (uint32_t i = 0; i < n; i++) acc += a[i] * b[i];
    return acc;
#endif
}

static inline void axpy_f32(float *y, const float *x, float a, uint32_t n) {
#if defined(__ARM_NEON)
    uint32_t i = 0;
    const float32x4_t av = vdupq_n_f32(a);
    for (; i + 8 <= n; i += 8) {
        vst1q_f32(y + i,     vfmaq_f32(vld1q_f32(y + i),     av, vld1q_f32(x + i)));
        vst1q_f32(y + i + 4, vfmaq_f32(vld1q_f32(y + i + 4), av, vld1q_f32(x + i + 4)));
    }
    for (; i < n; i++) y[i] += a * x[i];
#else
```<｜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
32(y + i),     av, vld1q_f32(x + i)));
        vst1q_f32(y + i + 4, vfmaq_f32(vld1q_f32(y + i + 4), av, vld1q_f32(x + i + 4)));
    }
    for (; i < n; i++) y[i] += a * x[i];
#else
    for (uint32_t i = 0; i < n; i++) y[i] += a * x[i];
#endif
}

static inline void scale_f32(float *x, float a, uint32_t n) {
#if defined(__ARM_NEON)
    uint32_t i = 0;
    const float32x4_t av = vdupq_n_f32(a);
    for (; i + 8 <= n; i += 8) {
        vst1q_f32(x + i,     vmulq_f32(vld1q_f32(x + i),     av));
        vst1q_f32(x + i + 4, vmulq_f32(vld1q_f32(x + i + 4), av));
    }
    for (; i < n; i++) x[i] *= a;
#else
    for (uint32_t i = 0; i < n; i++) x[i] *= a;
#endif
}

static float sigmoid_stable(float x) {
    if (x >= 0.0f) {
        const float e = expf(-x);
        return 1.0f / (1.0f + e);
    } else {
        const float e = expf(x);
        return e / (1.0f + e);
    }
}

/* Sink-aware attention over a set of KV rows.  The learned sink logit is part
 * of the softmax denominator but contributes no value vector. */
static void layer_attention_rows_one(
        float             * out_heads,
        const ds4_model   * model,
        const ds4_layer_weights * layer,
        const float       * q,
        const float       * kv_rows,
        uint32_t            n_kv) {
    const float *sinks = tensor_data(model, layer->attn_sinks);
    const float kq_scale = 1.0f / sqrtf((float)DS4_N_HEAD_DIM);
    float score_stack[512];
    float *score = n_kv <= 512 ? score_stack : xmalloc((size_t)n_kv * sizeof(score[0]));

    for (uint32_t h = 0; h < DS4_N_HEAD; h++) {
        const float *qh = q + (uint64_t)h * DS4_N_HEAD_DIM;

        float max_score = sinks[h];
        for (uint32_t r = 0; r < n_kv; r++) {
            const float *kv = kv_rows + (uint64_t)r * DS4_N_HEAD_DIM;
            score[r] = dot_f32(qh, kv, DS4_N_HEAD_DIM) * kq_scale;
            if (score[r] > max_score) max_score = score[r];
        }

        float *oh = out_heads + (uint64_t)h * DS4_N_HEAD_DIM;
        memset(oh, 0, (size_t)DS4_N_HEAD_DIM * sizeof(oh[0]));

        float denom = expf(sinks[h] - max_score);
        for (uint32_t r = 0; r < n_kv; r++) {
            const float weight = expf(score[r] - max_score);
            const float *kv = kv_rows + (uint64_t)r * DS4_N_HEAD_DIM;
            denom += weight;
            axpy_f32(oh, kv, weight, DS4_N_HEAD_DIM);
        }

        const float inv = 1.0f / denom;
        scale_f32(oh, inv, DS4_N_HEAD_DIM);
    }

    if (score != score_stack) free(score);
}

static void layer_attention_one(
        float             * out_heads,
```

<｜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
32(y + i),     av, vld1q_f32(x + i)));
        vst1q_f32(y + i + 4, vfmaq_f32(vld1q_f32(y + i + 4), av, vld1q_f32(x + i + 4)));
    }
    for (; i < n; i++) y[i] += a * x[i];
#else
    for (uint32_t i = 0; i < n; i++) y[i] += a * x[i];
#endif
}

static inline void scale_f32(float *x, float a, uint32_t n) {
#if defined(__ARM_NEON)
    uint32_t i = 0;
    const float32x4_t av = vdupq_n_f32(a);
    for (; i + 8 <= n; i += 8) {
        vst1q_f32(x + i,     vmulq_f32(vld1q_f32(x + i),     av));
        vst1q_f32(x + i + 4, vmulq_f32(vld1q_f32(x + i + 4), av));
    }
    for (; i < n; i++) x[i] *= a;
#else
    for (uint32_t i = 0; i < n; i++) x[i] *= a;
#endif
}

static float sigmoid_stable(float x) {
    if (x >= 0.0f) {
        const float e = expf(-x);
        return 1.0f / (1.0f + e);
    } else {
        const float e = expf(x);
        return e / (1.0f + e);
    }
}

/* Sink-aware attention over a set of KV rows.  The learned sink logit is part
 * of the softmax denominator but contributes no value vector. */
static void layer_attention_rows_one(
        float             * out_heads,
        const ds4_model   * model,
        const ds4_layer_weights * layer,
        const float       * q,
        const float       * kv_rows,
        uint32_t            n_kv) {
    const float *sinks = tensor_data(model, layer->attn_sinks);
    const float kq_scale = 1.0f / sqrtf((float)DS4_N_HEAD_DIM);
    float score_stack[512];
    float *score = n_kv <= 512 ? score_stack : xmalloc((size_t)n_kv * sizeof(score[0]));

    for (uint32_t h = 0; h < DS4_N_HEAD; h++) {
        const float *qh = q + (uint64_t)h * DS4_N_HEAD_DIM;

        float max_score = sinks[h];
        for (uint32_t r = 0; r < n_kv; r++) {
            const float *kv = kv_rows + (uint64_t)r * DS4_N_HEAD_DIM;
            score[r] = dot_f32(qh, kv, DS4_N_HEAD_DIM) * kq_scale;
            if (score[r] > max_score) max_score = score[r];
        }

        float *oh = out_heads + (uint64_t)h * DS4_N_HEAD_DIM;
        memset(oh, 0, (size_t)DS4_N_HEAD_DIM * sizeof(oh[0]));

        float denom = expf(sinks[h] - max_score);
        for (uint32_t r = 0; r < n_kv; r++) {
            const float weight = expf(score[r] - max_score);
            const float *kv = kv_rows + (uint64_t)r * DS4_N_HEAD_DIM;
            denom += weight;
            axpy_f32(oh, kv, weight, DS4_N_HEAD_DIM);
        }

        const float inv = 1.0f / denom;
        scale_f32(oh, inv, DS4_N_HEAD_DIM);
    }

    if (score != score_stack) free(score);
}

static void layer_attention_one(
        float             * out_heads,
```<｜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
32(y + i),     av, vld1q_f32(x + i)));
        vst1q_f32(y + i + 4, vfmaq_f32(vld1q_f32(y + i + 4), av, vld1q_f32(x + i + 4)));
    }
    for (; i < n; i++) y[i] += a * x[i];
#else
    for (uint32_t i = 0; i < n; i++) y[i] += a * x[i];
#endif
}

static inline void scale_f32(float *x, float a, uint32_t n) {
#if defined(__ARM_NEON)
    uint32_t i = 0;
    const float32x4_t av = vdupq_n_f32(a);
    for (; i + 8 <= n; i += 8) {
        vst1q_f32(x + i,     vmulq_f32(vld1q_f32(x + i),     av));
        vst1q_f32(x + i + 4, vmulq_f32(vld1q_f32(x + i + 4), av));
    }
    for (; i < n; i++) x[i] *= a;
#else
    for (uint32_t i = 0; i < n; i++) x[i] *= a;
#endif
}

static float sigmoid_stable(float x) {
    if (x >= 0.0f) {
        const float e = expf(-x);
        return 1.0f / (1.0f + e);
    } else {
        const float e = expf(x);
        return e / (1.0f + e);
    }
}

/* Sink-aware attention over a set of KV rows.  The learned sink logit is part
 * of the softmax denominator but contributes no value vector. */
static void layer_attention_rows_one(
        float             * out_heads,
        const ds4_model   * model,
        const ds4_layer_weights * layer,
        const float       * q,
        const float       * kv_rows,
        uint32_t            n_kv) {
    const float *sinks = tensor_data(model, layer->attn_sinks);
    const float kq_scale = 1.0f / sqrtf((float)DS4_N_HEAD_DIM);
    float score_stack[512];
    float *score = n_kv <= 512 ? score_stack : xmalloc((size_t)n_kv * sizeof(score[0]));

    for (uint32_t h = 0; h < DS4_N_HEAD; h++) {
        const float *qh = q + (uint64_t)h * DS4_N_HEAD_DIM;

        float max_score = sinks[h];
        for (uint32_t r = 0; r < n_kv; r++) {
            const float *kv = kv_rows + (uint64_t)r * DS4_N_HEAD_DIM;
            score[r] = dot_f32(qh, kv, DS4_N_HEAD_DIM) * kq_scale;
            if (score[r] > max_score) max_score = score[r];
        }

        float *oh = out_heads + (uint64_t)h * DS4_N_HEAD_DIM;
        memset(oh, 0, (size_t)DS4_N_HEAD_DIM * sizeof(oh[0]));

        float denom = expf(sinks[h] - max_score);
        for (uint32_t r = 0; r < n_kv; r++) {
            const float weight = expf(score[r] - max_score);
            const float *kv = kv_rows + (uint64_t)r * DS4_N_HEAD_DIM;
            denom += weight;
            axpy_f32(oh, kv, weight, DS4_N_HEAD_DIM);
        }

        const float inv = 1.0f / denom;
        scale_f32(oh, inv, DS4_N_HEAD_DIM);
    }

    if (score != score_stack) free(score);
}

static void layer_attention_one(
        float             * out_heads,
```

<｜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
32(y + i),     av, vld1q_f32(x + i)));
        vst1q_f32(y + i + 4, vfmaq_f32(vld1q_f32(y + i + 4), av, vld1q_f32(x + i + 4)));
    }
    for (; i < n; i++) y[i] += a * x[i];
#else
    for (uint32_t i = 0; i < n; i++) y[i] += a * x[i];
#endif
}

static inline void scale_f32(float *x, float a, uint32_t n) {
#if defined(__ARM_NEON)
    uint32_t i = 0;
    const float32x4_t av = vdupq_n_f32(a);
    for (; i + 8 <= n; i += 8) {
        vst1q_f32(x + i,     vmulq_f32(vld1q_f32(x + i),     av));
        vst1q_f32(x + i + 4, vmulq_f32(vld1q_f32(x + i + 4), av));
    }
    for (; i < n; i++) x[i] *= a;
#else
    for (uint32_t i = 0; i < n; i++) x[i] *= a;
#endif
}

static float sigmoid_stable(float x) {
    if (x >= 0.0f) {
        const float e = expf(-x);
        return 1.0f / (1.0f + e);
    } else {
        const float e = expf(x);
        return e / (1.0f + e);
    }
}

/* Sink-aware attention over a set of KV rows.  The learned sink logit is part
 * of the softmax denominator but contributes no value vector. */
static void layer_attention_rows_one(
        float             * out_heads,
        const ds4_model   * model,
        const ds4_layer_weights * layer,
        const float       * q,
        const float       * kv_rows,
        uint32_t            n_kv) {
    const float *sinks = tensor_data(model, layer->attn_sinks);
    const float kq_scale = 1.0f / sqrtf((float)DS4_N_HEAD_DIM);
    float score_stack[512];
    float *score = n_kv <= 512 ? score_stack : xmalloc((size_t)n_kv * sizeof(score[0]));

    for (uint32_t h = 0; h < DS4_N_HEAD; h++) {
        const float *qh = q + (uint64_t)h * DS4_N_HEAD_DIM;

        float max_score = sinks[h];
        for (uint32_t r = 0; r < n_kv; r++) {
            const float *kv = kv_rows + (uint64_t)r * DS4_N_HEAD_DIM;
            score[r] = dot_f32(qh, kv, DS4_N_HEAD_DIM) * kq_scale;
            if (score[r] > max_score) max_score = score[r];
        }

        float *oh = out_heads + (uint64_t)h * DS4_N_HEAD_DIM;
        memset(oh, 0, (size_t)DS4_N_HEAD_DIM * sizeof(oh[0]));

        float denom = expf(sinks[h] - max_score);
        for (uint32_t r = 0; r < n_kv; r++) {
            const float weight = expf(score[r] - max_score);
            const float *kv = kv_rows + (uint64_t)r * DS4_N_HEAD_DIM;
            denom += weight;
            axpy_f32(oh, kv, weight, DS4_N_HEAD_DIM);
        }

        const float inv = 1.0f / denom;
        scale_f32(oh, inv, DS4_N_HEAD_DIM);
    }

    if (score != score_stack) free(score);
}

static void layer_attention_one(
        float             * out_heads,
```<｜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
0f / denom;
        scale_f32(oh, inv, DS4_N_HEAD_DIM);
    }

    if (score != score_stack) free(score);
}

static void layer_attention_one(
        float             * out_heads,
        const ds4_model   * model,
        const ds4_layer_weights * layer,
        const float       * q,
        const float       * kv) {
    layer_attention_rows_one(out_heads, model, layer, q, kv, 1);
}

/* Attention output projection is grouped: each group first maps its heads to
 * a 1024-rank low vector, then all groups are projected back to 4096. */
static void layer_grouped_out_one(
        float             * out,
        const ds4_model   * model,
        const ds4_layer_weights * layer,
        const float       * heads) {
    const uint32_t n_groups = 8;
    const uint32_t group_heads = DS4_N_HEAD / n_groups;
    const uint32_t group_dim = DS4_N_HEAD_DIM * group_heads;
    const uint32_t rank = 1024;

    float *low = xcalloc((size_t)n_groups * rank, sizeof(low[0]));

    matvec_q8_0_grouped_rows(low, model, layer->attn_output_a, heads, n_groups, group_dim, rank);

    matvec_q8_0(out, model, layer->attn_output_b, low);
    free(low);
}

static void layer_grouped_out_one_decode_scratch(
        float                  * out,
        const ds4_model        * model,
        const ds4_layer_weights * layer,
        const float            * heads,
        ds4_cpu_decode_scratch * scratch) {
    const uint32_t n_groups = 8;
    const uint32_t group_heads = DS4_N_HEAD / n_groups;
    const uint32_t group_dim = DS4_N_HEAD_DIM * group_heads;
    const uint32_t rank = 1024;

    memset(scratch->attn_low, 0, (size_t)n_groups * rank * sizeof(scratch->attn_low[0]));
    matvec_q8_0_grouped_rows_decode_scratch(scratch->attn_low, model, layer->attn_output_a,
                                            heads, n_groups, group_dim, rank, scratch);
    matvec_q8_0_decode_scratch(out, model, layer->attn_output_b, scratch->attn_low, scratch);
}

static void layer_grouped_out_batch(
        float             * out,
        const ds4_model   * model,
        const ds4_layer_weights * layer,
        const float       * heads,
        uint32_t            n_tok) {
    const uint32_t n_groups = 8;
    const uint32_t group_heads = DS4_N_HEAD / n_groups;
    const uint32_t group_dim = DS4_N_HEAD_DIM * group_heads;
    const uint32_t rank = 1024;

    float *low = xcalloc((size_t)n_tok * n_groups * rank, sizeof(low[0]));

    matmul_q8_0_grouped_batch(low, model, layer->attn_output_a, heads,
                              n_tok, n_groups, group_dim, rank);
```

<｜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
0f / denom;
        scale_f32(oh, inv, DS4_N_HEAD_DIM);
    }

    if (score != score_stack) free(score);
}

static void layer_attention_one(
        float             * out_heads,
        const ds4_model   * model,
        const ds4_layer_weights * layer,
        const float       * q,
        const float       * kv) {
    layer_attention_rows_one(out_heads, model, layer, q, kv, 1);
}

/* Attention output projection is grouped: each group first maps its heads to
 * a 1024-rank low vector, then all groups are projected back to 4096. */
static void layer_grouped_out_one(
        float             * out,
        const ds4_model   * model,
        const ds4_layer_weights * layer,
        const float       * heads) {
    const uint32_t n_groups = 8;
    const uint32_t group_heads = DS4_N_HEAD / n_groups;
    const uint32_t group_dim = DS4_N_HEAD_DIM * group_heads;
    const uint32_t rank = 1024;

    float *low = xcalloc((size_t)n_groups * rank, sizeof(low[0]));

    matvec_q8_0_grouped_rows(low, model, layer->attn_output_a, heads, n_groups, group_dim, rank);

    matvec_q8_0(out, model, layer->attn_output_b, low);
    free(low);
}

static void layer_grouped_out_one_decode_scratch(
        float                  * out,
        const ds4_model        * model,
        const ds4_layer_weights * layer,
        const float            * heads,
        ds4_cpu_decode_scratch * scratch) {
    const uint32_t n_groups = 8;
    const uint32_t group_heads = DS4_N_HEAD / n_groups;
    const uint32_t group_dim = DS4_N_HEAD_DIM * group_heads;
    const uint32_t rank = 1024;

    memset(scratch->attn_low, 0, (size_t)n_groups * rank * sizeof(scratch->attn_low[0]));
    matvec_q8_0_grouped_rows_decode_scratch(scratch->attn_low, model, layer->attn_output_a,
                                            heads, n_groups, group_dim, rank, scratch);
    matvec_q8_0_decode_scratch(out, model, layer->attn_output_b, scratch->attn_low, scratch);
}

static void layer_grouped_out_batch(
        float             * out,
        const ds4_model   * model,
        const ds4_layer_weights * layer,
        const float       * heads,
        uint32_t            n_tok) {
    const uint32_t n_groups = 8;
    const uint32_t group_heads = DS4_N_HEAD / n_groups;
    const uint32_t group_dim = DS4_N_HEAD_DIM * group_heads;
    const uint32_t rank = 1024;

    float *low = xcalloc((size_t)n_tok * n_groups * rank, sizeof(low[0]));

    matmul_q8_0_grouped_batch(low, model, layer->attn_output_a, heads,
                              n_tok, n_groups, group_dim, rank);
```<｜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
0f / denom;
        scale_f32(oh, inv, DS4_N_HEAD_DIM);
    }

    if (score != score_stack) free(score);
}

static void layer_attention_one(
        float             * out_heads,
        const ds4_model   * model,
        const ds4_layer_weights * layer,
        const float       * q,
        const float       * kv) {
    layer_attention_rows_one(out_heads, model, layer, q, kv, 1);
}

/* Attention output projection is grouped: each group first maps its heads to
 * a 1024-rank low vector, then all groups are projected back to 4096. */
static void layer_grouped_out_one(
        float             * out,
        const ds4_model   * model,
        const ds4_layer_weights * layer,
        const float       * heads) {
    const uint32_t n_groups = 8;
    const uint32_t group_heads = DS4_N_HEAD / n_groups;
    const uint32_t group_dim = DS4_N_HEAD_DIM * group_heads;
    const uint32_t rank = 1024;

    float *low = xcalloc((size_t)n_groups * rank, sizeof(low[0]));

    matvec_q8_0_grouped_rows(low, model, layer->attn_output_a, heads, n_groups, group_dim, rank);

    matvec_q8_0(out, model, layer->attn_output_b, low);
    free(low);
}

static void layer_grouped_out_one_decode_scratch(
        float                  * out,
        const ds4_model        * model,
        const ds4_layer_weights * layer,
        const float            * heads,
        ds4_cpu_decode_scratch * scratch) {
    const uint32_t n_groups = 8;
    const uint32_t group_heads = DS4_N_HEAD / n_groups;
    const uint32_t group_dim = DS4_N_HEAD_DIM * group_heads;
    const uint32_t rank = 1024;

    memset(scratch->attn_low, 0, (size_t)n_groups * rank * sizeof(scratch->attn_low[0]));
    matvec_q8_0_grouped_rows_decode_scratch(scratch->attn_low, model, layer->attn_output_a,
                                            heads, n_groups, group_dim, rank, scratch);
    matvec_q8_0_decode_scratch(out, model, layer->attn_output_b, scratch->attn_low, scratch);
}

static void layer_grouped_out_batch(
        float             * out,
        const ds4_model   * model,
        const ds4_layer_weights * layer,
        const float       * heads,
        uint32_t            n_tok) {
    const uint32_t n_groups = 8;
    const uint32_t group_heads = DS4_N_HEAD / n_groups;
    const uint32_t group_dim = DS4_N_HEAD_DIM * group_heads;
    const uint32_t rank = 1024;

    float *low = xcalloc((size_t)n_tok * n_groups * rank, sizeof(low[0]));

    matmul_q8_0_grouped_batch(low, model, layer->attn_output_a, heads,
                              n_tok, n_groups, group_dim, rank);
```

<｜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
0f / denom;
        scale_f32(oh, inv, DS4_N_HEAD_DIM);
    }

    if (score != score_stack) free(score);
}

static void layer_attention_one(
        float             * out_heads,
        const ds4_model   * model,
        const ds4_layer_weights * layer,
        const float       * q,
        const float       * kv) {
    layer_attention_rows_one(out_heads, model, layer, q, kv, 1);
}

/* Attention output projection is grouped: each group first maps its heads to
 * a 1024-rank low vector, then all groups are projected back to 4096. */
static void layer_grouped_out_one(
        float             * out,
        const ds4_model   * model,
        const ds4_layer_weights * layer,
        const float       * heads) {
    const uint32_t n_groups = 8;
    const uint32_t group_heads = DS4_N_HEAD / n_groups;
    const uint32_t group_dim = DS4_N_HEAD_DIM * group_heads;
    const uint32_t rank = 1024;

    float *low = xcalloc((size_t)n_groups * rank, sizeof(low[0]));

    matvec_q8_0_grouped_rows(low, model, layer->attn_output_a, heads, n_groups, group_dim, rank);

    matvec_q8_0(out, model, layer->attn_output_b, low);
    free(low);
}

static void layer_grouped_out_one_decode_scratch(
        float                  * out,
        const ds4_model        * model,
        const ds4_layer_weights * layer,
        const float            * heads,
        ds4_cpu_decode_scratch * scratch) {
    const uint32_t n_groups = 8;
    const uint32_t group_heads = DS4_N_HEAD / n_groups;
    const uint32_t group_dim = DS4_N_HEAD_DIM * group_heads;
    const uint32_t rank = 1024;

    memset(scratch->attn_low, 0, (size_t)n_groups * rank * sizeof(scratch->attn_low[0]));
    matvec_q8_0_grouped_rows_decode_scratch(scratch->attn_low, model, layer->attn_output_a,
                                            heads, n_groups, group_dim, rank, scratch);
    matvec_q8_0_decode_scratch(out, model, layer->attn_output_b, scratch->attn_low, scratch);
}

static void layer_grouped_out_batch(
        float             * out,
        const ds4_model   * model,
        const ds4_layer_weights * layer,
        const float       * heads,
        uint32_t            n_tok) {
    const uint32_t n_groups = 8;
    const uint32_t group_heads = DS4_N_HEAD / n_groups;
    const uint32_t group_dim = DS4_N_HEAD_DIM * group_heads;
    const uint32_t rank = 1024;

    float *low = xcalloc((size_t)n_tok * n_groups * rank, sizeof(low[0]));

    matmul_q8_0_grouped_batch(low, model, layer->attn_output_a, heads,
                              n_tok, n_groups, group_dim, rank);
```<｜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
t)n_tok * n_groups * rank, sizeof(low[0]));

    matmul_q8_0_grouped_batch(low, model, layer->attn_output_a, heads,
                              n_tok, n_groups, group_dim, rank);
    matmul_q8_0_batch(out, model, layer->attn_output_b, low, n_tok);

    free(low);
}

/* =========================================================================
 * Mixture-of-Experts FFN.
 * =========================================================================
 *
 * This is the FFN half of each layer.  It includes the shared expert, routed
 * expert selection, IQ2_XXS gate/up projections, SwiGLU, Q2_K down projection,
 * and the HC post step that returns the result to four-stream state.
 */

static float silu(float x) {
    return x * sigmoid_stable(x);
}

static float softplus_stable(float x) {
    if (x > 20.0f) return x;
    if (x < -20.0f) return expf(x);
    return log1pf(expf(x));
}

static void swiglu(float *out, const float *gate, const float *up, uint64_t n) {
    for (uint64_t i = 0; i < n; i++) {
        out[i] = silu(gate[i]) * up[i];
    }
}

/* The shared expert is a normal Q8_0 SwiGLU MLP that runs for every token. */
static void layer_shared_ffn_one(
        float             * out,
        const ds4_model   * model,
        const ds4_layer_weights * layer,
        const float       * x) {
    float *gate = xmalloc((size_t)DS4_N_FF_EXP * sizeof(gate[0]));
    float *up = xmalloc((size_t)DS4_N_FF_EXP * sizeof(up[0]));
    float *mid = xmalloc((size_t)DS4_N_FF_EXP * sizeof(mid[0]));
    const uint64_t in_dim = layer->ffn_gate_shexp->dim[0];
    const uint64_t blocks = (in_dim + 31) / 32;
    int8_t *xq = xmalloc((size_t)blocks * 32);
    float *xscale = xmalloc((size_t)blocks * sizeof(xscale[0]));

    if (layer->ffn_up_shexp->type != 8 ||
        layer->ffn_gate_shexp->type != 8 ||
        layer->ffn_up_shexp->dim[0] != in_dim) {
        ds4_die("shared expert gate/up tensors do not share a Q8_0 input layout");
    }

    quantize_q8_0_activation(x, xq, xscale, in_dim);
    matvec_q8_0_pair_prequant(gate, up, model,
                              layer->ffn_gate_shexp,
                              layer->ffn_up_shexp,
                              xq, xscale);
    swiglu(mid, gate, up, DS4_N_FF_EXP);
    matvec_q8_0(out, model, layer->ffn_down_shexp, mid);

    free(xscale);
    free(xq);
    free(mid);
    free(up);
    free(gate);
}

static void layer_shared_ffn_one_decode_scratch(
        float                  * out,
        const ds4_model        * model,
        const ds4_layer_weights * layer,
        const float            * x,
```

<｜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
t)n_tok * n_groups * rank, sizeof(low[0]));

    matmul_q8_0_grouped_batch(low, model, layer->attn_output_a, heads,
                              n_tok, n_groups, group_dim, rank);
    matmul_q8_0_batch(out, model, layer->attn_output_b, low, n_tok);

    free(low);
}

/* =========================================================================
 * Mixture-of-Experts FFN.
 * =========================================================================
 *
 * This is the FFN half of each layer.  It includes the shared expert, routed
 * expert selection, IQ2_XXS gate/up projections, SwiGLU, Q2_K down projection,
 * and the HC post step that returns the result to four-stream state.
 */

static float silu(float x) {
    return x * sigmoid_stable(x);
}

static float softplus_stable(float x) {
    if (x > 20.0f) return x;
    if (x < -20.0f) return expf(x);
    return log1pf(expf(x));
}

static void swiglu(float *out, const float *gate, const float *up, uint64_t n) {
    for (uint64_t i = 0; i < n; i++) {
        out[i] = silu(gate[i]) * up[i];
    }
}

/* The shared expert is a normal Q8_0 SwiGLU MLP that runs for every token. */
static void layer_shared_ffn_one(
        float             * out,
        const ds4_model   * model,
        const ds4_layer_weights * layer,
        const float       * x) {
    float *gate = xmalloc((size_t)DS4_N_FF_EXP * sizeof(gate[0]));
    float *up = xmalloc((size_t)DS4_N_FF_EXP * sizeof(up[0]));
    float *mid = xmalloc((size_t)DS4_N_FF_EXP * sizeof(mid[0]));
    const uint64_t in_dim = layer->ffn_gate_shexp->dim[0];
    const uint64_t blocks = (in_dim + 31) / 32;
    int8_t *xq = xmalloc((size_t)blocks * 32);
    float *xscale = xmalloc((size_t)blocks * sizeof(xscale[0]));

    if (layer->ffn_up_shexp->type != 8 ||
        layer->ffn_gate_shexp->type != 8 ||
        layer->ffn_up_shexp->dim[0] != in_dim) {
        ds4_die("shared expert gate/up tensors do not share a Q8_0 input layout");
    }

    quantize_q8_0_activation(x, xq, xscale, in_dim);
    matvec_q8_0_pair_prequant(gate, up, model,
                              layer->ffn_gate_shexp,
                              layer->ffn_up_shexp,
                              xq, xscale);
    swiglu(mid, gate, up, DS4_N_FF_EXP);
    matvec_q8_0(out, model, layer->ffn_down_shexp, mid);

    free(xscale);
    free(xq);
    free(mid);
    free(up);
    free(gate);
}

static void layer_shared_ffn_one_decode_scratch(
        float                  * out,
        const ds4_model        * model,
        const ds4_layer_weights * layer,
        const float            * x,
```<｜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
t)n_tok * n_groups * rank, sizeof(low[0]));

    matmul_q8_0_grouped_batch(low, model, layer->attn_output_a, heads,
                              n_tok, n_groups, group_dim, rank);
    matmul_q8_0_batch(out, model, layer->attn_output_b, low, n_tok);

    free(low);
}

/* =========================================================================
 * Mixture-of-Experts FFN.
 * =========================================================================
 *
 * This is the FFN half of each layer.  It includes the shared expert, routed
 * expert selection, IQ2_XXS gate/up projections, SwiGLU, Q2_K down projection,
 * and the HC post step that returns the result to four-stream state.
 */

static float silu(float x) {
    return x * sigmoid_stable(x);
}

static float softplus_stable(float x) {
    if (x > 20.0f) return x;
    if (x < -20.0f) return expf(x);
    return log1pf(expf(x));
}

static void swiglu(float *out, const float *gate, const float *up, uint64_t n) {
    for (uint64_t i = 0; i < n; i++) {
        out[i] = silu(gate[i]) * up[i];
    }
}

/* The shared expert is a normal Q8_0 SwiGLU MLP that runs for every token. */
static void layer_shared_ffn_one(
        float             * out,
        const ds4_model   * model,
        const ds4_layer_weights * layer,
        const float       * x) {
    float *gate = xmalloc((size_t)DS4_N_FF_EXP * sizeof(gate[0]));
    float *up = xmalloc((size_t)DS4_N_FF_EXP * sizeof(up[0]));
    float *mid = xmalloc((size_t)DS4_N_FF_EXP * sizeof(mid[0]));
    const uint64_t in_dim = layer->ffn_gate_shexp->dim[0];
    const uint64_t blocks = (in_dim + 31) / 32;
    int8_t *xq = xmalloc((size_t)blocks * 32);
    float *xscale = xmalloc((size_t)blocks * sizeof(xscale[0]));

    if (layer->ffn_up_shexp->type != 8 ||
        layer->ffn_gate_shexp->type != 8 ||
        layer->ffn_up_shexp->dim[0] != in_dim) {
        ds4_die("shared expert gate/up tensors do not share a Q8_0 input layout");
    }

    quantize_q8_0_activation(x, xq, xscale, in_dim);
    matvec_q8_0_pair_prequant(gate, up, model,
                              layer->ffn_gate_shexp,
                              layer->ffn_up_shexp,
                              xq, xscale);
    swiglu(mid, gate, up, DS4_N_FF_EXP);
    matvec_q8_0(out, model, layer->ffn_down_shexp, mid);

    free(xscale);
    free(xq);
    free(mid);
    free(up);
    free(gate);
}

static void layer_shared_ffn_one_decode_scratch(
        float                  * out,
        const ds4_model        * model,
        const ds4_layer_weights * layer,
        const float            * x,
```

<｜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
t)n_tok * n_groups * rank, sizeof(low[0]));

    matmul_q8_0_grouped_batch(low, model, layer->attn_output_a, heads,
                              n_tok, n_groups, group_dim, rank);
    matmul_q8_0_batch(out, model, layer->attn_output_b, low, n_tok);

    free(low);
}

/* =========================================================================
 * Mixture-of-Experts FFN.
 * =========================================================================
 *
 * This is the FFN half of each layer.  It includes the shared expert, routed
 * expert selection, IQ2_XXS gate/up projections, SwiGLU, Q2_K down projection,
 * and the HC post step that returns the result to four-stream state.
 */

static float silu(float x) {
    return x * sigmoid_stable(x);
}

static float softplus_stable(float x) {
    if (x > 20.0f) return x;
    if (x < -20.0f) return expf(x);
    return log1pf(expf(x));
}

static void swiglu(float *out, const float *gate, const float *up, uint64_t n) {
    for (uint64_t i = 0; i < n; i++) {
        out[i] = silu(gate[i]) * up[i];
    }
}

/* The shared expert is a normal Q8_0 SwiGLU MLP that runs for every token. */
static void layer_shared_ffn_one(
        float             * out,
        const ds4_model   * model,
        const ds4_layer_weights * layer,
        const float       * x) {
    float *gate = xmalloc((size_t)DS4_N_FF_EXP * sizeof(gate[0]));
    float *up = xmalloc((size_t)DS4_N_FF_EXP * sizeof(up[0]));
    float *mid = xmalloc((size_t)DS4_N_FF_EXP * sizeof(mid[0]));
    const uint64_t in_dim = layer->ffn_gate_shexp->dim[0];
    const uint64_t blocks = (in_dim + 31) / 32;
    int8_t *xq = xmalloc((size_t)blocks * 32);
    float *xscale = xmalloc((size_t)blocks * sizeof(xscale[0]));

    if (layer->ffn_up_shexp->type != 8 ||
        layer->ffn_gate_shexp->type != 8 ||
        layer->ffn_up_shexp->dim[0] != in_dim) {
        ds4_die("shared expert gate/up tensors do not share a Q8_0 input layout");
    }

    quantize_q8_0_activation(x, xq, xscale, in_dim);
    matvec_q8_0_pair_prequant(gate, up, model,
                              layer->ffn_gate_shexp,
                              layer->ffn_up_shexp,
                              xq, xscale);
    swiglu(mid, gate, up, DS4_N_FF_EXP);
    matvec_q8_0(out, model, layer->ffn_down_shexp, mid);

    free(xscale);
    free(xq);
    free(mid);
    free(up);
    free(gate);
}

static void layer_shared_ffn_one_decode_scratch(
        float                  * out,
        const ds4_model        * model,
        const ds4_layer_weights * layer,
        const float            * x,
```<｜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
n, size_t size) {
    if (size != 0 && n > SIZE_MAX / size) ds4_die("allocation size overflow");
    const size_t total = n * size;
    void *p = xmalloc(total ? total : 1);
    /*
     * This is intentionally not calloc(). Large untouched calloc ranges may be
     * represented by the VM through shared zero-page bookkeeping. The CPU decode
     * KV cache grows one token at a time, so using calloc here can move thousands
     * of first-touch faults into generation. On Darwin we have observed this end
     * in a kernel cpt_mapcnt_inc overflow panic instead of a user-space error.
     *
     * Explicitly writing the zeroes while the cache is allocated keeps those VM
     * faults out of the token loop and gives the cache private resident pages.
     */
    memset(p, 0, total);
    return p;
}

static double now_sec(void) {
    struct timespec ts;
    clock_gettime(CLOCK_MONOTONIC, &ts);
    return (double)ts.tv_sec + (double)ts.tv_nsec * 1.0e-9;
}

static const char *ds4_log_color_code(ds4_log_type type) {
    switch (type) {
    case DS4_LOG_PREFILL:
    case DS4_LOG_TIMING:
        return "\x1b[36m";
    case DS4_LOG_GENERATION:
    case DS4_LOG_OK:
        return "\x1b[32m";
    case DS4_LOG_KVCACHE:
        return "\x1b[33m";
    case DS4_LOG_TOOL:
        return "\x1b[90m";
    case DS4_LOG_WARNING:
        return "\x1b[38;5;208m";
    case DS4_LOG_ERROR:
        return "\x1b[31m";
    default:
        return "";
    }
}

bool ds4_log_is_tty(FILE *fp) {
    int fd = fileno(fp);
    return fd >= 0 && isatty(fd) != 0;
}

static void ds4_vlog(FILE *fp, ds4_log_type type, const char *fmt, va_list ap) {
    const bool colorize = type != DS4_LOG_DEFAULT && ds4_log_is_tty(fp);
    if (colorize) fputs(ds4_log_color_code(type), fp);
    vfprintf(fp, fmt, ap);
    if (colorize) fputs("\x1b[0m", fp);
}

void ds4_log(FILE *fp, ds4_log_type type, const char *fmt, ...) {
    va_list ap;
    va_start(ap, fmt);
    ds4_vlog(fp, type, fmt, ap);
    va_end(ap);
}

static bool write_f32_binary_file(const char *path, const float *data, uint64_t n) {
    FILE *fp = fopen(path, "wb");
    if (!fp) {
        fprintf(stderr, "ds4: failed to open %s for writing: %s\n", path, strerror(errno));
        return false;
    }
    const size_t nw = fwrite(data, sizeof(float), (size_t)n, fp);
    const bool ok = nw == (size_t)n && fclose(fp) == 0;
    if (!ok) {
        fprintf(stderr, "ds4: failed to write %s\n", path);
        return false;
    }
    return true;
}

static bool read_f32_binary_file(const char *path, float *data, uint64_t n) {
    struct stat st;
```

<｜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
n, size_t size) {
    if (size != 0 && n > SIZE_MAX / size) ds4_die("allocation size overflow");
    const size_t total = n * size;
    void *p = xmalloc(total ? total : 1);
    /*
     * This is intentionally not calloc(). Large untouched calloc ranges may be
     * represented by the VM through shared zero-page bookkeeping. The CPU decode
     * KV cache grows one token at a time, so using calloc here can move thousands
     * of first-touch faults into generation. On Darwin we have observed this end
     * in a kernel cpt_mapcnt_inc overflow panic instead of a user-space error.
     *
     * Explicitly writing the zeroes while the cache is allocated keeps those VM
     * faults out of the token loop and gives the cache private resident pages.
     */
    memset(p, 0, total);
    return p;
}

static double now_sec(void) {
    struct timespec ts;
    clock_gettime(CLOCK_MONOTONIC, &ts);
    return (double)ts.tv_sec + (double)ts.tv_nsec * 1.0e-9;
}

static const char *ds4_log_color_code(ds4_log_type type) {
    switch (type) {
    case DS4_LOG_PREFILL:
    case DS4_LOG_TIMING:
        return "\x1b[36m";
    case DS4_LOG_GENERATION:
    case DS4_LOG_OK:
        return "\x1b[32m";
    case DS4_LOG_KVCACHE:
        return "\x1b[33m";
    case DS4_LOG_TOOL:
        return "\x1b[90m";
    case DS4_LOG_WARNING:
        return "\x1b[38;5;208m";
    case DS4_LOG_ERROR:
        return "\x1b[31m";
    default:
        return "";
    }
}

bool ds4_log_is_tty(FILE *fp) {
    int fd = fileno(fp);
    return fd >= 0 && isatty(fd) != 0;
}

static void ds4_vlog(FILE *fp, ds4_log_type type, const char *fmt, va_list ap) {
    const bool colorize = type != DS4_LOG_DEFAULT && ds4_log_is_tty(fp);
    if (colorize) fputs(ds4_log_color_code(type), fp);
    vfprintf(fp, fmt, ap);
    if (colorize) fputs("\x1b[0m", fp);
}

void ds4_log(FILE *fp, ds4_log_type type, const char *fmt, ...) {
    va_list ap;
    va_start(ap, fmt);
    ds4_vlog(fp, type, fmt, ap);
    va_end(ap);
}

static bool write_f32_binary_file(const char *path, const float *data, uint64_t n) {
    FILE *fp = fopen(path, "wb");
    if (!fp) {
        fprintf(stderr, "ds4: failed to open %s for writing: %s\n", path, strerror(errno));
        return false;
    }
    const size_t nw = fwrite(data, sizeof(float), (size_t)n, fp);
    const bool ok = nw == (size_t)n && fclose(fp) == 0;
    if (!ok) {
        fprintf(stderr, "ds4: failed to write %s\n", path);
        return false;
    }
    return true;
}

static bool read_f32_binary_file(const char *path, float *data, uint64_t n) {
    struct stat st;
```<｜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
n, size_t size) {
    if (size != 0 && n > SIZE_MAX / size) ds4_die("allocation size overflow");
    const size_t total = n * size;
    void *p = xmalloc(total ? total : 1);
    /*
     * This is intentionally not calloc(). Large untouched calloc ranges may be
     * represented by the VM through shared zero-page bookkeeping. The CPU decode
     * KV cache grows one token at a time, so using calloc here can move thousands
     * of first-touch faults into generation. On Darwin we have observed this end
     * in a kernel cpt_mapcnt_inc overflow panic instead of a user-space error.
     *
     * Explicitly writing the zeroes while the cache is allocated keeps those VM
     * faults out of the token loop and gives the cache private resident pages.
     */
    memset(p, 0, total);
    return p;
}

static double now_sec(void) {
    struct timespec ts;
    clock_gettime(CLOCK_MONOTONIC, &ts);
    return (double)ts.tv_sec + (double)ts.tv_nsec * 1.0e-9;
}

static const char *ds4_log_color_code(ds4_log_type type) {
    switch (type) {
    case DS4_LOG_PREFILL:
    case DS4_LOG_TIMING:
        return "\x1b[36m";
    case DS4_LOG_GENERATION:
    case DS4_LOG_OK:
        return "\x1b[32m";
    case DS4_LOG_KVCACHE:
        return "\x1b[33m";
    case DS4_LOG_TOOL:
        return "\x1b[90m";
    case DS4_LOG_WARNING:
        return "\x1b[38;5;208m";
    case DS4_LOG_ERROR:
        return "\x1b[31m";
    default:
        return "";
    }
}

bool ds4_log_is_tty(FILE *fp) {
    int fd = fileno(fp);
    return fd >= 0 && isatty(fd) != 0;
}

static void ds4_vlog(FILE *fp, ds4_log_type type, const char *fmt, va_list ap) {
    const bool colorize = type != DS4_LOG_DEFAULT && ds4_log_is_tty(fp);
    if (colorize) fputs(ds4_log_color_code(type), fp);
    vfprintf(fp, fmt, ap);
    if (colorize) fputs("\x1b[0m", fp);
}

void ds4_log(FILE *fp, ds4_log_type type, const char *fmt, ...) {
    va_list ap;
    va_start(ap, fmt);
    ds4_vlog(fp, type, fmt, ap);
    va_end(ap);
}

static bool write_f32_binary_file(const char *path, const float *data, uint64_t n) {
    FILE *fp = fopen(path, "wb");
    if (!fp) {
        fprintf(stderr, "ds4: failed to open %s for writing: %s\n", path, strerror(errno));
        return false;
    }
    const size_t nw = fwrite(data, sizeof(float), (size_t)n, fp);
    const bool ok = nw == (size_t)n && fclose(fp) == 0;
    if (!ok) {
        fprintf(stderr, "ds4: failed to write %s\n", path);
        return false;
    }
    return true;
}

static bool read_f32_binary_file(const char *path, float *data, uint64_t n) {
    struct stat st;
```

<｜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
n, size_t size) {
    if (size != 0 && n > SIZE_MAX / size) ds4_die("allocation size overflow");
    const size_t total = n * size;
    void *p = xmalloc(total ? total : 1);
    /*
     * This is intentionally not calloc(). Large untouched calloc ranges may be
     * represented by the VM through shared zero-page bookkeeping. The CPU decode
     * KV cache grows one token at a time, so using calloc here can move thousands
     * of first-touch faults into generation. On Darwin we have observed this end
     * in a kernel cpt_mapcnt_inc overflow panic instead of a user-space error.
     *
     * Explicitly writing the zeroes while the cache is allocated keeps those VM
     * faults out of the token loop and gives the cache private resident pages.
     */
    memset(p, 0, total);
    return p;
}

static double now_sec(void) {
    struct timespec ts;
    clock_gettime(CLOCK_MONOTONIC, &ts);
    return (double)ts.tv_sec + (double)ts.tv_nsec * 1.0e-9;
}

static const char *ds4_log_color_code(ds4_log_type type) {
    switch (type) {
    case DS4_LOG_PREFILL:
    case DS4_LOG_TIMING:
        return "\x1b[36m";
    case DS4_LOG_GENERATION:
    case DS4_LOG_OK:
        return "\x1b[32m";
    case DS4_LOG_KVCACHE:
        return "\x1b[33m";
    case DS4_LOG_TOOL:
        return "\x1b[90m";
    case DS4_LOG_WARNING:
        return "\x1b[38;5;208m";
    case DS4_LOG_ERROR:
        return "\x1b[31m";
    default:
        return "";
    }
}

bool ds4_log_is_tty(FILE *fp) {
    int fd = fileno(fp);
    return fd >= 0 && isatty(fd) != 0;
}

static void ds4_vlog(FILE *fp, ds4_log_type type, const char *fmt, va_list ap) {
    const bool colorize = type != DS4_LOG_DEFAULT && ds4_log_is_tty(fp);
    if (colorize) fputs(ds4_log_color_code(type), fp);
    vfprintf(fp, fmt, ap);
    if (colorize) fputs("\x1b[0m", fp);
}

void ds4_log(FILE *fp, ds4_log_type type, const char *fmt, ...) {
    va_list ap;
    va_start(ap, fmt);
    ds4_vlog(fp, type, fmt, ap);
    va_end(ap);
}

static bool write_f32_binary_file(const char *path, const float *data, uint64_t n) {
    FILE *fp = fopen(path, "wb");
    if (!fp) {
        fprintf(stderr, "ds4: failed to open %s for writing: %s\n", path, strerror(errno));
        return false;
    }
    const size_t nw = fwrite(data, sizeof(float), (size_t)n, fp);
    const bool ok = nw == (size_t)n && fclose(fp) == 0;
    if (!ok) {
        fprintf(stderr, "ds4: failed to write %s\n", path);
        return false;
    }
    return true;
}

static bool read_f32_binary_file(const char *path, float *data, uint64_t n) {
    struct stat st;
```<｜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
d_ffn_one_decode_scratch(
        float                  * out,
        const ds4_model        * model,
        const ds4_layer_weights * layer,
        const float            * x,
        ds4_cpu_decode_scratch * scratch) {
    const uint64_t in_dim = layer->ffn_gate_shexp->dim[0];
    if (layer->ffn_up_shexp->type != 8 ||
        layer->ffn_gate_shexp->type != 8 ||
        layer->ffn_up_shexp->dim[0] != in_dim) {
        ds4_die("shared expert gate/up tensors do not share a Q8_0 input layout");
    }

    matvec_q8_0_pair_decode_scratch(scratch->shared_gate,
                                    scratch->shared_up,
                                    model,
                                    layer->ffn_gate_shexp,
                                    layer->ffn_up_shexp,
                                    x,
                                    scratch);
    swiglu(scratch->shared_mid, scratch->shared_gate, scratch->shared_up, DS4_N_FF_EXP);
    matvec_q8_0_decode_scratch(out, model, layer->ffn_down_shexp, scratch->shared_mid, scratch);
}

typedef struct {
    float *mid;
    const float *gate;
    const float *up;
    uint64_t n;
} swiglu_batch_ctx;

static void swiglu_batch_worker(void *vctx, uint64_t t0, uint64_t t1) {
    swiglu_batch_ctx *ctx = vctx;
    for (uint64_t t = t0; t < t1; t++) {
        swiglu(ctx->mid + t * ctx->n,
               ctx->gate + t * ctx->n,
               ctx->up + t * ctx->n,
               ctx->n);
    }
}

static void layer_shared_ffn_batch(
        float             * out,
        const ds4_model   * model,
        const ds4_layer_weights * layer,
        const float       * x,
        uint32_t            n_tok) {
    const uint64_t in_dim = layer->ffn_gate_shexp->dim[0];
    const uint64_t hidden = layer->ffn_gate_shexp->dim[1];

    if (layer->ffn_up_shexp->type != 8 ||
        layer->ffn_gate_shexp->type != 8 ||
        layer->ffn_down_shexp->type != 8 ||
        layer->ffn_up_shexp->dim[0] != in_dim ||
        layer->ffn_up_shexp->dim[1] != hidden ||
        layer->ffn_down_shexp->dim[0] != hidden) {
        ds4_die("shared expert tensors do not share the expected Q8_0 layout");
    }

    float *gate = xmalloc((size_t)n_tok * hidden * sizeof(gate[0]));
    float *up = xmalloc((size_t)n_tok * hidden * sizeof(up[0]));
    float *mid = xmalloc((size_t)n_tok * hidden * sizeof(mid[0]));

    matmul_q8_0_pair_batch(gate, up, model,
                           layer->ffn_gate_shexp,
                           layer->ffn_up_shexp,
                           x,
                           n_tok);
```

<｜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
d_ffn_one_decode_scratch(
        float                  * out,
        const ds4_model        * model,
        const ds4_layer_weights * layer,
        const float            * x,
        ds4_cpu_decode_scratch * scratch) {
    const uint64_t in_dim = layer->ffn_gate_shexp->dim[0];
    if (layer->ffn_up_shexp->type != 8 ||
        layer->ffn_gate_shexp->type != 8 ||
        layer->ffn_up_shexp->dim[0] != in_dim) {
        ds4_die("shared expert gate/up tensors do not share a Q8_0 input layout");
    }

    matvec_q8_0_pair_decode_scratch(scratch->shared_gate,
                                    scratch->shared_up,
                                    model,
                                    layer->ffn_gate_shexp,
                                    layer->ffn_up_shexp,
                                    x,
                                    scratch);
    swiglu(scratch->shared_mid, scratch->shared_gate, scratch->shared_up, DS4_N_FF_EXP);
    matvec_q8_0_decode_scratch(out, model, layer->ffn_down_shexp, scratch->shared_mid, scratch);
}

typedef struct {
    float *mid;
    const float *gate;
    const float *up;
    uint64_t n;
} swiglu_batch_ctx;

static void swiglu_batch_worker(void *vctx, uint64_t t0, uint64_t t1) {
    swiglu_batch_ctx *ctx = vctx;
    for (uint64_t t = t0; t < t1; t++) {
        swiglu(ctx->mid + t * ctx->n,
               ctx->gate + t * ctx->n,
               ctx->up + t * ctx->n,
               ctx->n);
    }
}

static void layer_shared_ffn_batch(
        float             * out,
        const ds4_model   * model,
        const ds4_layer_weights * layer,
        const float       * x,
        uint32_t            n_tok) {
    const uint64_t in_dim = layer->ffn_gate_shexp->dim[0];
    const uint64_t hidden = layer->ffn_gate_shexp->dim[1];

    if (layer->ffn_up_shexp->type != 8 ||
        layer->ffn_gate_shexp->type != 8 ||
        layer->ffn_down_shexp->type != 8 ||
        layer->ffn_up_shexp->dim[0] != in_dim ||
        layer->ffn_up_shexp->dim[1] != hidden ||
        layer->ffn_down_shexp->dim[0] != hidden) {
        ds4_die("shared expert tensors do not share the expected Q8_0 layout");
    }

    float *gate = xmalloc((size_t)n_tok * hidden * sizeof(gate[0]));
    float *up = xmalloc((size_t)n_tok * hidden * sizeof(up[0]));
    float *mid = xmalloc((size_t)n_tok * hidden * sizeof(mid[0]));

    matmul_q8_0_pair_batch(gate, up, model,
                           layer->ffn_gate_shexp,
                           layer->ffn_up_shexp,
                           x,
                           n_tok);
```<｜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
d_ffn_one_decode_scratch(
        float                  * out,
        const ds4_model        * model,
        const ds4_layer_weights * layer,
        const float            * x,
        ds4_cpu_decode_scratch * scratch) {
    const uint64_t in_dim = layer->ffn_gate_shexp->dim[0];
    if (layer->ffn_up_shexp->type != 8 ||
        layer->ffn_gate_shexp->type != 8 ||
        layer->ffn_up_shexp->dim[0] != in_dim) {
        ds4_die("shared expert gate/up tensors do not share a Q8_0 input layout");
    }

    matvec_q8_0_pair_decode_scratch(scratch->shared_gate,
                                    scratch->shared_up,
                                    model,
                                    layer->ffn_gate_shexp,
                                    layer->ffn_up_shexp,
                                    x,
                                    scratch);
    swiglu(scratch->shared_mid, scratch->shared_gate, scratch->shared_up, DS4_N_FF_EXP);
    matvec_q8_0_decode_scratch(out, model, layer->ffn_down_shexp, scratch->shared_mid, scratch);
}

typedef struct {
    float *mid;
    const float *gate;
    const float *up;
    uint64_t n;
} swiglu_batch_ctx;

static void swiglu_batch_worker(void *vctx, uint64_t t0, uint64_t t1) {
    swiglu_batch_ctx *ctx = vctx;
    for (uint64_t t = t0; t < t1; t++) {
        swiglu(ctx->mid + t * ctx->n,
               ctx->gate + t * ctx->n,
               ctx->up + t * ctx->n,
               ctx->n);
    }
}

static void layer_shared_ffn_batch(
        float             * out,
        const ds4_model   * model,
        const ds4_layer_weights * layer,
        const float       * x,
        uint32_t            n_tok) {
    const uint64_t in_dim = layer->ffn_gate_shexp->dim[0];
    const uint64_t hidden = layer->ffn_gate_shexp->dim[1];

    if (layer->ffn_up_shexp->type != 8 ||
        layer->ffn_gate_shexp->type != 8 ||
        layer->ffn_down_shexp->type != 8 ||
        layer->ffn_up_shexp->dim[0] != in_dim ||
        layer->ffn_up_shexp->dim[1] != hidden ||
        layer->ffn_down_shexp->dim[0] != hidden) {
        ds4_die("shared expert tensors do not share the expected Q8_0 layout");
    }

    float *gate = xmalloc((size_t)n_tok * hidden * sizeof(gate[0]));
    float *up = xmalloc((size_t)n_tok * hidden * sizeof(up[0]));
    float *mid = xmalloc((size_t)n_tok * hidden * sizeof(mid[0]));

    matmul_q8_0_pair_batch(gate, up, model,
                           layer->ffn_gate_shexp,
                           layer->ffn_up_shexp,
                           x,
                           n_tok);
```

<｜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
d_ffn_one_decode_scratch(
        float                  * out,
        const ds4_model        * model,
        const ds4_layer_weights * layer,
        const float            * x,
        ds4_cpu_decode_scratch * scratch) {
    const uint64_t in_dim = layer->ffn_gate_shexp->dim[0];
    if (layer->ffn_up_shexp->type != 8 ||
        layer->ffn_gate_shexp->type != 8 ||
        layer->ffn_up_shexp->dim[0] != in_dim) {
        ds4_die("shared expert gate/up tensors do not share a Q8_0 input layout");
    }

    matvec_q8_0_pair_decode_scratch(scratch->shared_gate,
                                    scratch->shared_up,
                                    model,
                                    layer->ffn_gate_shexp,
                                    layer->ffn_up_shexp,
                                    x,
                                    scratch);
    swiglu(scratch->shared_mid, scratch->shared_gate, scratch->shared_up, DS4_N_FF_EXP);
    matvec_q8_0_decode_scratch(out, model, layer->ffn_down_shexp, scratch->shared_mid, scratch);
}

typedef struct {
    float *mid;
    const float *gate;
    const float *up;
    uint64_t n;
} swiglu_batch_ctx;

static void swiglu_batch_worker(void *vctx, uint64_t t0, uint64_t t1) {
    swiglu_batch_ctx *ctx = vctx;
    for (uint64_t t = t0; t < t1; t++) {
        swiglu(ctx->mid + t * ctx->n,
               ctx->gate + t * ctx->n,
               ctx->up + t * ctx->n,
               ctx->n);
    }
}

static void layer_shared_ffn_batch(
        float             * out,
        const ds4_model   * model,
        const ds4_layer_weights * layer,
        const float       * x,
        uint32_t            n_tok) {
    const uint64_t in_dim = layer->ffn_gate_shexp->dim[0];
    const uint64_t hidden = layer->ffn_gate_shexp->dim[1];

    if (layer->ffn_up_shexp->type != 8 ||
        layer->ffn_gate_shexp->type != 8 ||
        layer->ffn_down_shexp->type != 8 ||
        layer->ffn_up_shexp->dim[0] != in_dim ||
        layer->ffn_up_shexp->dim[1] != hidden ||
        layer->ffn_down_shexp->dim[0] != hidden) {
        ds4_die("shared expert tensors do not share the expected Q8_0 layout");
    }

    float *gate = xmalloc((size_t)n_tok * hidden * sizeof(gate[0]));
    float *up = xmalloc((size_t)n_tok * hidden * sizeof(up[0]));
    float *mid = xmalloc((size_t)n_tok * hidden * sizeof(mid[0]));

    matmul_q8_0_pair_batch(gate, up, model,
                           layer->ffn_gate_shexp,
                           layer->ffn_up_shexp,
                           x,
                           n_tok);
```<｜Assistant｜><think>