[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
f (n_tokens == 0 || n_tokens > g->prefill_cap || !g->spec_logits) return false;

    const uint64_t hc_dim = (uint64_t)DS4_N_HC * DS4_N_EMBD;
    ds4_gpu_tensor *output_pre = NULL;
    ds4_gpu_tensor *output_weights = NULL;
    ds4_gpu_tensor *output_embd = NULL;
    ds4_gpu_tensor *output_norm = NULL;
    ds4_gpu_tensor *logits = NULL;

    bool ok = true;
    output_pre = ds4_gpu_tensor_view(g->batch_hc_mix,
                                       0,
                                       (uint64_t)n_tokens * DS4_N_HC * sizeof(float));
    output_weights = ds4_gpu_tensor_view(g->batch_hc_split,
                                           0,
                                           (uint64_t)n_tokens * DS4_N_HC * sizeof(float));
    output_embd = ds4_gpu_tensor_view(g->batch_ffn_cur,
                                        0,
                                        (uint64_t)n_tokens * DS4_N_EMBD * sizeof(float));
    output_norm = ds4_gpu_tensor_view(g->batch_ffn_norm,
                                        0,
                                        (uint64_t)n_tokens * DS4_N_EMBD * sizeof(float));
    logits = ds4_gpu_tensor_view(g->spec_logits,
                                   0,
                                   (uint64_t)n_tokens * vocab_dim * sizeof(float));
    ok = output_pre && output_weights && output_embd && output_norm && logits;

    if (ok) ok = ds4_gpu_rms_norm_plain_rows_tensor(g->batch_flat_hc,
                                                      g->batch_cur_hc,
                                                      (uint32_t)hc_dim,
                                                      n_tokens,
                                                      DS4_RMS_EPS) != 0;
    if (ok) ok = ds4_gpu_matmul_f16_tensor(output_pre,
                                             model->map,
                                             model->size,
                                             weights->output_hc_fn->abs_offset,
                                             hc_dim,
                                             DS4_N_HC,
                                             g->batch_flat_hc,
                                             n_tokens) != 0;
    if (ok) ok = ds4_gpu_output_hc_weights_tensor(output_weights,
                                                    output_pre,
                                                    model->map,
                                                    model->size,
                                                    weights->output_hc_scale->abs_offset,
```

<｜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
f (n_tokens == 0 || n_tokens > g->prefill_cap || !g->spec_logits) return false;

    const uint64_t hc_dim = (uint64_t)DS4_N_HC * DS4_N_EMBD;
    ds4_gpu_tensor *output_pre = NULL;
    ds4_gpu_tensor *output_weights = NULL;
    ds4_gpu_tensor *output_embd = NULL;
    ds4_gpu_tensor *output_norm = NULL;
    ds4_gpu_tensor *logits = NULL;

    bool ok = true;
    output_pre = ds4_gpu_tensor_view(g->batch_hc_mix,
                                       0,
                                       (uint64_t)n_tokens * DS4_N_HC * sizeof(float));
    output_weights = ds4_gpu_tensor_view(g->batch_hc_split,
                                           0,
                                           (uint64_t)n_tokens * DS4_N_HC * sizeof(float));
    output_embd = ds4_gpu_tensor_view(g->batch_ffn_cur,
                                        0,
                                        (uint64_t)n_tokens * DS4_N_EMBD * sizeof(float));
    output_norm = ds4_gpu_tensor_view(g->batch_ffn_norm,
                                        0,
                                        (uint64_t)n_tokens * DS4_N_EMBD * sizeof(float));
    logits = ds4_gpu_tensor_view(g->spec_logits,
                                   0,
                                   (uint64_t)n_tokens * vocab_dim * sizeof(float));
    ok = output_pre && output_weights && output_embd && output_norm && logits;

    if (ok) ok = ds4_gpu_rms_norm_plain_rows_tensor(g->batch_flat_hc,
                                                      g->batch_cur_hc,
                                                      (uint32_t)hc_dim,
                                                      n_tokens,
                                                      DS4_RMS_EPS) != 0;
    if (ok) ok = ds4_gpu_matmul_f16_tensor(output_pre,
                                             model->map,
                                             model->size,
                                             weights->output_hc_fn->abs_offset,
                                             hc_dim,
                                             DS4_N_HC,
                                             g->batch_flat_hc,
                                             n_tokens) != 0;
    if (ok) ok = ds4_gpu_output_hc_weights_tensor(output_weights,
                                                    output_pre,
                                                    model->map,
                                                    model->size,
                                                    weights->output_hc_scale->abs_offset,
```<｜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
model->map,
                                                    model->size,
                                                    weights->output_hc_scale->abs_offset,
                                                    weights->output_hc_base->abs_offset,
                                                    DS4_N_HC,
                                                    DS4_HC_EPS) != 0;
    if (ok) ok = ds4_gpu_hc_weighted_sum_tensor(output_embd,
                                                  g->batch_cur_hc,
                                                  output_weights,
                                                  DS4_N_EMBD,
                                                  DS4_N_HC) != 0;
    if (ok) ok = ds4_gpu_rms_norm_weight_rows_tensor(output_norm,
                                                       output_embd,
                                                       model->map,
                                                       model->size,
                                                       weights->output_norm->abs_offset,
                                                       DS4_N_EMBD,
                                                       n_tokens,
                                                       DS4_RMS_EPS) != 0;
    if (ok) ok = ds4_gpu_matmul_q8_0_tensor(logits,
                                              model->map,
                                              model->size,
                                              weights->output->abs_offset,
                                              DS4_N_EMBD,
                                              vocab_dim,
                                              output_norm,
                                              n_tokens) != 0;

    ds4_gpu_tensor_free(logits);
    ds4_gpu_tensor_free(output_norm);
    ds4_gpu_tensor_free(output_embd);
    ds4_gpu_tensor_free(output_weights);
    ds4_gpu_tensor_free(output_pre);
    return ok;
}

static bool metal_graph_matmul_plain_tensor(
        ds4_gpu_tensor       *out,
        const ds4_model        *model,
        const ds4_tensor       *w,
        uint64_t                in_dim,
        uint64_t                out_dim,
        const ds4_gpu_tensor *x,
        uint64_t                n_tok) {
    if (w->type == DS4_TENSOR_F16) {
        return ds4_gpu_matmul_f16_tensor(out, model->map, model->size,
                                           w->abs_offset, in_dim, out_dim, x, n_tok) != 0;
    }
    if (w->type == DS4_TENSOR_F32) {
        return ds4_gpu_matmul_f32_tensor(out, model->map, model->size,
```

<｜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
model->map,
                                                    model->size,
                                                    weights->output_hc_scale->abs_offset,
                                                    weights->output_hc_base->abs_offset,
                                                    DS4_N_HC,
                                                    DS4_HC_EPS) != 0;
    if (ok) ok = ds4_gpu_hc_weighted_sum_tensor(output_embd,
                                                  g->batch_cur_hc,
                                                  output_weights,
                                                  DS4_N_EMBD,
                                                  DS4_N_HC) != 0;
    if (ok) ok = ds4_gpu_rms_norm_weight_rows_tensor(output_norm,
                                                       output_embd,
                                                       model->map,
                                                       model->size,
                                                       weights->output_norm->abs_offset,
                                                       DS4_N_EMBD,
                                                       n_tokens,
                                                       DS4_RMS_EPS) != 0;
    if (ok) ok = ds4_gpu_matmul_q8_0_tensor(logits,
                                              model->map,
                                              model->size,
                                              weights->output->abs_offset,
                                              DS4_N_EMBD,
                                              vocab_dim,
                                              output_norm,
                                              n_tokens) != 0;

    ds4_gpu_tensor_free(logits);
    ds4_gpu_tensor_free(output_norm);
    ds4_gpu_tensor_free(output_embd);
    ds4_gpu_tensor_free(output_weights);
    ds4_gpu_tensor_free(output_pre);
    return ok;
}

static bool metal_graph_matmul_plain_tensor(
        ds4_gpu_tensor       *out,
        const ds4_model        *model,
        const ds4_tensor       *w,
        uint64_t                in_dim,
        uint64_t                out_dim,
        const ds4_gpu_tensor *x,
        uint64_t                n_tok) {
    if (w->type == DS4_TENSOR_F16) {
        return ds4_gpu_matmul_f16_tensor(out, model->map, model->size,
                                           w->abs_offset, in_dim, out_dim, x, n_tok) != 0;
    }
    if (w->type == DS4_TENSOR_F32) {
        return ds4_gpu_matmul_f32_tensor(out, model->map, model->size,
```<｜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
model->map,
                                                    model->size,
                                                    weights->output_hc_scale->abs_offset,
                                                    weights->output_hc_base->abs_offset,
                                                    DS4_N_HC,
                                                    DS4_HC_EPS) != 0;
    if (ok) ok = ds4_gpu_hc_weighted_sum_tensor(output_embd,
                                                  g->batch_cur_hc,
                                                  output_weights,
                                                  DS4_N_EMBD,
                                                  DS4_N_HC) != 0;
    if (ok) ok = ds4_gpu_rms_norm_weight_rows_tensor(output_norm,
                                                       output_embd,
                                                       model->map,
                                                       model->size,
                                                       weights->output_norm->abs_offset,
                                                       DS4_N_EMBD,
                                                       n_tokens,
                                                       DS4_RMS_EPS) != 0;
    if (ok) ok = ds4_gpu_matmul_q8_0_tensor(logits,
                                              model->map,
                                              model->size,
                                              weights->output->abs_offset,
                                              DS4_N_EMBD,
                                              vocab_dim,
                                              output_norm,
                                              n_tokens) != 0;

    ds4_gpu_tensor_free(logits);
    ds4_gpu_tensor_free(output_norm);
    ds4_gpu_tensor_free(output_embd);
    ds4_gpu_tensor_free(output_weights);
    ds4_gpu_tensor_free(output_pre);
    return ok;
}

static bool metal_graph_matmul_plain_tensor(
        ds4_gpu_tensor       *out,
        const ds4_model        *model,
        const ds4_tensor       *w,
        uint64_t                in_dim,
        uint64_t                out_dim,
        const ds4_gpu_tensor *x,
        uint64_t                n_tok) {
    if (w->type == DS4_TENSOR_F16) {
        return ds4_gpu_matmul_f16_tensor(out, model->map, model->size,
                                           w->abs_offset, in_dim, out_dim, x, n_tok) != 0;
    }
    if (w->type == DS4_TENSOR_F32) {
        return ds4_gpu_matmul_f32_tensor(out, model->map, model->size,
```

<｜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
model->map,
                                                    model->size,
                                                    weights->output_hc_scale->abs_offset,
                                                    weights->output_hc_base->abs_offset,
                                                    DS4_N_HC,
                                                    DS4_HC_EPS) != 0;
    if (ok) ok = ds4_gpu_hc_weighted_sum_tensor(output_embd,
                                                  g->batch_cur_hc,
                                                  output_weights,
                                                  DS4_N_EMBD,
                                                  DS4_N_HC) != 0;
    if (ok) ok = ds4_gpu_rms_norm_weight_rows_tensor(output_norm,
                                                       output_embd,
                                                       model->map,
                                                       model->size,
                                                       weights->output_norm->abs_offset,
                                                       DS4_N_EMBD,
                                                       n_tokens,
                                                       DS4_RMS_EPS) != 0;
    if (ok) ok = ds4_gpu_matmul_q8_0_tensor(logits,
                                              model->map,
                                              model->size,
                                              weights->output->abs_offset,
                                              DS4_N_EMBD,
                                              vocab_dim,
                                              output_norm,
                                              n_tokens) != 0;

    ds4_gpu_tensor_free(logits);
    ds4_gpu_tensor_free(output_norm);
    ds4_gpu_tensor_free(output_embd);
    ds4_gpu_tensor_free(output_weights);
    ds4_gpu_tensor_free(output_pre);
    return ok;
}

static bool metal_graph_matmul_plain_tensor(
        ds4_gpu_tensor       *out,
        const ds4_model        *model,
        const ds4_tensor       *w,
        uint64_t                in_dim,
        uint64_t                out_dim,
        const ds4_gpu_tensor *x,
        uint64_t                n_tok) {
    if (w->type == DS4_TENSOR_F16) {
        return ds4_gpu_matmul_f16_tensor(out, model->map, model->size,
                                           w->abs_offset, in_dim, out_dim, x, n_tok) != 0;
    }
    if (w->type == DS4_TENSOR_F32) {
        return ds4_gpu_matmul_f32_tensor(out, model->map, model->size,
```<｜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
w->abs_offset, in_dim, out_dim, x, n_tok) != 0;
    }
    if (w->type == DS4_TENSOR_F32) {
        return ds4_gpu_matmul_f32_tensor(out, model->map, model->size,
                                           w->abs_offset, in_dim, out_dim, x, n_tok) != 0;
    }
    fprintf(stderr, "ds4: Metal plain matmul does not support %s\n", tensor_type_name(w->type));
    return false;
}

static bool metal_graph_encode_output_head_mtp(
        ds4_gpu_graph       *g,
        const ds4_model       *base_model,
        const ds4_weights     *base_weights,
        const ds4_model       *mtp_model,
        const ds4_mtp_weights *mtp,
        uint64_t               vocab_dim) {
    const uint64_t hc_dim = (uint64_t)DS4_N_HC * DS4_N_EMBD;
    bool ok = ds4_gpu_rms_norm_plain_tensor(g->flat_hc, g->cur_hc, (uint32_t)hc_dim, DS4_RMS_EPS) != 0;
    if (ok) ok = metal_graph_matmul_plain_tensor(g->output_pre, mtp_model, mtp->hc_head_fn,
                                                 hc_dim, DS4_N_HC, g->flat_hc, 1);
    if (ok) ok = ds4_gpu_output_hc_weights_tensor(g->output_weights,
                                                    g->output_pre,
                                                    mtp_model->map,
                                                    mtp_model->size,
                                                    mtp->hc_head_scale->abs_offset,
                                                    mtp->hc_head_base->abs_offset,
                                                    DS4_N_HC,
                                                    DS4_HC_EPS) != 0;
    if (ok) ok = ds4_gpu_hc_weighted_sum_tensor(g->output_embd,
                                                  g->cur_hc,
                                                  g->output_weights,
                                                  DS4_N_EMBD,
                                                  DS4_N_HC) != 0;
    if (ok) ok = ds4_gpu_rms_norm_weight_tensor(g->output_norm,
                                                  g->output_embd,
                                                  mtp_model->map,
                                                  mtp_model->size,
                                                  mtp->norm->abs_offset,
                                                  DS4_N_EMBD,
                                                  DS4_RMS_EPS) != 0;
    if (ok) ok = ds4_gpu_matmul_q8_0_tensor(g->logits,
                                              base_model->map,
                                              base_model->size,
```

<｜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
w->abs_offset, in_dim, out_dim, x, n_tok) != 0;
    }
    if (w->type == DS4_TENSOR_F32) {
        return ds4_gpu_matmul_f32_tensor(out, model->map, model->size,
                                           w->abs_offset, in_dim, out_dim, x, n_tok) != 0;
    }
    fprintf(stderr, "ds4: Metal plain matmul does not support %s\n", tensor_type_name(w->type));
    return false;
}

static bool metal_graph_encode_output_head_mtp(
        ds4_gpu_graph       *g,
        const ds4_model       *base_model,
        const ds4_weights     *base_weights,
        const ds4_model       *mtp_model,
        const ds4_mtp_weights *mtp,
        uint64_t               vocab_dim) {
    const uint64_t hc_dim = (uint64_t)DS4_N_HC * DS4_N_EMBD;
    bool ok = ds4_gpu_rms_norm_plain_tensor(g->flat_hc, g->cur_hc, (uint32_t)hc_dim, DS4_RMS_EPS) != 0;
    if (ok) ok = metal_graph_matmul_plain_tensor(g->output_pre, mtp_model, mtp->hc_head_fn,
                                                 hc_dim, DS4_N_HC, g->flat_hc, 1);
    if (ok) ok = ds4_gpu_output_hc_weights_tensor(g->output_weights,
                                                    g->output_pre,
                                                    mtp_model->map,
                                                    mtp_model->size,
                                                    mtp->hc_head_scale->abs_offset,
                                                    mtp->hc_head_base->abs_offset,
                                                    DS4_N_HC,
                                                    DS4_HC_EPS) != 0;
    if (ok) ok = ds4_gpu_hc_weighted_sum_tensor(g->output_embd,
                                                  g->cur_hc,
                                                  g->output_weights,
                                                  DS4_N_EMBD,
                                                  DS4_N_HC) != 0;
    if (ok) ok = ds4_gpu_rms_norm_weight_tensor(g->output_norm,
                                                  g->output_embd,
                                                  mtp_model->map,
                                                  mtp_model->size,
                                                  mtp->norm->abs_offset,
                                                  DS4_N_EMBD,
                                                  DS4_RMS_EPS) != 0;
    if (ok) ok = ds4_gpu_matmul_q8_0_tensor(g->logits,
                                              base_model->map,
                                              base_model->size,
```<｜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
w->abs_offset, in_dim, out_dim, x, n_tok) != 0;
    }
    if (w->type == DS4_TENSOR_F32) {
        return ds4_gpu_matmul_f32_tensor(out, model->map, model->size,
                                           w->abs_offset, in_dim, out_dim, x, n_tok) != 0;
    }
    fprintf(stderr, "ds4: Metal plain matmul does not support %s\n", tensor_type_name(w->type));
    return false;
}

static bool metal_graph_encode_output_head_mtp(
        ds4_gpu_graph       *g,
        const ds4_model       *base_model,
        const ds4_weights     *base_weights,
        const ds4_model       *mtp_model,
        const ds4_mtp_weights *mtp,
        uint64_t               vocab_dim) {
    const uint64_t hc_dim = (uint64_t)DS4_N_HC * DS4_N_EMBD;
    bool ok = ds4_gpu_rms_norm_plain_tensor(g->flat_hc, g->cur_hc, (uint32_t)hc_dim, DS4_RMS_EPS) != 0;
    if (ok) ok = metal_graph_matmul_plain_tensor(g->output_pre, mtp_model, mtp->hc_head_fn,
                                                 hc_dim, DS4_N_HC, g->flat_hc, 1);
    if (ok) ok = ds4_gpu_output_hc_weights_tensor(g->output_weights,
                                                    g->output_pre,
                                                    mtp_model->map,
                                                    mtp_model->size,
                                                    mtp->hc_head_scale->abs_offset,
                                                    mtp->hc_head_base->abs_offset,
                                                    DS4_N_HC,
                                                    DS4_HC_EPS) != 0;
    if (ok) ok = ds4_gpu_hc_weighted_sum_tensor(g->output_embd,
                                                  g->cur_hc,
                                                  g->output_weights,
                                                  DS4_N_EMBD,
                                                  DS4_N_HC) != 0;
    if (ok) ok = ds4_gpu_rms_norm_weight_tensor(g->output_norm,
                                                  g->output_embd,
                                                  mtp_model->map,
                                                  mtp_model->size,
                                                  mtp->norm->abs_offset,
                                                  DS4_N_EMBD,
                                                  DS4_RMS_EPS) != 0;
    if (ok) ok = ds4_gpu_matmul_q8_0_tensor(g->logits,
                                              base_model->map,
                                              base_model->size,
```

<｜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
w->abs_offset, in_dim, out_dim, x, n_tok) != 0;
    }
    if (w->type == DS4_TENSOR_F32) {
        return ds4_gpu_matmul_f32_tensor(out, model->map, model->size,
                                           w->abs_offset, in_dim, out_dim, x, n_tok) != 0;
    }
    fprintf(stderr, "ds4: Metal plain matmul does not support %s\n", tensor_type_name(w->type));
    return false;
}

static bool metal_graph_encode_output_head_mtp(
        ds4_gpu_graph       *g,
        const ds4_model       *base_model,
        const ds4_weights     *base_weights,
        const ds4_model       *mtp_model,
        const ds4_mtp_weights *mtp,
        uint64_t               vocab_dim) {
    const uint64_t hc_dim = (uint64_t)DS4_N_HC * DS4_N_EMBD;
    bool ok = ds4_gpu_rms_norm_plain_tensor(g->flat_hc, g->cur_hc, (uint32_t)hc_dim, DS4_RMS_EPS) != 0;
    if (ok) ok = metal_graph_matmul_plain_tensor(g->output_pre, mtp_model, mtp->hc_head_fn,
                                                 hc_dim, DS4_N_HC, g->flat_hc, 1);
    if (ok) ok = ds4_gpu_output_hc_weights_tensor(g->output_weights,
                                                    g->output_pre,
                                                    mtp_model->map,
                                                    mtp_model->size,
                                                    mtp->hc_head_scale->abs_offset,
                                                    mtp->hc_head_base->abs_offset,
                                                    DS4_N_HC,
                                                    DS4_HC_EPS) != 0;
    if (ok) ok = ds4_gpu_hc_weighted_sum_tensor(g->output_embd,
                                                  g->cur_hc,
                                                  g->output_weights,
                                                  DS4_N_EMBD,
                                                  DS4_N_HC) != 0;
    if (ok) ok = ds4_gpu_rms_norm_weight_tensor(g->output_norm,
                                                  g->output_embd,
                                                  mtp_model->map,
                                                  mtp_model->size,
                                                  mtp->norm->abs_offset,
                                                  DS4_N_EMBD,
                                                  DS4_RMS_EPS) != 0;
    if (ok) ok = ds4_gpu_matmul_q8_0_tensor(g->logits,
                                              base_model->map,
                                              base_model->size,
```<｜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
if (ok) ok = ds4_gpu_matmul_q8_0_tensor(g->logits,
                                              base_model->map,
                                              base_model->size,
                                              base_weights->output->abs_offset,
                                              DS4_N_EMBD,
                                              vocab_dim,
                                              g->output_norm,
                                              1) != 0;
    return ok;
}

/* =========================================================================
 * Metal Diagnostic Comparisons.
 * =========================================================================
 *
 * These routines deliberately allocate CPU-side reference buffers and read
 * Metal tensors back.  They are not part of generation; command-line tests use
 * them to localize drift against the C reference pipeline.
 */

static void metal_graph_trace_layer_stages(
        ds4_gpu_graph  *g,
        const ds4_model        *model,
        const ds4_layer_weights *layer,
        const float            *cpu_in_hc,
        uint32_t                il,
        int                     token) {
    const uint64_t hc_dim = (uint64_t)DS4_N_HC * DS4_N_EMBD;
    const uint64_t q_rank = layer->attn_q_a->dim[1];
    const uint64_t q_dim = (uint64_t)DS4_N_HEAD * DS4_N_HEAD_DIM;
    const uint64_t shared_in_dim = layer->ffn_gate_shexp->dim[0];
    const uint64_t shared_dim = layer->ffn_gate_shexp->dim[1];
    const uint64_t expert_in_dim = layer->ffn_gate_exps->dim[0];
    const uint64_t down_in_dim = layer->ffn_down_exps->dim[0];

    float *cpu_attn_cur = xmalloc((size_t)DS4_N_EMBD * sizeof(float));
    float *cpu_attn_norm = xmalloc((size_t)DS4_N_EMBD * sizeof(float));
    float *cpu_q = xmalloc((size_t)q_dim * sizeof(float));
    float *cpu_qr_norm = xmalloc((size_t)q_rank * sizeof(float));
    float *cpu_kv = xmalloc((size_t)DS4_N_HEAD_DIM * sizeof(float));
    float *cpu_heads = xmalloc((size_t)q_dim * sizeof(float));
    float *cpu_attn_out = xmalloc((size_t)DS4_N_EMBD * sizeof(float));
    float *cpu_after_attn_hc = xmalloc((size_t)hc_dim * sizeof(float));
    float *cpu_ffn_cur = xmalloc((size_t)DS4_N_EMBD * sizeof(float));
    float *cpu_ffn_norm = xmalloc((size_t)DS4_N_EMBD * sizeof(float));
    float *cpu_shared_gate = xmalloc((size_t)shared_dim * sizeof(float));
    float *cpu_shared_up = xmalloc((size_t)shared_dim * sizeof(float));
    float *cpu_shared_mid = xmalloc((size_t)shared_dim * sizeof(float));
```

<｜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
if (ok) ok = ds4_gpu_matmul_q8_0_tensor(g->logits,
                                              base_model->map,
                                              base_model->size,
                                              base_weights->output->abs_offset,
                                              DS4_N_EMBD,
                                              vocab_dim,
                                              g->output_norm,
                                              1) != 0;
    return ok;
}

/* =========================================================================
 * Metal Diagnostic Comparisons.
 * =========================================================================
 *
 * These routines deliberately allocate CPU-side reference buffers and read
 * Metal tensors back.  They are not part of generation; command-line tests use
 * them to localize drift against the C reference pipeline.
 */

static void metal_graph_trace_layer_stages(
        ds4_gpu_graph  *g,
        const ds4_model        *model,
        const ds4_layer_weights *layer,
        const float            *cpu_in_hc,
        uint32_t                il,
        int                     token) {
    const uint64_t hc_dim = (uint64_t)DS4_N_HC * DS4_N_EMBD;
    const uint64_t q_rank = layer->attn_q_a->dim[1];
    const uint64_t q_dim = (uint64_t)DS4_N_HEAD * DS4_N_HEAD_DIM;
    const uint64_t shared_in_dim = layer->ffn_gate_shexp->dim[0];
    const uint64_t shared_dim = layer->ffn_gate_shexp->dim[1];
    const uint64_t expert_in_dim = layer->ffn_gate_exps->dim[0];
    const uint64_t down_in_dim = layer->ffn_down_exps->dim[0];

    float *cpu_attn_cur = xmalloc((size_t)DS4_N_EMBD * sizeof(float));
    float *cpu_attn_norm = xmalloc((size_t)DS4_N_EMBD * sizeof(float));
    float *cpu_q = xmalloc((size_t)q_dim * sizeof(float));
    float *cpu_qr_norm = xmalloc((size_t)q_rank * sizeof(float));
    float *cpu_kv = xmalloc((size_t)DS4_N_HEAD_DIM * sizeof(float));
    float *cpu_heads = xmalloc((size_t)q_dim * sizeof(float));
    float *cpu_attn_out = xmalloc((size_t)DS4_N_EMBD * sizeof(float));
    float *cpu_after_attn_hc = xmalloc((size_t)hc_dim * sizeof(float));
    float *cpu_ffn_cur = xmalloc((size_t)DS4_N_EMBD * sizeof(float));
    float *cpu_ffn_norm = xmalloc((size_t)DS4_N_EMBD * sizeof(float));
    float *cpu_shared_gate = xmalloc((size_t)shared_dim * sizeof(float));
    float *cpu_shared_up = xmalloc((size_t)shared_dim * sizeof(float));
    float *cpu_shared_mid = xmalloc((size_t)shared_dim * sizeof(float));
```<｜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
if (ok) ok = ds4_gpu_matmul_q8_0_tensor(g->logits,
                                              base_model->map,
                                              base_model->size,
                                              base_weights->output->abs_offset,
                                              DS4_N_EMBD,
                                              vocab_dim,
                                              g->output_norm,
                                              1) != 0;
    return ok;
}

/* =========================================================================
 * Metal Diagnostic Comparisons.
 * =========================================================================
 *
 * These routines deliberately allocate CPU-side reference buffers and read
 * Metal tensors back.  They are not part of generation; command-line tests use
 * them to localize drift against the C reference pipeline.
 */

static void metal_graph_trace_layer_stages(
        ds4_gpu_graph  *g,
        const ds4_model        *model,
        const ds4_layer_weights *layer,
        const float            *cpu_in_hc,
        uint32_t                il,
        int                     token) {
    const uint64_t hc_dim = (uint64_t)DS4_N_HC * DS4_N_EMBD;
    const uint64_t q_rank = layer->attn_q_a->dim[1];
    const uint64_t q_dim = (uint64_t)DS4_N_HEAD * DS4_N_HEAD_DIM;
    const uint64_t shared_in_dim = layer->ffn_gate_shexp->dim[0];
    const uint64_t shared_dim = layer->ffn_gate_shexp->dim[1];
    const uint64_t expert_in_dim = layer->ffn_gate_exps->dim[0];
    const uint64_t down_in_dim = layer->ffn_down_exps->dim[0];

    float *cpu_attn_cur = xmalloc((size_t)DS4_N_EMBD * sizeof(float));
    float *cpu_attn_norm = xmalloc((size_t)DS4_N_EMBD * sizeof(float));
    float *cpu_q = xmalloc((size_t)q_dim * sizeof(float));
    float *cpu_qr_norm = xmalloc((size_t)q_rank * sizeof(float));
    float *cpu_kv = xmalloc((size_t)DS4_N_HEAD_DIM * sizeof(float));
    float *cpu_heads = xmalloc((size_t)q_dim * sizeof(float));
    float *cpu_attn_out = xmalloc((size_t)DS4_N_EMBD * sizeof(float));
    float *cpu_after_attn_hc = xmalloc((size_t)hc_dim * sizeof(float));
    float *cpu_ffn_cur = xmalloc((size_t)DS4_N_EMBD * sizeof(float));
    float *cpu_ffn_norm = xmalloc((size_t)DS4_N_EMBD * sizeof(float));
    float *cpu_shared_gate = xmalloc((size_t)shared_dim * sizeof(float));
    float *cpu_shared_up = xmalloc((size_t)shared_dim * sizeof(float));
    float *cpu_shared_mid = xmalloc((size_t)shared_dim * sizeof(float));
```

<｜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
if (ok) ok = ds4_gpu_matmul_q8_0_tensor(g->logits,
                                              base_model->map,
                                              base_model->size,
                                              base_weights->output->abs_offset,
                                              DS4_N_EMBD,
                                              vocab_dim,
                                              g->output_norm,
                                              1) != 0;
    return ok;
}

/* =========================================================================
 * Metal Diagnostic Comparisons.
 * =========================================================================
 *
 * These routines deliberately allocate CPU-side reference buffers and read
 * Metal tensors back.  They are not part of generation; command-line tests use
 * them to localize drift against the C reference pipeline.
 */

static void metal_graph_trace_layer_stages(
        ds4_gpu_graph  *g,
        const ds4_model        *model,
        const ds4_layer_weights *layer,
        const float            *cpu_in_hc,
        uint32_t                il,
        int                     token) {
    const uint64_t hc_dim = (uint64_t)DS4_N_HC * DS4_N_EMBD;
    const uint64_t q_rank = layer->attn_q_a->dim[1];
    const uint64_t q_dim = (uint64_t)DS4_N_HEAD * DS4_N_HEAD_DIM;
    const uint64_t shared_in_dim = layer->ffn_gate_shexp->dim[0];
    const uint64_t shared_dim = layer->ffn_gate_shexp->dim[1];
    const uint64_t expert_in_dim = layer->ffn_gate_exps->dim[0];
    const uint64_t down_in_dim = layer->ffn_down_exps->dim[0];

    float *cpu_attn_cur = xmalloc((size_t)DS4_N_EMBD * sizeof(float));
    float *cpu_attn_norm = xmalloc((size_t)DS4_N_EMBD * sizeof(float));
    float *cpu_q = xmalloc((size_t)q_dim * sizeof(float));
    float *cpu_qr_norm = xmalloc((size_t)q_rank * sizeof(float));
    float *cpu_kv = xmalloc((size_t)DS4_N_HEAD_DIM * sizeof(float));
    float *cpu_heads = xmalloc((size_t)q_dim * sizeof(float));
    float *cpu_attn_out = xmalloc((size_t)DS4_N_EMBD * sizeof(float));
    float *cpu_after_attn_hc = xmalloc((size_t)hc_dim * sizeof(float));
    float *cpu_ffn_cur = xmalloc((size_t)DS4_N_EMBD * sizeof(float));
    float *cpu_ffn_norm = xmalloc((size_t)DS4_N_EMBD * sizeof(float));
    float *cpu_shared_gate = xmalloc((size_t)shared_dim * sizeof(float));
    float *cpu_shared_up = xmalloc((size_t)shared_dim * sizeof(float));
    float *cpu_shared_mid = xmalloc((size_t)shared_dim * sizeof(float));
```<｜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
size_t)shared_dim * sizeof(float));
    float *cpu_shared_up = xmalloc((size_t)shared_dim * sizeof(float));
    float *cpu_shared_mid = xmalloc((size_t)shared_dim * sizeof(float));
    float *cpu_shared = xmalloc((size_t)DS4_N_EMBD * sizeof(float));
    float *cpu_routed = xmalloc((size_t)DS4_N_EMBD * sizeof(float));
    float *cpu_ffn_out = xmalloc((size_t)DS4_N_EMBD * sizeof(float));
    float *cpu_after_ffn_hc = xmalloc((size_t)hc_dim * sizeof(float));
    float post[4];
    float comb[16];
    float ffn_post[4];
    float ffn_comb[16];
    int selected[DS4_N_EXPERT_USED];
    float expert_weight[DS4_N_EXPERT_USED];
    const uint64_t shared_blocks = (shared_in_dim + 31) / 32;
    int8_t *shared_xq = xmalloc((size_t)shared_blocks * 32);
    float *shared_xscale = xmalloc((size_t)shared_blocks * sizeof(float));
    float *routed_mid_all = xmalloc((size_t)DS4_N_EXPERT_USED * down_in_dim * sizeof(float));
    block_q8_K *routed_xq = xmalloc((size_t)(expert_in_dim / QK_K) * sizeof(block_q8_K));
    block_q8_K *routed_midq = xmalloc((size_t)DS4_N_EXPERT_USED * (down_in_dim / QK_K) * sizeof(block_q8_K));

    hc_pre_from_state_one(model,
                          layer->hc_attn_fn,
                          layer->hc_attn_scale,
                          layer->hc_attn_base,
                          cpu_in_hc, cpu_attn_cur, post, comb);
    layer_attn_norm_one(cpu_attn_norm, model, layer, cpu_attn_cur);
    layer_q_projection_with_lora_one(model, layer, cpu_attn_norm, cpu_q, cpu_qr_norm);
    layer_kv_projection_normed_one(model, layer, cpu_attn_norm, cpu_kv);
    rope_tail_layer_inplace(cpu_q, DS4_N_HEAD, DS4_N_HEAD_DIM, DS4_N_ROT, 0, il, false);
    rope_tail_layer_inplace(cpu_kv, DS4_N_HEAD_KV, DS4_N_HEAD_DIM, DS4_N_ROT, 0, il, false);
    dsv4_fp8_kv_quantize_row_inplace_cpu(cpu_kv, DS4_N_HEAD_DIM, DS4_N_ROT);
    f16_round_inplace_cpu(cpu_kv, DS4_N_HEAD_DIM);
    layer_attention_one(cpu_heads, model, layer, cpu_q, cpu_kv);
    rope_tail_layer_inplace(cpu_heads, DS4_N_HEAD, DS4_N_HEAD_DIM, DS4_N_ROT, 0, il, true);
    layer_grouped_out_one(cpu_attn_out, model, layer, cpu_heads);
    hc_post_one(cpu_after_attn_hc, cpu_attn_out, cpu_in_hc, post, comb, DS4_N_EMBD, DS4_N_HC);
    hc_pre_from_state_one(model,
                          layer->hc_ffn_fn,
                          layer->hc_ffn_scale,
                          layer->hc_ffn_base,
                          cpu_after_attn_hc, cpu_ffn_cur, ffn_post, ffn_comb);
    rms_norm_weight(cpu_ffn_norm, cpu_ffn_cur, tensor_data(model, layer->ffn_norm), DS4_N_EMBD, DS4_RMS_EPS);
```

<｜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
size_t)shared_dim * sizeof(float));
    float *cpu_shared_up = xmalloc((size_t)shared_dim * sizeof(float));
    float *cpu_shared_mid = xmalloc((size_t)shared_dim * sizeof(float));
    float *cpu_shared = xmalloc((size_t)DS4_N_EMBD * sizeof(float));
    float *cpu_routed = xmalloc((size_t)DS4_N_EMBD * sizeof(float));
    float *cpu_ffn_out = xmalloc((size_t)DS4_N_EMBD * sizeof(float));
    float *cpu_after_ffn_hc = xmalloc((size_t)hc_dim * sizeof(float));
    float post[4];
    float comb[16];
    float ffn_post[4];
    float ffn_comb[16];
    int selected[DS4_N_EXPERT_USED];
    float expert_weight[DS4_N_EXPERT_USED];
    const uint64_t shared_blocks = (shared_in_dim + 31) / 32;
    int8_t *shared_xq = xmalloc((size_t)shared_blocks * 32);
    float *shared_xscale = xmalloc((size_t)shared_blocks * sizeof(float));
    float *routed_mid_all = xmalloc((size_t)DS4_N_EXPERT_USED * down_in_dim * sizeof(float));
    block_q8_K *routed_xq = xmalloc((size_t)(expert_in_dim / QK_K) * sizeof(block_q8_K));
    block_q8_K *routed_midq = xmalloc((size_t)DS4_N_EXPERT_USED * (down_in_dim / QK_K) * sizeof(block_q8_K));

    hc_pre_from_state_one(model,
                          layer->hc_attn_fn,
                          layer->hc_attn_scale,
                          layer->hc_attn_base,
                          cpu_in_hc, cpu_attn_cur, post, comb);
    layer_attn_norm_one(cpu_attn_norm, model, layer, cpu_attn_cur);
    layer_q_projection_with_lora_one(model, layer, cpu_attn_norm, cpu_q, cpu_qr_norm);
    layer_kv_projection_normed_one(model, layer, cpu_attn_norm, cpu_kv);
    rope_tail_layer_inplace(cpu_q, DS4_N_HEAD, DS4_N_HEAD_DIM, DS4_N_ROT, 0, il, false);
    rope_tail_layer_inplace(cpu_kv, DS4_N_HEAD_KV, DS4_N_HEAD_DIM, DS4_N_ROT, 0, il, false);
    dsv4_fp8_kv_quantize_row_inplace_cpu(cpu_kv, DS4_N_HEAD_DIM, DS4_N_ROT);
    f16_round_inplace_cpu(cpu_kv, DS4_N_HEAD_DIM);
    layer_attention_one(cpu_heads, model, layer, cpu_q, cpu_kv);
    rope_tail_layer_inplace(cpu_heads, DS4_N_HEAD, DS4_N_HEAD_DIM, DS4_N_ROT, 0, il, true);
    layer_grouped_out_one(cpu_attn_out, model, layer, cpu_heads);
    hc_post_one(cpu_after_attn_hc, cpu_attn_out, cpu_in_hc, post, comb, DS4_N_EMBD, DS4_N_HC);
    hc_pre_from_state_one(model,
                          layer->hc_ffn_fn,
                          layer->hc_ffn_scale,
                          layer->hc_ffn_base,
                          cpu_after_attn_hc, cpu_ffn_cur, ffn_post, ffn_comb);
    rms_norm_weight(cpu_ffn_norm, cpu_ffn_cur, tensor_data(model, layer->ffn_norm), DS4_N_EMBD, DS4_RMS_EPS);
```<｜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
size_t)shared_dim * sizeof(float));
    float *cpu_shared_up = xmalloc((size_t)shared_dim * sizeof(float));
    float *cpu_shared_mid = xmalloc((size_t)shared_dim * sizeof(float));
    float *cpu_shared = xmalloc((size_t)DS4_N_EMBD * sizeof(float));
    float *cpu_routed = xmalloc((size_t)DS4_N_EMBD * sizeof(float));
    float *cpu_ffn_out = xmalloc((size_t)DS4_N_EMBD * sizeof(float));
    float *cpu_after_ffn_hc = xmalloc((size_t)hc_dim * sizeof(float));
    float post[4];
    float comb[16];
    float ffn_post[4];
    float ffn_comb[16];
    int selected[DS4_N_EXPERT_USED];
    float expert_weight[DS4_N_EXPERT_USED];
    const uint64_t shared_blocks = (shared_in_dim + 31) / 32;
    int8_t *shared_xq = xmalloc((size_t)shared_blocks * 32);
    float *shared_xscale = xmalloc((size_t)shared_blocks * sizeof(float));
    float *routed_mid_all = xmalloc((size_t)DS4_N_EXPERT_USED * down_in_dim * sizeof(float));
    block_q8_K *routed_xq = xmalloc((size_t)(expert_in_dim / QK_K) * sizeof(block_q8_K));
    block_q8_K *routed_midq = xmalloc((size_t)DS4_N_EXPERT_USED * (down_in_dim / QK_K) * sizeof(block_q8_K));

    hc_pre_from_state_one(model,
                          layer->hc_attn_fn,
                          layer->hc_attn_scale,
                          layer->hc_attn_base,
                          cpu_in_hc, cpu_attn_cur, post, comb);
    layer_attn_norm_one(cpu_attn_norm, model, layer, cpu_attn_cur);
    layer_q_projection_with_lora_one(model, layer, cpu_attn_norm, cpu_q, cpu_qr_norm);
    layer_kv_projection_normed_one(model, layer, cpu_attn_norm, cpu_kv);
    rope_tail_layer_inplace(cpu_q, DS4_N_HEAD, DS4_N_HEAD_DIM, DS4_N_ROT, 0, il, false);
    rope_tail_layer_inplace(cpu_kv, DS4_N_HEAD_KV, DS4_N_HEAD_DIM, DS4_N_ROT, 0, il, false);
    dsv4_fp8_kv_quantize_row_inplace_cpu(cpu_kv, DS4_N_HEAD_DIM, DS4_N_ROT);
    f16_round_inplace_cpu(cpu_kv, DS4_N_HEAD_DIM);
    layer_attention_one(cpu_heads, model, layer, cpu_q, cpu_kv);
    rope_tail_layer_inplace(cpu_heads, DS4_N_HEAD, DS4_N_HEAD_DIM, DS4_N_ROT, 0, il, true);
    layer_grouped_out_one(cpu_attn_out, model, layer, cpu_heads);
    hc_post_one(cpu_after_attn_hc, cpu_attn_out, cpu_in_hc, post, comb, DS4_N_EMBD, DS4_N_HC);
    hc_pre_from_state_one(model,
                          layer->hc_ffn_fn,
                          layer->hc_ffn_scale,
                          layer->hc_ffn_base,
                          cpu_after_attn_hc, cpu_ffn_cur, ffn_post, ffn_comb);
    rms_norm_weight(cpu_ffn_norm, cpu_ffn_cur, tensor_data(model, layer->ffn_norm), DS4_N_EMBD, DS4_RMS_EPS);
```

<｜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
size_t)shared_dim * sizeof(float));
    float *cpu_shared_up = xmalloc((size_t)shared_dim * sizeof(float));
    float *cpu_shared_mid = xmalloc((size_t)shared_dim * sizeof(float));
    float *cpu_shared = xmalloc((size_t)DS4_N_EMBD * sizeof(float));
    float *cpu_routed = xmalloc((size_t)DS4_N_EMBD * sizeof(float));
    float *cpu_ffn_out = xmalloc((size_t)DS4_N_EMBD * sizeof(float));
    float *cpu_after_ffn_hc = xmalloc((size_t)hc_dim * sizeof(float));
    float post[4];
    float comb[16];
    float ffn_post[4];
    float ffn_comb[16];
    int selected[DS4_N_EXPERT_USED];
    float expert_weight[DS4_N_EXPERT_USED];
    const uint64_t shared_blocks = (shared_in_dim + 31) / 32;
    int8_t *shared_xq = xmalloc((size_t)shared_blocks * 32);
    float *shared_xscale = xmalloc((size_t)shared_blocks * sizeof(float));
    float *routed_mid_all = xmalloc((size_t)DS4_N_EXPERT_USED * down_in_dim * sizeof(float));
    block_q8_K *routed_xq = xmalloc((size_t)(expert_in_dim / QK_K) * sizeof(block_q8_K));
    block_q8_K *routed_midq = xmalloc((size_t)DS4_N_EXPERT_USED * (down_in_dim / QK_K) * sizeof(block_q8_K));

    hc_pre_from_state_one(model,
                          layer->hc_attn_fn,
                          layer->hc_attn_scale,
                          layer->hc_attn_base,
                          cpu_in_hc, cpu_attn_cur, post, comb);
    layer_attn_norm_one(cpu_attn_norm, model, layer, cpu_attn_cur);
    layer_q_projection_with_lora_one(model, layer, cpu_attn_norm, cpu_q, cpu_qr_norm);
    layer_kv_projection_normed_one(model, layer, cpu_attn_norm, cpu_kv);
    rope_tail_layer_inplace(cpu_q, DS4_N_HEAD, DS4_N_HEAD_DIM, DS4_N_ROT, 0, il, false);
    rope_tail_layer_inplace(cpu_kv, DS4_N_HEAD_KV, DS4_N_HEAD_DIM, DS4_N_ROT, 0, il, false);
    dsv4_fp8_kv_quantize_row_inplace_cpu(cpu_kv, DS4_N_HEAD_DIM, DS4_N_ROT);
    f16_round_inplace_cpu(cpu_kv, DS4_N_HEAD_DIM);
    layer_attention_one(cpu_heads, model, layer, cpu_q, cpu_kv);
    rope_tail_layer_inplace(cpu_heads, DS4_N_HEAD, DS4_N_HEAD_DIM, DS4_N_ROT, 0, il, true);
    layer_grouped_out_one(cpu_attn_out, model, layer, cpu_heads);
    hc_post_one(cpu_after_attn_hc, cpu_attn_out, cpu_in_hc, post, comb, DS4_N_EMBD, DS4_N_HC);
    hc_pre_from_state_one(model,
                          layer->hc_ffn_fn,
                          layer->hc_ffn_scale,
                          layer->hc_ffn_base,
                          cpu_after_attn_hc, cpu_ffn_cur, ffn_post, ffn_comb);
    rms_norm_weight(cpu_ffn_norm, cpu_ffn_cur, tensor_data(model, layer->ffn_norm), DS4_N_EMBD, DS4_RMS_EPS);
```<｜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
cpu_after_attn_hc, cpu_ffn_cur, ffn_post, ffn_comb);
    rms_norm_weight(cpu_ffn_norm, cpu_ffn_cur, tensor_data(model, layer->ffn_norm), DS4_N_EMBD, DS4_RMS_EPS);
    quantize_q8_0_activation(cpu_ffn_norm, shared_xq, shared_xscale, shared_in_dim);
    matvec_q8_0_pair_prequant(cpu_shared_gate,
                              cpu_shared_up,
                              model,
                              layer->ffn_gate_shexp,
                              layer->ffn_up_shexp,
                              shared_xq,
                              shared_xscale);
    swiglu(cpu_shared_mid, cpu_shared_gate, cpu_shared_up, shared_dim);
    matvec_q8_0(cpu_shared, model, layer->ffn_down_shexp, cpu_shared_mid);
    layer_routed_moe_one_prealloc(cpu_routed,
                                  model,
                                  layer,
                                  cpu_ffn_norm,
                                  il,
                                  token,
                                  DS4_SWIGLU_CLAMP_EXP,
                                  routed_mid_all,
                                  routed_xq,
                                  routed_midq);
    if (layer->ffn_gate_tid2eid) {
        layer_hash_selected_experts(selected, model, layer, token);
        layer_hash_router_weights_one(expert_weight, model, layer, cpu_ffn_norm, selected);
    } else {
        layer_topk_selected_experts(selected, expert_weight, model, layer, cpu_ffn_norm);
    }
    for (uint32_t i = 0; i < DS4_N_EMBD; i++) cpu_ffn_out[i] = cpu_shared[i] + cpu_routed[i];
    hc_post_one(cpu_after_ffn_hc, cpu_ffn_out, cpu_after_attn_hc, ffn_post, ffn_comb, DS4_N_EMBD, DS4_N_HC);

    float *gpu_attn_cur = xmalloc((size_t)DS4_N_EMBD * sizeof(float));
    float *gpu_attn_norm = xmalloc((size_t)DS4_N_EMBD * sizeof(float));
    float *gpu_q = xmalloc((size_t)q_dim * sizeof(float));
    float *gpu_kv = xmalloc((size_t)DS4_N_HEAD_DIM * sizeof(float));
    float *gpu_attn_out = xmalloc((size_t)DS4_N_EMBD * sizeof(float));
    float *gpu_after_attn_hc = xmalloc((size_t)hc_dim * sizeof(float));
    float *gpu_ffn_cur = xmalloc((size_t)DS4_N_EMBD * sizeof(float));
    float *gpu_ffn_norm = xmalloc((size_t)DS4_N_EMBD * sizeof(float));
    float *gpu_shared_gate = xmalloc((size_t)shared_dim * sizeof(float));
    float *gpu_shared_up = xmalloc((size_t)shared_dim * sizeof(float));
    float *gpu_shared_mid = xmalloc((size_t)shared_dim * sizeof(float));
    float *gpu_shared = xmalloc((size_t)DS4_N_EMBD * sizeof(float));
```

<｜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
cpu_after_attn_hc, cpu_ffn_cur, ffn_post, ffn_comb);
    rms_norm_weight(cpu_ffn_norm, cpu_ffn_cur, tensor_data(model, layer->ffn_norm), DS4_N_EMBD, DS4_RMS_EPS);
    quantize_q8_0_activation(cpu_ffn_norm, shared_xq, shared_xscale, shared_in_dim);
    matvec_q8_0_pair_prequant(cpu_shared_gate,
                              cpu_shared_up,
                              model,
                              layer->ffn_gate_shexp,
                              layer->ffn_up_shexp,
                              shared_xq,
                              shared_xscale);
    swiglu(cpu_shared_mid, cpu_shared_gate, cpu_shared_up, shared_dim);
    matvec_q8_0(cpu_shared, model, layer->ffn_down_shexp, cpu_shared_mid);
    layer_routed_moe_one_prealloc(cpu_routed,
                                  model,
                                  layer,
                                  cpu_ffn_norm,
                                  il,
                                  token,
                                  DS4_SWIGLU_CLAMP_EXP,
                                  routed_mid_all,
                                  routed_xq,
                                  routed_midq);
    if (layer->ffn_gate_tid2eid) {
        layer_hash_selected_experts(selected, model, layer, token);
        layer_hash_router_weights_one(expert_weight, model, layer, cpu_ffn_norm, selected);
    } else {
        layer_topk_selected_experts(selected, expert_weight, model, layer, cpu_ffn_norm);
    }
    for (uint32_t i = 0; i < DS4_N_EMBD; i++) cpu_ffn_out[i] = cpu_shared[i] + cpu_routed[i];
    hc_post_one(cpu_after_ffn_hc, cpu_ffn_out, cpu_after_attn_hc, ffn_post, ffn_comb, DS4_N_EMBD, DS4_N_HC);

    float *gpu_attn_cur = xmalloc((size_t)DS4_N_EMBD * sizeof(float));
    float *gpu_attn_norm = xmalloc((size_t)DS4_N_EMBD * sizeof(float));
    float *gpu_q = xmalloc((size_t)q_dim * sizeof(float));
    float *gpu_kv = xmalloc((size_t)DS4_N_HEAD_DIM * sizeof(float));
    float *gpu_attn_out = xmalloc((size_t)DS4_N_EMBD * sizeof(float));
    float *gpu_after_attn_hc = xmalloc((size_t)hc_dim * sizeof(float));
    float *gpu_ffn_cur = xmalloc((size_t)DS4_N_EMBD * sizeof(float));
    float *gpu_ffn_norm = xmalloc((size_t)DS4_N_EMBD * sizeof(float));
    float *gpu_shared_gate = xmalloc((size_t)shared_dim * sizeof(float));
    float *gpu_shared_up = xmalloc((size_t)shared_dim * sizeof(float));
    float *gpu_shared_mid = xmalloc((size_t)shared_dim * sizeof(float));
    float *gpu_shared = xmalloc((size_t)DS4_N_EMBD * sizeof(float));
```<｜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
cpu_after_attn_hc, cpu_ffn_cur, ffn_post, ffn_comb);
    rms_norm_weight(cpu_ffn_norm, cpu_ffn_cur, tensor_data(model, layer->ffn_norm), DS4_N_EMBD, DS4_RMS_EPS);
    quantize_q8_0_activation(cpu_ffn_norm, shared_xq, shared_xscale, shared_in_dim);
    matvec_q8_0_pair_prequant(cpu_shared_gate,
                              cpu_shared_up,
                              model,
                              layer->ffn_gate_shexp,
                              layer->ffn_up_shexp,
                              shared_xq,
                              shared_xscale);
    swiglu(cpu_shared_mid, cpu_shared_gate, cpu_shared_up, shared_dim);
    matvec_q8_0(cpu_shared, model, layer->ffn_down_shexp, cpu_shared_mid);
    layer_routed_moe_one_prealloc(cpu_routed,
                                  model,
                                  layer,
                                  cpu_ffn_norm,
                                  il,
                                  token,
                                  DS4_SWIGLU_CLAMP_EXP,
                                  routed_mid_all,
                                  routed_xq,
                                  routed_midq);
    if (layer->ffn_gate_tid2eid) {
        layer_hash_selected_experts(selected, model, layer, token);
        layer_hash_router_weights_one(expert_weight, model, layer, cpu_ffn_norm, selected);
    } else {
        layer_topk_selected_experts(selected, expert_weight, model, layer, cpu_ffn_norm);
    }
    for (uint32_t i = 0; i < DS4_N_EMBD; i++) cpu_ffn_out[i] = cpu_shared[i] + cpu_routed[i];
    hc_post_one(cpu_after_ffn_hc, cpu_ffn_out, cpu_after_attn_hc, ffn_post, ffn_comb, DS4_N_EMBD, DS4_N_HC);

    float *gpu_attn_cur = xmalloc((size_t)DS4_N_EMBD * sizeof(float));
    float *gpu_attn_norm = xmalloc((size_t)DS4_N_EMBD * sizeof(float));
    float *gpu_q = xmalloc((size_t)q_dim * sizeof(float));
    float *gpu_kv = xmalloc((size_t)DS4_N_HEAD_DIM * sizeof(float));
    float *gpu_attn_out = xmalloc((size_t)DS4_N_EMBD * sizeof(float));
    float *gpu_after_attn_hc = xmalloc((size_t)hc_dim * sizeof(float));
    float *gpu_ffn_cur = xmalloc((size_t)DS4_N_EMBD * sizeof(float));
    float *gpu_ffn_norm = xmalloc((size_t)DS4_N_EMBD * sizeof(float));
    float *gpu_shared_gate = xmalloc((size_t)shared_dim * sizeof(float));
    float *gpu_shared_up = xmalloc((size_t)shared_dim * sizeof(float));
    float *gpu_shared_mid = xmalloc((size_t)shared_dim * sizeof(float));
    float *gpu_shared = xmalloc((size_t)DS4_N_EMBD * sizeof(float));
```

<｜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
cpu_after_attn_hc, cpu_ffn_cur, ffn_post, ffn_comb);
    rms_norm_weight(cpu_ffn_norm, cpu_ffn_cur, tensor_data(model, layer->ffn_norm), DS4_N_EMBD, DS4_RMS_EPS);
    quantize_q8_0_activation(cpu_ffn_norm, shared_xq, shared_xscale, shared_in_dim);
    matvec_q8_0_pair_prequant(cpu_shared_gate,
                              cpu_shared_up,
                              model,
                              layer->ffn_gate_shexp,
                              layer->ffn_up_shexp,
                              shared_xq,
                              shared_xscale);
    swiglu(cpu_shared_mid, cpu_shared_gate, cpu_shared_up, shared_dim);
    matvec_q8_0(cpu_shared, model, layer->ffn_down_shexp, cpu_shared_mid);
    layer_routed_moe_one_prealloc(cpu_routed,
                                  model,
                                  layer,
                                  cpu_ffn_norm,
                                  il,
                                  token,
                                  DS4_SWIGLU_CLAMP_EXP,
                                  routed_mid_all,
                                  routed_xq,
                                  routed_midq);
    if (layer->ffn_gate_tid2eid) {
        layer_hash_selected_experts(selected, model, layer, token);
        layer_hash_router_weights_one(expert_weight, model, layer, cpu_ffn_norm, selected);
    } else {
        layer_topk_selected_experts(selected, expert_weight, model, layer, cpu_ffn_norm);
    }
    for (uint32_t i = 0; i < DS4_N_EMBD; i++) cpu_ffn_out[i] = cpu_shared[i] + cpu_routed[i];
    hc_post_one(cpu_after_ffn_hc, cpu_ffn_out, cpu_after_attn_hc, ffn_post, ffn_comb, DS4_N_EMBD, DS4_N_HC);

    float *gpu_attn_cur = xmalloc((size_t)DS4_N_EMBD * sizeof(float));
    float *gpu_attn_norm = xmalloc((size_t)DS4_N_EMBD * sizeof(float));
    float *gpu_q = xmalloc((size_t)q_dim * sizeof(float));
    float *gpu_kv = xmalloc((size_t)DS4_N_HEAD_DIM * sizeof(float));
    float *gpu_attn_out = xmalloc((size_t)DS4_N_EMBD * sizeof(float));
    float *gpu_after_attn_hc = xmalloc((size_t)hc_dim * sizeof(float));
    float *gpu_ffn_cur = xmalloc((size_t)DS4_N_EMBD * sizeof(float));
    float *gpu_ffn_norm = xmalloc((size_t)DS4_N_EMBD * sizeof(float));
    float *gpu_shared_gate = xmalloc((size_t)shared_dim * sizeof(float));
    float *gpu_shared_up = xmalloc((size_t)shared_dim * sizeof(float));
    float *gpu_shared_mid = xmalloc((size_t)shared_dim * sizeof(float));
    float *gpu_shared = xmalloc((size_t)DS4_N_EMBD * sizeof(float));
```<｜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
nt32_t indexer_top_k = 0;
    uint32_t n_expert = 0;
    uint32_t n_expert_used = 0;
    uint32_t n_expert_groups = 0;
    uint32_t n_group_used = 0;
    uint64_t tensor_bytes = 0;
    uint64_t params = 0;

    model_get_string(m, "general.name", &name);
    model_get_string(m, "general.architecture", &arch);
    model_get_u32(m, "deepseek4.block_count", &layers);
    model_get_u64(m, "deepseek4.context_length", &ctx_train);
    model_get_u32(m, "deepseek4.attention.head_count", &n_head);
    model_get_u32(m, "deepseek4.attention.head_count_kv", &n_head_kv);
    model_get_u32(m, "deepseek4.attention.key_length", &head_dim);
    model_get_u32(m, "deepseek4.attention.sliding_window", &n_swa);
    model_get_u32(m, "deepseek4.attention.indexer.head_count", &indexer_heads);
    model_get_u32(m, "deepseek4.attention.indexer.key_length", &indexer_head_dim);
    model_get_u32(m, "deepseek4.attention.indexer.top_k", &indexer_top_k);
    model_get_u32(m, "deepseek4.expert_count", &n_expert);
    model_get_u32(m, "deepseek4.expert_used_count", &n_expert_used);
    model_get_u32(m, "deepseek4.expert_group_count", &n_expert_groups);
    model_get_u32(m, "deepseek4.expert_group_used_count", &n_group_used);

    for (uint64_t i = 0; i < m->n_tensors; i++) {
        tensor_bytes += m->tensors[i].bytes;
        params += m->tensors[i].elements;
    }

    printf("model: %.*s\n", (int)name.len, name.ptr);
    printf("arch:  %.*s\n", (int)arch.len, arch.ptr);
    printf("gguf:  v%u, %" PRIu64 " metadata keys, %" PRIu64 " tensors\n",
        m->version, m->n_kv, m->n_tensors);
    if (layers) printf("layers: %u\n", layers);
    if (ctx_train) printf("train context: %" PRIu64 "\n", ctx_train);
    if (n_head || n_head_kv || head_dim || n_swa) {
        printf("attention: heads=%u kv_heads=%u head_dim=%u swa=%u\n",
               n_head, n_head_kv, head_dim, n_swa);
    }
    if (indexer_heads || indexer_head_dim || indexer_top_k) {
        printf("indexer: heads=%u head_dim=%u top_k=%u\n",
               indexer_heads, indexer_head_dim, indexer_top_k);
    }
    if (n_expert || n_expert_used || n_expert_groups || n_group_used) {
        printf("experts: count=%u used=%u groups=%u groups_used=%u\n",
               n_expert, n_expert_used, n_expert_groups, n_group_used);
    }
    printf("file size: ");
    print_size(m->size);
    printf("\n");
    printf("tensor bytes described by GGUF: ");
    print_size(tensor_bytes);
    printf("\n");
    printf("logical parameters: %.2f B\n", (double)params / 1000000000.0);

    printf("tensor types:\n");
```

<｜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
nt32_t indexer_top_k = 0;
    uint32_t n_expert = 0;
    uint32_t n_expert_used = 0;
    uint32_t n_expert_groups = 0;
    uint32_t n_group_used = 0;
    uint64_t tensor_bytes = 0;
    uint64_t params = 0;

    model_get_string(m, "general.name", &name);
    model_get_string(m, "general.architecture", &arch);
    model_get_u32(m, "deepseek4.block_count", &layers);
    model_get_u64(m, "deepseek4.context_length", &ctx_train);
    model_get_u32(m, "deepseek4.attention.head_count", &n_head);
    model_get_u32(m, "deepseek4.attention.head_count_kv", &n_head_kv);
    model_get_u32(m, "deepseek4.attention.key_length", &head_dim);
    model_get_u32(m, "deepseek4.attention.sliding_window", &n_swa);
    model_get_u32(m, "deepseek4.attention.indexer.head_count", &indexer_heads);
    model_get_u32(m, "deepseek4.attention.indexer.key_length", &indexer_head_dim);
    model_get_u32(m, "deepseek4.attention.indexer.top_k", &indexer_top_k);
    model_get_u32(m, "deepseek4.expert_count", &n_expert);
    model_get_u32(m, "deepseek4.expert_used_count", &n_expert_used);
    model_get_u32(m, "deepseek4.expert_group_count", &n_expert_groups);
    model_get_u32(m, "deepseek4.expert_group_used_count", &n_group_used);

    for (uint64_t i = 0; i < m->n_tensors; i++) {
        tensor_bytes += m->tensors[i].bytes;
        params += m->tensors[i].elements;
    }

    printf("model: %.*s\n", (int)name.len, name.ptr);
    printf("arch:  %.*s\n", (int)arch.len, arch.ptr);
    printf("gguf:  v%u, %" PRIu64 " metadata keys, %" PRIu64 " tensors\n",
        m->version, m->n_kv, m->n_tensors);
    if (layers) printf("layers: %u\n", layers);
    if (ctx_train) printf("train context: %" PRIu64 "\n", ctx_train);
    if (n_head || n_head_kv || head_dim || n_swa) {
        printf("attention: heads=%u kv_heads=%u head_dim=%u swa=%u\n",
               n_head, n_head_kv, head_dim, n_swa);
    }
    if (indexer_heads || indexer_head_dim || indexer_top_k) {
        printf("indexer: heads=%u head_dim=%u top_k=%u\n",
               indexer_heads, indexer_head_dim, indexer_top_k);
    }
    if (n_expert || n_expert_used || n_expert_groups || n_group_used) {
        printf("experts: count=%u used=%u groups=%u groups_used=%u\n",
               n_expert, n_expert_used, n_expert_groups, n_group_used);
    }
    printf("file size: ");
    print_size(m->size);
    printf("\n");
    printf("tensor bytes described by GGUF: ");
    print_size(tensor_bytes);
    printf("\n");
    printf("logical parameters: %.2f B\n", (double)params / 1000000000.0);

    printf("tensor types:\n");
```<｜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
nt32_t indexer_top_k = 0;
    uint32_t n_expert = 0;
    uint32_t n_expert_used = 0;
    uint32_t n_expert_groups = 0;
    uint32_t n_group_used = 0;
    uint64_t tensor_bytes = 0;
    uint64_t params = 0;

    model_get_string(m, "general.name", &name);
    model_get_string(m, "general.architecture", &arch);
    model_get_u32(m, "deepseek4.block_count", &layers);
    model_get_u64(m, "deepseek4.context_length", &ctx_train);
    model_get_u32(m, "deepseek4.attention.head_count", &n_head);
    model_get_u32(m, "deepseek4.attention.head_count_kv", &n_head_kv);
    model_get_u32(m, "deepseek4.attention.key_length", &head_dim);
    model_get_u32(m, "deepseek4.attention.sliding_window", &n_swa);
    model_get_u32(m, "deepseek4.attention.indexer.head_count", &indexer_heads);
    model_get_u32(m, "deepseek4.attention.indexer.key_length", &indexer_head_dim);
    model_get_u32(m, "deepseek4.attention.indexer.top_k", &indexer_top_k);
    model_get_u32(m, "deepseek4.expert_count", &n_expert);
    model_get_u32(m, "deepseek4.expert_used_count", &n_expert_used);
    model_get_u32(m, "deepseek4.expert_group_count", &n_expert_groups);
    model_get_u32(m, "deepseek4.expert_group_used_count", &n_group_used);

    for (uint64_t i = 0; i < m->n_tensors; i++) {
        tensor_bytes += m->tensors[i].bytes;
        params += m->tensors[i].elements;
    }

    printf("model: %.*s\n", (int)name.len, name.ptr);
    printf("arch:  %.*s\n", (int)arch.len, arch.ptr);
    printf("gguf:  v%u, %" PRIu64 " metadata keys, %" PRIu64 " tensors\n",
        m->version, m->n_kv, m->n_tensors);
    if (layers) printf("layers: %u\n", layers);
    if (ctx_train) printf("train context: %" PRIu64 "\n", ctx_train);
    if (n_head || n_head_kv || head_dim || n_swa) {
        printf("attention: heads=%u kv_heads=%u head_dim=%u swa=%u\n",
               n_head, n_head_kv, head_dim, n_swa);
    }
    if (indexer_heads || indexer_head_dim || indexer_top_k) {
        printf("indexer: heads=%u head_dim=%u top_k=%u\n",
               indexer_heads, indexer_head_dim, indexer_top_k);
    }
    if (n_expert || n_expert_used || n_expert_groups || n_group_used) {
        printf("experts: count=%u used=%u groups=%u groups_used=%u\n",
               n_expert, n_expert_used, n_expert_groups, n_group_used);
    }
    printf("file size: ");
    print_size(m->size);
    printf("\n");
    printf("tensor bytes described by GGUF: ");
    print_size(tensor_bytes);
    printf("\n");
    printf("logical parameters: %.2f B\n", (double)params / 1000000000.0);

    printf("tensor types:\n");
```

<｜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
nt32_t indexer_top_k = 0;
    uint32_t n_expert = 0;
    uint32_t n_expert_used = 0;
    uint32_t n_expert_groups = 0;
    uint32_t n_group_used = 0;
    uint64_t tensor_bytes = 0;
    uint64_t params = 0;

    model_get_string(m, "general.name", &name);
    model_get_string(m, "general.architecture", &arch);
    model_get_u32(m, "deepseek4.block_count", &layers);
    model_get_u64(m, "deepseek4.context_length", &ctx_train);
    model_get_u32(m, "deepseek4.attention.head_count", &n_head);
    model_get_u32(m, "deepseek4.attention.head_count_kv", &n_head_kv);
    model_get_u32(m, "deepseek4.attention.key_length", &head_dim);
    model_get_u32(m, "deepseek4.attention.sliding_window", &n_swa);
    model_get_u32(m, "deepseek4.attention.indexer.head_count", &indexer_heads);
    model_get_u32(m, "deepseek4.attention.indexer.key_length", &indexer_head_dim);
    model_get_u32(m, "deepseek4.attention.indexer.top_k", &indexer_top_k);
    model_get_u32(m, "deepseek4.expert_count", &n_expert);
    model_get_u32(m, "deepseek4.expert_used_count", &n_expert_used);
    model_get_u32(m, "deepseek4.expert_group_count", &n_expert_groups);
    model_get_u32(m, "deepseek4.expert_group_used_count", &n_group_used);

    for (uint64_t i = 0; i < m->n_tensors; i++) {
        tensor_bytes += m->tensors[i].bytes;
        params += m->tensors[i].elements;
    }

    printf("model: %.*s\n", (int)name.len, name.ptr);
    printf("arch:  %.*s\n", (int)arch.len, arch.ptr);
    printf("gguf:  v%u, %" PRIu64 " metadata keys, %" PRIu64 " tensors\n",
        m->version, m->n_kv, m->n_tensors);
    if (layers) printf("layers: %u\n", layers);
    if (ctx_train) printf("train context: %" PRIu64 "\n", ctx_train);
    if (n_head || n_head_kv || head_dim || n_swa) {
        printf("attention: heads=%u kv_heads=%u head_dim=%u swa=%u\n",
               n_head, n_head_kv, head_dim, n_swa);
    }
    if (indexer_heads || indexer_head_dim || indexer_top_k) {
        printf("indexer: heads=%u head_dim=%u top_k=%u\n",
               indexer_heads, indexer_head_dim, indexer_top_k);
    }
    if (n_expert || n_expert_used || n_expert_groups || n_group_used) {
        printf("experts: count=%u used=%u groups=%u groups_used=%u\n",
               n_expert, n_expert_used, n_expert_groups, n_group_used);
    }
    printf("file size: ");
    print_size(m->size);
    printf("\n");
    printf("tensor bytes described by GGUF: ");
    print_size(tensor_bytes);
    printf("\n");
    printf("logical parameters: %.2f B\n", (double)params / 1000000000.0);

    printf("tensor types:\n");
```<｜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
c((size_t)shared_dim * sizeof(float));
    float *gpu_shared_mid = xmalloc((size_t)shared_dim * sizeof(float));
    float *gpu_shared = xmalloc((size_t)DS4_N_EMBD * sizeof(float));
    float *gpu_routed_mid_all = xmalloc((size_t)DS4_N_EXPERT_USED * down_in_dim * sizeof(float));
    float *gpu_routed = xmalloc((size_t)DS4_N_EMBD * sizeof(float));
    float *gpu_ffn_out = xmalloc((size_t)DS4_N_EMBD * sizeof(float));
    float *gpu_after_ffn_hc = xmalloc((size_t)hc_dim * sizeof(float));
    int gpu_selected[DS4_N_EXPERT_USED];
    float gpu_expert_weight[DS4_N_EXPERT_USED];

    bool ok = ds4_gpu_tensor_read(g->attn_cur, 0, gpu_attn_cur, (uint64_t)DS4_N_EMBD * sizeof(float)) != 0 &&
              ds4_gpu_tensor_read(g->attn_norm, 0, gpu_attn_norm, (uint64_t)DS4_N_EMBD * sizeof(float)) != 0 &&
              ds4_gpu_tensor_read(g->q, 0, gpu_q, q_dim * sizeof(float)) != 0 &&
              ds4_gpu_tensor_read(g->kv, 0, gpu_kv, (uint64_t)DS4_N_HEAD_DIM * sizeof(float)) != 0 &&
              ds4_gpu_tensor_read(g->attn_out, 0, gpu_attn_out, (uint64_t)DS4_N_EMBD * sizeof(float)) != 0 &&
              ds4_gpu_tensor_read(g->after_attn_hc, 0, gpu_after_attn_hc, hc_dim * sizeof(float)) != 0 &&
              ds4_gpu_tensor_read(g->ffn_cur, 0, gpu_ffn_cur, (uint64_t)DS4_N_EMBD * sizeof(float)) != 0 &&
              ds4_gpu_tensor_read(g->ffn_norm, 0, gpu_ffn_norm, (uint64_t)DS4_N_EMBD * sizeof(float)) != 0 &&
              ds4_gpu_tensor_read(g->shared_gate, 0, gpu_shared_gate, shared_dim * sizeof(float)) != 0 &&
              ds4_gpu_tensor_read(g->shared_up, 0, gpu_shared_up, shared_dim * sizeof(float)) != 0 &&
              ds4_gpu_tensor_read(g->shared_mid, 0, gpu_shared_mid, shared_dim * sizeof(float)) != 0 &&
              ds4_gpu_tensor_read(g->shared_out, 0, gpu_shared, (uint64_t)DS4_N_EMBD * sizeof(float)) != 0 &&
              ds4_gpu_tensor_read(g->router_selected, 0, gpu_selected, sizeof(gpu_selected)) != 0 &&
              ds4_gpu_tensor_read(g->router_weights, 0, gpu_expert_weight, sizeof(gpu_expert_weight)) != 0 &&
              ds4_gpu_tensor_read(g->routed_mid, 0, gpu_routed_mid_all, (uint64_t)DS4_N_EXPERT_USED * down_in_dim * sizeof(float)) != 0 &&
              ds4_gpu_tensor_read(g->routed_out, 0, gpu_routed, (uint64_t)DS4_N_EMBD * sizeof(float)) != 0 &&
              ds4_gpu_tensor_read(g->ffn_out, 0, gpu_ffn_out, (uint64_t)DS4_N_EMBD * sizeof(float)) != 0 &&
              ds4_gpu_tensor_read(g->cur_hc, 0, gpu_after_ffn_hc, hc_dim * sizeof(float)) != 0;

    if (ok) {
        fprintf(stderr,
```

<｜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
c((size_t)shared_dim * sizeof(float));
    float *gpu_shared_mid = xmalloc((size_t)shared_dim * sizeof(float));
    float *gpu_shared = xmalloc((size_t)DS4_N_EMBD * sizeof(float));
    float *gpu_routed_mid_all = xmalloc((size_t)DS4_N_EXPERT_USED * down_in_dim * sizeof(float));
    float *gpu_routed = xmalloc((size_t)DS4_N_EMBD * sizeof(float));
    float *gpu_ffn_out = xmalloc((size_t)DS4_N_EMBD * sizeof(float));
    float *gpu_after_ffn_hc = xmalloc((size_t)hc_dim * sizeof(float));
    int gpu_selected[DS4_N_EXPERT_USED];
    float gpu_expert_weight[DS4_N_EXPERT_USED];

    bool ok = ds4_gpu_tensor_read(g->attn_cur, 0, gpu_attn_cur, (uint64_t)DS4_N_EMBD * sizeof(float)) != 0 &&
              ds4_gpu_tensor_read(g->attn_norm, 0, gpu_attn_norm, (uint64_t)DS4_N_EMBD * sizeof(float)) != 0 &&
              ds4_gpu_tensor_read(g->q, 0, gpu_q, q_dim * sizeof(float)) != 0 &&
              ds4_gpu_tensor_read(g->kv, 0, gpu_kv, (uint64_t)DS4_N_HEAD_DIM * sizeof(float)) != 0 &&
              ds4_gpu_tensor_read(g->attn_out, 0, gpu_attn_out, (uint64_t)DS4_N_EMBD * sizeof(float)) != 0 &&
              ds4_gpu_tensor_read(g->after_attn_hc, 0, gpu_after_attn_hc, hc_dim * sizeof(float)) != 0 &&
              ds4_gpu_tensor_read(g->ffn_cur, 0, gpu_ffn_cur, (uint64_t)DS4_N_EMBD * sizeof(float)) != 0 &&
              ds4_gpu_tensor_read(g->ffn_norm, 0, gpu_ffn_norm, (uint64_t)DS4_N_EMBD * sizeof(float)) != 0 &&
              ds4_gpu_tensor_read(g->shared_gate, 0, gpu_shared_gate, shared_dim * sizeof(float)) != 0 &&
              ds4_gpu_tensor_read(g->shared_up, 0, gpu_shared_up, shared_dim * sizeof(float)) != 0 &&
              ds4_gpu_tensor_read(g->shared_mid, 0, gpu_shared_mid, shared_dim * sizeof(float)) != 0 &&
              ds4_gpu_tensor_read(g->shared_out, 0, gpu_shared, (uint64_t)DS4_N_EMBD * sizeof(float)) != 0 &&
              ds4_gpu_tensor_read(g->router_selected, 0, gpu_selected, sizeof(gpu_selected)) != 0 &&
              ds4_gpu_tensor_read(g->router_weights, 0, gpu_expert_weight, sizeof(gpu_expert_weight)) != 0 &&
              ds4_gpu_tensor_read(g->routed_mid, 0, gpu_routed_mid_all, (uint64_t)DS4_N_EXPERT_USED * down_in_dim * sizeof(float)) != 0 &&
              ds4_gpu_tensor_read(g->routed_out, 0, gpu_routed, (uint64_t)DS4_N_EMBD * sizeof(float)) != 0 &&
              ds4_gpu_tensor_read(g->ffn_out, 0, gpu_ffn_out, (uint64_t)DS4_N_EMBD * sizeof(float)) != 0 &&
              ds4_gpu_tensor_read(g->cur_hc, 0, gpu_after_ffn_hc, hc_dim * sizeof(float)) != 0;

    if (ok) {
        fprintf(stderr,
```<｜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
c((size_t)shared_dim * sizeof(float));
    float *gpu_shared_mid = xmalloc((size_t)shared_dim * sizeof(float));
    float *gpu_shared = xmalloc((size_t)DS4_N_EMBD * sizeof(float));
    float *gpu_routed_mid_all = xmalloc((size_t)DS4_N_EXPERT_USED * down_in_dim * sizeof(float));
    float *gpu_routed = xmalloc((size_t)DS4_N_EMBD * sizeof(float));
    float *gpu_ffn_out = xmalloc((size_t)DS4_N_EMBD * sizeof(float));
    float *gpu_after_ffn_hc = xmalloc((size_t)hc_dim * sizeof(float));
    int gpu_selected[DS4_N_EXPERT_USED];
    float gpu_expert_weight[DS4_N_EXPERT_USED];

    bool ok = ds4_gpu_tensor_read(g->attn_cur, 0, gpu_attn_cur, (uint64_t)DS4_N_EMBD * sizeof(float)) != 0 &&
              ds4_gpu_tensor_read(g->attn_norm, 0, gpu_attn_norm, (uint64_t)DS4_N_EMBD * sizeof(float)) != 0 &&
              ds4_gpu_tensor_read(g->q, 0, gpu_q, q_dim * sizeof(float)) != 0 &&
              ds4_gpu_tensor_read(g->kv, 0, gpu_kv, (uint64_t)DS4_N_HEAD_DIM * sizeof(float)) != 0 &&
              ds4_gpu_tensor_read(g->attn_out, 0, gpu_attn_out, (uint64_t)DS4_N_EMBD * sizeof(float)) != 0 &&
              ds4_gpu_tensor_read(g->after_attn_hc, 0, gpu_after_attn_hc, hc_dim * sizeof(float)) != 0 &&
              ds4_gpu_tensor_read(g->ffn_cur, 0, gpu_ffn_cur, (uint64_t)DS4_N_EMBD * sizeof(float)) != 0 &&
              ds4_gpu_tensor_read(g->ffn_norm, 0, gpu_ffn_norm, (uint64_t)DS4_N_EMBD * sizeof(float)) != 0 &&
              ds4_gpu_tensor_read(g->shared_gate, 0, gpu_shared_gate, shared_dim * sizeof(float)) != 0 &&
              ds4_gpu_tensor_read(g->shared_up, 0, gpu_shared_up, shared_dim * sizeof(float)) != 0 &&
              ds4_gpu_tensor_read(g->shared_mid, 0, gpu_shared_mid, shared_dim * sizeof(float)) != 0 &&
              ds4_gpu_tensor_read(g->shared_out, 0, gpu_shared, (uint64_t)DS4_N_EMBD * sizeof(float)) != 0 &&
              ds4_gpu_tensor_read(g->router_selected, 0, gpu_selected, sizeof(gpu_selected)) != 0 &&
              ds4_gpu_tensor_read(g->router_weights, 0, gpu_expert_weight, sizeof(gpu_expert_weight)) != 0 &&
              ds4_gpu_tensor_read(g->routed_mid, 0, gpu_routed_mid_all, (uint64_t)DS4_N_EXPERT_USED * down_in_dim * sizeof(float)) != 0 &&
              ds4_gpu_tensor_read(g->routed_out, 0, gpu_routed, (uint64_t)DS4_N_EMBD * sizeof(float)) != 0 &&
              ds4_gpu_tensor_read(g->ffn_out, 0, gpu_ffn_out, (uint64_t)DS4_N_EMBD * sizeof(float)) != 0 &&
              ds4_gpu_tensor_read(g->cur_hc, 0, gpu_after_ffn_hc, hc_dim * sizeof(float)) != 0;

    if (ok) {
        fprintf(stderr,
```

<｜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
c((size_t)shared_dim * sizeof(float));
    float *gpu_shared_mid = xmalloc((size_t)shared_dim * sizeof(float));
    float *gpu_shared = xmalloc((size_t)DS4_N_EMBD * sizeof(float));
    float *gpu_routed_mid_all = xmalloc((size_t)DS4_N_EXPERT_USED * down_in_dim * sizeof(float));
    float *gpu_routed = xmalloc((size_t)DS4_N_EMBD * sizeof(float));
    float *gpu_ffn_out = xmalloc((size_t)DS4_N_EMBD * sizeof(float));
    float *gpu_after_ffn_hc = xmalloc((size_t)hc_dim * sizeof(float));
    int gpu_selected[DS4_N_EXPERT_USED];
    float gpu_expert_weight[DS4_N_EXPERT_USED];

    bool ok = ds4_gpu_tensor_read(g->attn_cur, 0, gpu_attn_cur, (uint64_t)DS4_N_EMBD * sizeof(float)) != 0 &&
              ds4_gpu_tensor_read(g->attn_norm, 0, gpu_attn_norm, (uint64_t)DS4_N_EMBD * sizeof(float)) != 0 &&
              ds4_gpu_tensor_read(g->q, 0, gpu_q, q_dim * sizeof(float)) != 0 &&
              ds4_gpu_tensor_read(g->kv, 0, gpu_kv, (uint64_t)DS4_N_HEAD_DIM * sizeof(float)) != 0 &&
              ds4_gpu_tensor_read(g->attn_out, 0, gpu_attn_out, (uint64_t)DS4_N_EMBD * sizeof(float)) != 0 &&
              ds4_gpu_tensor_read(g->after_attn_hc, 0, gpu_after_attn_hc, hc_dim * sizeof(float)) != 0 &&
              ds4_gpu_tensor_read(g->ffn_cur, 0, gpu_ffn_cur, (uint64_t)DS4_N_EMBD * sizeof(float)) != 0 &&
              ds4_gpu_tensor_read(g->ffn_norm, 0, gpu_ffn_norm, (uint64_t)DS4_N_EMBD * sizeof(float)) != 0 &&
              ds4_gpu_tensor_read(g->shared_gate, 0, gpu_shared_gate, shared_dim * sizeof(float)) != 0 &&
              ds4_gpu_tensor_read(g->shared_up, 0, gpu_shared_up, shared_dim * sizeof(float)) != 0 &&
              ds4_gpu_tensor_read(g->shared_mid, 0, gpu_shared_mid, shared_dim * sizeof(float)) != 0 &&
              ds4_gpu_tensor_read(g->shared_out, 0, gpu_shared, (uint64_t)DS4_N_EMBD * sizeof(float)) != 0 &&
              ds4_gpu_tensor_read(g->router_selected, 0, gpu_selected, sizeof(gpu_selected)) != 0 &&
              ds4_gpu_tensor_read(g->router_weights, 0, gpu_expert_weight, sizeof(gpu_expert_weight)) != 0 &&
              ds4_gpu_tensor_read(g->routed_mid, 0, gpu_routed_mid_all, (uint64_t)DS4_N_EXPERT_USED * down_in_dim * sizeof(float)) != 0 &&
              ds4_gpu_tensor_read(g->routed_out, 0, gpu_routed, (uint64_t)DS4_N_EMBD * sizeof(float)) != 0 &&
              ds4_gpu_tensor_read(g->ffn_out, 0, gpu_ffn_out, (uint64_t)DS4_N_EMBD * sizeof(float)) != 0 &&
              ds4_gpu_tensor_read(g->cur_hc, 0, gpu_after_ffn_hc, hc_dim * sizeof(float)) != 0;

    if (ok) {
        fprintf(stderr,
```<｜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
(uint64_t)DS4_N_EMBD * sizeof(float)) != 0 &&
              ds4_gpu_tensor_read(g->cur_hc, 0, gpu_after_ffn_hc, hc_dim * sizeof(float)) != 0;

    if (ok) {
        fprintf(stderr,
                "ds4: Metal stage layer %u attn_cur=%g/%g attn_norm=%g/%g q=%g/%g kv=%g/%g attn_out=%g/%g after_attn_hc=%g/%g ffn_cur=%g/%g ffn_norm=%g/%g shared=%g/%g router_w=%g routed=%g/%g ffn_out=%g/%g after_ffn_hc=%g/%g\n",
                il,
                max_abs_diff(cpu_attn_cur, gpu_attn_cur, DS4_N_EMBD), rms_abs_diff(cpu_attn_cur, gpu_attn_cur, DS4_N_EMBD),
                max_abs_diff(cpu_attn_norm, gpu_attn_norm, DS4_N_EMBD), rms_abs_diff(cpu_attn_norm, gpu_attn_norm, DS4_N_EMBD),
                max_abs_diff(cpu_q, gpu_q, q_dim), rms_abs_diff(cpu_q, gpu_q, q_dim),
                max_abs_diff(cpu_kv, gpu_kv, DS4_N_HEAD_DIM), rms_abs_diff(cpu_kv, gpu_kv, DS4_N_HEAD_DIM),
                max_abs_diff(cpu_attn_out, gpu_attn_out, DS4_N_EMBD), rms_abs_diff(cpu_attn_out, gpu_attn_out, DS4_N_EMBD),
                max_abs_diff(cpu_after_attn_hc, gpu_after_attn_hc, hc_dim), rms_abs_diff(cpu_after_attn_hc, gpu_after_attn_hc, hc_dim),
                max_abs_diff(cpu_ffn_cur, gpu_ffn_cur, DS4_N_EMBD), rms_abs_diff(cpu_ffn_cur, gpu_ffn_cur, DS4_N_EMBD),
                max_abs_diff(cpu_ffn_norm, gpu_ffn_norm, DS4_N_EMBD), rms_abs_diff(cpu_ffn_norm, gpu_ffn_norm, DS4_N_EMBD),
                max_abs_diff(cpu_shared, gpu_shared, DS4_N_EMBD), rms_abs_diff(cpu_shared, gpu_shared, DS4_N_EMBD),
                max_abs_diff(expert_weight, gpu_expert_weight, DS4_N_EXPERT_USED),
                max_abs_diff(cpu_routed, gpu_routed, DS4_N_EMBD), rms_abs_diff(cpu_routed, gpu_routed, DS4_N_EMBD),
                max_abs_diff(cpu_ffn_out, gpu_ffn_out, DS4_N_EMBD), rms_abs_diff(cpu_ffn_out, gpu_ffn_out, DS4_N_EMBD),
                max_abs_diff(cpu_after_ffn_hc, gpu_after_ffn_hc, hc_dim), rms_abs_diff(cpu_after_ffn_hc, gpu_after_ffn_hc, hc_dim));
        fprintf(stderr,
                "ds4: Metal shared layer %u gate=%g/%g up=%g/%g mid=%g/%g down=%g/%g\n",
                il,
                max_abs_diff(cpu_shared_gate, gpu_shared_gate, shared_dim), rms_abs_diff(cpu_shared_gate, gpu_shared_gate, shared_dim),
                max_abs_diff(cpu_shared_up, gpu_shared_up, shared_dim), rms_abs_diff(cpu_shared_up, gpu_shared_up, shared_dim),
                max_abs_diff(cpu_shared_mid, gpu_shared_mid, shared_dim), rms_abs_diff(cpu_shared_mid, gpu_shared_mid, shared_dim),
                max_abs_diff(cpu_shared, gpu_shared, DS4_N_EMBD), rms_abs_diff(cpu_shared, gpu_shared, DS4_N_EMBD));
```

<｜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
(uint64_t)DS4_N_EMBD * sizeof(float)) != 0 &&
              ds4_gpu_tensor_read(g->cur_hc, 0, gpu_after_ffn_hc, hc_dim * sizeof(float)) != 0;

    if (ok) {
        fprintf(stderr,
                "ds4: Metal stage layer %u attn_cur=%g/%g attn_norm=%g/%g q=%g/%g kv=%g/%g attn_out=%g/%g after_attn_hc=%g/%g ffn_cur=%g/%g ffn_norm=%g/%g shared=%g/%g router_w=%g routed=%g/%g ffn_out=%g/%g after_ffn_hc=%g/%g\n",
                il,
                max_abs_diff(cpu_attn_cur, gpu_attn_cur, DS4_N_EMBD), rms_abs_diff(cpu_attn_cur, gpu_attn_cur, DS4_N_EMBD),
                max_abs_diff(cpu_attn_norm, gpu_attn_norm, DS4_N_EMBD), rms_abs_diff(cpu_attn_norm, gpu_attn_norm, DS4_N_EMBD),
                max_abs_diff(cpu_q, gpu_q, q_dim), rms_abs_diff(cpu_q, gpu_q, q_dim),
                max_abs_diff(cpu_kv, gpu_kv, DS4_N_HEAD_DIM), rms_abs_diff(cpu_kv, gpu_kv, DS4_N_HEAD_DIM),
                max_abs_diff(cpu_attn_out, gpu_attn_out, DS4_N_EMBD), rms_abs_diff(cpu_attn_out, gpu_attn_out, DS4_N_EMBD),
                max_abs_diff(cpu_after_attn_hc, gpu_after_attn_hc, hc_dim), rms_abs_diff(cpu_after_attn_hc, gpu_after_attn_hc, hc_dim),
                max_abs_diff(cpu_ffn_cur, gpu_ffn_cur, DS4_N_EMBD), rms_abs_diff(cpu_ffn_cur, gpu_ffn_cur, DS4_N_EMBD),
                max_abs_diff(cpu_ffn_norm, gpu_ffn_norm, DS4_N_EMBD), rms_abs_diff(cpu_ffn_norm, gpu_ffn_norm, DS4_N_EMBD),
                max_abs_diff(cpu_shared, gpu_shared, DS4_N_EMBD), rms_abs_diff(cpu_shared, gpu_shared, DS4_N_EMBD),
                max_abs_diff(expert_weight, gpu_expert_weight, DS4_N_EXPERT_USED),
                max_abs_diff(cpu_routed, gpu_routed, DS4_N_EMBD), rms_abs_diff(cpu_routed, gpu_routed, DS4_N_EMBD),
                max_abs_diff(cpu_ffn_out, gpu_ffn_out, DS4_N_EMBD), rms_abs_diff(cpu_ffn_out, gpu_ffn_out, DS4_N_EMBD),
                max_abs_diff(cpu_after_ffn_hc, gpu_after_ffn_hc, hc_dim), rms_abs_diff(cpu_after_ffn_hc, gpu_after_ffn_hc, hc_dim));
        fprintf(stderr,
                "ds4: Metal shared layer %u gate=%g/%g up=%g/%g mid=%g/%g down=%g/%g\n",
                il,
                max_abs_diff(cpu_shared_gate, gpu_shared_gate, shared_dim), rms_abs_diff(cpu_shared_gate, gpu_shared_gate, shared_dim),
                max_abs_diff(cpu_shared_up, gpu_shared_up, shared_dim), rms_abs_diff(cpu_shared_up, gpu_shared_up, shared_dim),
                max_abs_diff(cpu_shared_mid, gpu_shared_mid, shared_dim), rms_abs_diff(cpu_shared_mid, gpu_shared_mid, shared_dim),
                max_abs_diff(cpu_shared, gpu_shared, DS4_N_EMBD), rms_abs_diff(cpu_shared, gpu_shared, DS4_N_EMBD));
```<｜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
(uint64_t)DS4_N_EMBD * sizeof(float)) != 0 &&
              ds4_gpu_tensor_read(g->cur_hc, 0, gpu_after_ffn_hc, hc_dim * sizeof(float)) != 0;

    if (ok) {
        fprintf(stderr,
                "ds4: Metal stage layer %u attn_cur=%g/%g attn_norm=%g/%g q=%g/%g kv=%g/%g attn_out=%g/%g after_attn_hc=%g/%g ffn_cur=%g/%g ffn_norm=%g/%g shared=%g/%g router_w=%g routed=%g/%g ffn_out=%g/%g after_ffn_hc=%g/%g\n",
                il,
                max_abs_diff(cpu_attn_cur, gpu_attn_cur, DS4_N_EMBD), rms_abs_diff(cpu_attn_cur, gpu_attn_cur, DS4_N_EMBD),
                max_abs_diff(cpu_attn_norm, gpu_attn_norm, DS4_N_EMBD), rms_abs_diff(cpu_attn_norm, gpu_attn_norm, DS4_N_EMBD),
                max_abs_diff(cpu_q, gpu_q, q_dim), rms_abs_diff(cpu_q, gpu_q, q_dim),
                max_abs_diff(cpu_kv, gpu_kv, DS4_N_HEAD_DIM), rms_abs_diff(cpu_kv, gpu_kv, DS4_N_HEAD_DIM),
                max_abs_diff(cpu_attn_out, gpu_attn_out, DS4_N_EMBD), rms_abs_diff(cpu_attn_out, gpu_attn_out, DS4_N_EMBD),
                max_abs_diff(cpu_after_attn_hc, gpu_after_attn_hc, hc_dim), rms_abs_diff(cpu_after_attn_hc, gpu_after_attn_hc, hc_dim),
                max_abs_diff(cpu_ffn_cur, gpu_ffn_cur, DS4_N_EMBD), rms_abs_diff(cpu_ffn_cur, gpu_ffn_cur, DS4_N_EMBD),
                max_abs_diff(cpu_ffn_norm, gpu_ffn_norm, DS4_N_EMBD), rms_abs_diff(cpu_ffn_norm, gpu_ffn_norm, DS4_N_EMBD),
                max_abs_diff(cpu_shared, gpu_shared, DS4_N_EMBD), rms_abs_diff(cpu_shared, gpu_shared, DS4_N_EMBD),
                max_abs_diff(expert_weight, gpu_expert_weight, DS4_N_EXPERT_USED),
                max_abs_diff(cpu_routed, gpu_routed, DS4_N_EMBD), rms_abs_diff(cpu_routed, gpu_routed, DS4_N_EMBD),
                max_abs_diff(cpu_ffn_out, gpu_ffn_out, DS4_N_EMBD), rms_abs_diff(cpu_ffn_out, gpu_ffn_out, DS4_N_EMBD),
                max_abs_diff(cpu_after_ffn_hc, gpu_after_ffn_hc, hc_dim), rms_abs_diff(cpu_after_ffn_hc, gpu_after_ffn_hc, hc_dim));
        fprintf(stderr,
                "ds4: Metal shared layer %u gate=%g/%g up=%g/%g mid=%g/%g down=%g/%g\n",
                il,
                max_abs_diff(cpu_shared_gate, gpu_shared_gate, shared_dim), rms_abs_diff(cpu_shared_gate, gpu_shared_gate, shared_dim),
                max_abs_diff(cpu_shared_up, gpu_shared_up, shared_dim), rms_abs_diff(cpu_shared_up, gpu_shared_up, shared_dim),
                max_abs_diff(cpu_shared_mid, gpu_shared_mid, shared_dim), rms_abs_diff(cpu_shared_mid, gpu_shared_mid, shared_dim),
                max_abs_diff(cpu_shared, gpu_shared, DS4_N_EMBD), rms_abs_diff(cpu_shared, gpu_shared, DS4_N_EMBD));
```

<｜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
(uint64_t)DS4_N_EMBD * sizeof(float)) != 0 &&
              ds4_gpu_tensor_read(g->cur_hc, 0, gpu_after_ffn_hc, hc_dim * sizeof(float)) != 0;

    if (ok) {
        fprintf(stderr,
                "ds4: Metal stage layer %u attn_cur=%g/%g attn_norm=%g/%g q=%g/%g kv=%g/%g attn_out=%g/%g after_attn_hc=%g/%g ffn_cur=%g/%g ffn_norm=%g/%g shared=%g/%g router_w=%g routed=%g/%g ffn_out=%g/%g after_ffn_hc=%g/%g\n",
                il,
                max_abs_diff(cpu_attn_cur, gpu_attn_cur, DS4_N_EMBD), rms_abs_diff(cpu_attn_cur, gpu_attn_cur, DS4_N_EMBD),
                max_abs_diff(cpu_attn_norm, gpu_attn_norm, DS4_N_EMBD), rms_abs_diff(cpu_attn_norm, gpu_attn_norm, DS4_N_EMBD),
                max_abs_diff(cpu_q, gpu_q, q_dim), rms_abs_diff(cpu_q, gpu_q, q_dim),
                max_abs_diff(cpu_kv, gpu_kv, DS4_N_HEAD_DIM), rms_abs_diff(cpu_kv, gpu_kv, DS4_N_HEAD_DIM),
                max_abs_diff(cpu_attn_out, gpu_attn_out, DS4_N_EMBD), rms_abs_diff(cpu_attn_out, gpu_attn_out, DS4_N_EMBD),
                max_abs_diff(cpu_after_attn_hc, gpu_after_attn_hc, hc_dim), rms_abs_diff(cpu_after_attn_hc, gpu_after_attn_hc, hc_dim),
                max_abs_diff(cpu_ffn_cur, gpu_ffn_cur, DS4_N_EMBD), rms_abs_diff(cpu_ffn_cur, gpu_ffn_cur, DS4_N_EMBD),
                max_abs_diff(cpu_ffn_norm, gpu_ffn_norm, DS4_N_EMBD), rms_abs_diff(cpu_ffn_norm, gpu_ffn_norm, DS4_N_EMBD),
                max_abs_diff(cpu_shared, gpu_shared, DS4_N_EMBD), rms_abs_diff(cpu_shared, gpu_shared, DS4_N_EMBD),
                max_abs_diff(expert_weight, gpu_expert_weight, DS4_N_EXPERT_USED),
                max_abs_diff(cpu_routed, gpu_routed, DS4_N_EMBD), rms_abs_diff(cpu_routed, gpu_routed, DS4_N_EMBD),
                max_abs_diff(cpu_ffn_out, gpu_ffn_out, DS4_N_EMBD), rms_abs_diff(cpu_ffn_out, gpu_ffn_out, DS4_N_EMBD),
                max_abs_diff(cpu_after_ffn_hc, gpu_after_ffn_hc, hc_dim), rms_abs_diff(cpu_after_ffn_hc, gpu_after_ffn_hc, hc_dim));
        fprintf(stderr,
                "ds4: Metal shared layer %u gate=%g/%g up=%g/%g mid=%g/%g down=%g/%g\n",
                il,
                max_abs_diff(cpu_shared_gate, gpu_shared_gate, shared_dim), rms_abs_diff(cpu_shared_gate, gpu_shared_gate, shared_dim),
                max_abs_diff(cpu_shared_up, gpu_shared_up, shared_dim), rms_abs_diff(cpu_shared_up, gpu_shared_up, shared_dim),
                max_abs_diff(cpu_shared_mid, gpu_shared_mid, shared_dim), rms_abs_diff(cpu_shared_mid, gpu_shared_mid, shared_dim),
                max_abs_diff(cpu_shared, gpu_shared, DS4_N_EMBD), rms_abs_diff(cpu_shared, gpu_shared, DS4_N_EMBD));
```<｜Assistant｜><think>