fmt: trailing newlines on sglang trace fixtures

Pre-commit's end-of-file-fixer adds a trailing newline these 4 JSONs
were missing.

Co-Authored-By: Claude Sonnet 4.6 <noreply@anthropic.com>

Author: averyhNV

tests/trace/fi_trace_out_sglang/fused_add_rmsnorm_h3072.json

@@ -56,4 +56,4 @@
     }
   },
   "reference": "@torch.no_grad()\ndef _fused_add_rmsnorm_reference(hidden_states, residual, weight):\n    \"\"\"Fused Add + RMSNorm. Epsilon is fixed at 1e-6.\"\"\"\n    EPS = 1e-6\n    x = hidden_states.to(torch.float32) + residual.to(torch.float32)\n    inv_rms = torch.rsqrt(x.pow(2).mean(dim=-1, keepdim=True) + EPS)\n    y = (x * inv_rms) * weight.to(torch.float32)\n    return y.to(hidden_states.dtype)\n"
-}
+}

tests/trace/fi_trace_out_sglang/gqa_paged_decode_h24_kv128_d128_ps8.json

@@ -113,4 +113,4 @@
     }
   },
   "reference": "@torch.no_grad()\ndef _gqa_paged_decode_reference(q, k_cache, v_cache, kv_indptr, kv_indices, sm_scale):\n    batch_size, num_qo_heads, head_dim = q.shape\n    _, page_size, num_kv_heads, _ = k_cache.shape\n\n    output = torch.zeros(\n        (batch_size, num_qo_heads, head_dim), dtype=torch.bfloat16, device=q.device\n    )\n    lse = torch.full(\n        (batch_size, num_qo_heads), -float(\"inf\"), dtype=torch.float32, device=q.device\n    )\n\n    gqa_ratio = num_qo_heads // num_kv_heads\n    k_cache_f32 = k_cache.to(torch.float32)\n    v_cache_f32 = v_cache.to(torch.float32)\n\n    for b in range(batch_size):\n        page_start = int(kv_indptr[b].item())\n        page_end = int(kv_indptr[b + 1].item())\n        if page_start >= page_end:\n            output[b].zero_()\n            continue\n        # kv_indices are page IDs. Gather pages first, then flatten the\n        # [num_selected_pages, page_size] axis into a single token axis.\n        page_ids = kv_indices[page_start:page_end].to(torch.long)\n        k_b = k_cache_f32[page_ids].reshape(-1, num_kv_heads, head_dim)\n        v_b = v_cache_f32[page_ids].reshape(-1, num_kv_heads, head_dim)\n        q_b = q[b].to(torch.float32)  # [num_qo_heads, head_dim]\n        for h in range(num_qo_heads):\n            kv_h = h // gqa_ratio\n            logits = torch.matmul(q_b[h], k_b[:, kv_h].T) * sm_scale\n            lse[b, h] = torch.logsumexp(logits, dim=-1) / math.log(2.0)\n            attn = torch.softmax(logits, dim=-1)\n            output[b, h] = torch.matmul(attn, v_b[:, kv_h]).to(torch.bfloat16)\n\n    return output, lse\n"
-}
+}

tests/trace/fi_trace_out_sglang/gqa_paged_prefill_h24_kv128_d128_ps8.json

@@ -121,4 +121,4 @@
     }
   },
   "reference": "@torch.no_grad()\ndef _gqa_paged_prefill_reference(\n    q, k_cache, v_cache, qo_indptr, kv_indptr, kv_indices, sm_scale\n):\n    total_q, num_qo_heads, head_dim = q.shape\n    num_pages, page_size, num_kv_heads, _ = k_cache.shape\n    len_indptr = qo_indptr.shape[0]\n\n    output = torch.zeros(\n        (total_q, num_qo_heads, head_dim), dtype=torch.bfloat16, device=q.device\n    )\n    lse = torch.full(\n        (total_q, num_qo_heads), -float(\"inf\"), dtype=torch.float32, device=q.device\n    )\n\n    gqa_ratio = num_qo_heads // num_kv_heads\n    q_f32 = q.to(torch.float32)\n    k_cache_f32 = k_cache.to(torch.float32)\n    v_cache_f32 = v_cache.to(torch.float32)\n\n    for b in range(len_indptr - 1):\n        q_start = int(qo_indptr[b].item())\n        q_end = int(qo_indptr[b + 1].item())\n        kv_start = int(kv_indptr[b].item())\n        kv_end = int(kv_indptr[b + 1].item())\n        if q_start >= q_end or kv_start >= kv_end:\n            continue\n        # kv_indices are page IDs. Gather pages and flatten to a token axis.\n        page_ids = kv_indices[kv_start:kv_end].to(torch.long)\n        k_b = k_cache_f32[page_ids].reshape(-1, num_kv_heads, head_dim)\n        v_b = v_cache_f32[page_ids].reshape(-1, num_kv_heads, head_dim)\n        num_kv_tokens = k_b.shape[0]\n        q_b = q_f32[q_start:q_end]\n        delta = num_kv_tokens - q_b.shape[0]\n        for q_idx in range(q_b.shape[0]):\n            max_kv = min(q_idx + 1 + delta, num_kv_tokens)\n            if max_kv <= 0:\n                continue\n            global_q = q_start + q_idx\n            for h in range(num_qo_heads):\n                kv_h = h // gqa_ratio\n                logits = torch.matmul(q_b[q_idx, h], k_b[:max_kv, kv_h].T) * sm_scale\n                lse[global_q, h] = torch.logsumexp(logits, dim=-1) / math.log(2.0)\n                attn = torch.softmax(logits, dim=-1)\n                output[global_q, h] = torch.matmul(attn, v_b[:max_kv, kv_h]).to(\n                    torch.bfloat16\n                )\n\n    return output, lse\n"
-}
+}

tests/trace/fi_trace_out_sglang/rmsnorm_h3072.json

@@ -40,4 +40,4 @@
     }
   },
   "reference": "@torch.no_grad()\ndef _rmsnorm_reference(hidden_states, weight):\n    \"\"\"Root Mean Square Normalization. Epsilon is fixed at 1e-6.\"\"\"\n    EPS = 1e-6\n    x = hidden_states.to(torch.float32)\n    inv_rms = torch.rsqrt(x.pow(2).mean(dim=-1, keepdim=True) + EPS)\n    y = (x * inv_rms) * weight.to(torch.float32)\n    return y.to(hidden_states.dtype)\n"
-}
+}