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https://github.com/ggml-org/llama.cpp.git
synced 2026-09-19 17:24:57 +02:00
fix WebGPU
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@@ -435,6 +435,13 @@ static int test_backends(const llm_arch target_arch, const size_t seed, const gg
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continue; // TODO tensor shapes
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}
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// FIXME some models are segfaulting with WebGPU:
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#ifdef GGML_USE_WEBGPU
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if (arch == LLM_ARCH_QWEN3NEXT || arch == LLM_ARCH_QWEN35 || arch == LLM_ARCH_QWEN35MOE || arch == LLM_ARCH_KIMI_LINEAR) {
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continue;
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}
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#endif // GGML_USE_WEBGPU
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const bool encode = arch == LLM_ARCH_T5;
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for (bool moe : {false, true}) {
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if (moe && !moe_implemented(arch)) {
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@@ -454,7 +461,7 @@ static int test_backends(const llm_arch target_arch, const size_t seed, const gg
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auto model_and_ctx_dev = get_model_and_ctx(gguf_ctx.get(), seed, {dev});
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const std::vector<float> logits_dev = get_logits(model_and_ctx_dev.first.get(), model_and_ctx_dev.second.get(), tokens, encode);
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const double nmse_val = nmse(logits_cpu, logits_dev);
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const bool ok = nmse_val <= 1e-5;
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const bool ok = nmse_val <= 1e-4;
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all_ok = all_ok && ok;
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char nmse_str[10];
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snprintf(nmse_str, sizeof(nmse_str), "%.2e", nmse_val);
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@@ -130,7 +130,7 @@ int main(int argc, char ** argv) {
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const double nmse_val = nmse(logits_disk, logits_calc);
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LOG_INF("%s: NMSE=%.3e\n", __func__, nmse_val);
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if (nmse_val > 1e-5) {
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if (nmse_val > 1e-6) {
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printf("\033[1;31mFAIL\033[0m\n");
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return 1;
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}
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