diff --git a/ggml/src/ggml-sycl/ggml-sycl.cpp b/ggml/src/ggml-sycl/ggml-sycl.cpp index 5ff7fa13d..2f61be714 100644 --- a/ggml/src/ggml-sycl/ggml-sycl.cpp +++ b/ggml/src/ggml-sycl/ggml-sycl.cpp @@ -385,16 +385,17 @@ static void ggml_backend_sycl_buffer_set_tensor(ggml_backend_buffer_t buffer, ggml_backend_sycl_buffer_context * ctx = ( ggml_backend_sycl_buffer_context *)buffer->context; ggml_sycl_set_device(ctx->device); auto stream = &(dpct::dev_mgr::instance().get_device(ctx->device).default_queue()); - SYCL_CHECK( - CHECK_TRY_ERROR(dpct::dev_mgr::instance().get_device(ctx->device).queues_wait_and_throw())); + SYCL_CHECK(CHECK_TRY_ERROR(dpct::dev_mgr::instance().get_device(ctx->device).queues_wait_and_throw())); +#ifndef _WIN32 // Note: Use host buffer to save the data from mmap(), then copy to device. It's workaround for mmap() issue on PVC GPU. // This function will be called during load model from disk. Use memory buffer replace dynamic won't save more time and brings potential memory leak risk here. - char* host_buf = (char*)malloc(size); + char * host_buf = (char *) malloc(size); memcpy(host_buf, data, size); - SYCL_CHECK( - CHECK_TRY_ERROR((*stream).memcpy((char *)tensor->data + offset, host_buf, size) - .wait())); + SYCL_CHECK(CHECK_TRY_ERROR((*stream).memcpy((char *) tensor->data + offset, host_buf, size).wait())); free(host_buf); +#else + SYCL_CHECK(CHECK_TRY_ERROR((*stream).memcpy((char *) tensor->data + offset, data, size).wait())); +#endif } catch (sycl::exception const &exc) { std::cerr << exc.what() << "Exception caught at file:" << __FILE__ diff --git a/tools/llama-bench/README.md b/tools/llama-bench/README.md index 0479f81a3..31a273087 100644 --- a/tools/llama-bench/README.md +++ b/tools/llama-bench/README.md @@ -80,10 +80,6 @@ Using the `-d ` option, each test can be run at a specified context depth, pr For a description of the other options, see the [main example](../main/README.md). -Note: - -- When using SYCL backend, there would be hang issue in some cases. Please set `--mmp 0`. - ## Examples ### Text generation with different models