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sycl : Overcoming workaround for mmap() allocation on Windows (#13482)
* Remove mmap workaround on windows After some testing I found that mmap is supported on windows and for many GPUs on Linux. Therefore I remove the workaround for windows since it is not necessary. * Update llama-bench README SYCL backend introduced a workaround that allows execution of llama-bench also without specifying `--mmp 0` flag
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@ -385,16 +385,17 @@ static void ggml_backend_sycl_buffer_set_tensor(ggml_backend_buffer_t buffer,
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ggml_backend_sycl_buffer_context * ctx = ( ggml_backend_sycl_buffer_context *)buffer->context;
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ggml_sycl_set_device(ctx->device);
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auto stream = &(dpct::dev_mgr::instance().get_device(ctx->device).default_queue());
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SYCL_CHECK(
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CHECK_TRY_ERROR(dpct::dev_mgr::instance().get_device(ctx->device).queues_wait_and_throw()));
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SYCL_CHECK(CHECK_TRY_ERROR(dpct::dev_mgr::instance().get_device(ctx->device).queues_wait_and_throw()));
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#ifndef _WIN32
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// Note: Use host buffer to save the data from mmap(), then copy to device. It's workaround for mmap() issue on PVC GPU.
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// 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.
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char* host_buf = (char*)malloc(size);
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char * host_buf = (char *) malloc(size);
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memcpy(host_buf, data, size);
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SYCL_CHECK(
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CHECK_TRY_ERROR((*stream).memcpy((char *)tensor->data + offset, host_buf, size)
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.wait()));
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SYCL_CHECK(CHECK_TRY_ERROR((*stream).memcpy((char *) tensor->data + offset, host_buf, size).wait()));
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free(host_buf);
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#else
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SYCL_CHECK(CHECK_TRY_ERROR((*stream).memcpy((char *) tensor->data + offset, data, size).wait()));
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#endif
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}
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catch (sycl::exception const &exc) {
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std::cerr << exc.what() << "Exception caught at file:" << __FILE__
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@ -80,10 +80,6 @@ Using the `-d <n>` option, each test can be run at a specified context depth, pr
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For a description of the other options, see the [main example](../main/README.md).
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Note:
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- When using SYCL backend, there would be hang issue in some cases. Please set `--mmp 0`.
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## Examples
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### Text generation with different models
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