sycl : support to malloc memory on device more than 4GB, update the doc and script (#17566)
Co-authored-by: Neo Zhang Jianyu <jianyu.zhang@intel.com>
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@ -42,6 +42,9 @@ The following releases are verified and recommended:
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## News
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- 2025.11
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- Support malloc memory on device more than 4GB.
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- 2025.2
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- Optimize MUL_MAT Q4_0 on Intel GPU for all dGPUs and built-in GPUs since MTL. Increase the performance of LLM (llama-2-7b.Q4_0.gguf) 21%-87% on Intel GPUs (MTL, ARL-H, Arc, Flex, PVC).
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|GPU|Base tokens/s|Increased tokens/s|Percent|
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@ -789,6 +792,8 @@ use 1 SYCL GPUs: [0] with Max compute units:512
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| GGML_SYCL_DISABLE_GRAPH | 0 or 1 (default) | Disable running computations through SYCL Graphs feature. Disabled by default because graph performance isn't yet better than non-graph performance. |
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| GGML_SYCL_DISABLE_DNN | 0 (default) or 1 | Disable running computations through oneDNN and always use oneMKL. |
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| ZES_ENABLE_SYSMAN | 0 (default) or 1 | Support to get free memory of GPU by sycl::aspect::ext_intel_free_memory.<br>Recommended to use when --split-mode = layer |
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| UR_L0_ENABLE_RELAXED_ALLOCATION_LIMITS | 0 (default) or 1 | Support malloc device memory more than 4GB.|
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## Known Issues
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@ -835,6 +840,14 @@ use 1 SYCL GPUs: [0] with Max compute units:512
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| The default context is too big. It leads to excessive memory usage.|Set `-c 8192` or a smaller value.|
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| The model is too big and requires more memory than what is available.|Choose a smaller model or change to a smaller quantization, like Q5 -> Q4;<br>Alternatively, use more than one device to load model.|
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- `ggml_backend_sycl_buffer_type_alloc_buffer: can't allocate 5000000000 Bytes of memory on device`
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You need to enable to support 4GB memory malloc by:
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```
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export UR_L0_ENABLE_RELAXED_ALLOCATION_LIMITS=1
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set UR_L0_ENABLE_RELAXED_ALLOCATION_LIMITS=1
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```
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### **GitHub contribution**:
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Please add the `SYCL :` prefix/tag in issues/PRs titles to help the SYCL contributors to check/address them without delay.
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@ -15,6 +15,9 @@ MODEL_FILE=models/llama-2-7b.Q4_0.gguf
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NGL=99
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CONTEXT=4096
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#support malloc device memory more than 4GB.
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export UR_L0_ENABLE_RELAXED_ALLOCATION_LIMITS=1
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if [ $# -gt 0 ]; then
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GGML_SYCL_DEVICE=$1
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echo "use $GGML_SYCL_DEVICE as main GPU"
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@ -6,7 +6,7 @@
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# If you want more control, DPC++ Allows selecting a specific device through the
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# following environment variable
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#export ONEAPI_DEVICE_SELECTOR="level_zero:0"
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export ONEAPI_DEVICE_SELECTOR="level_zero:0"
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source /opt/intel/oneapi/setvars.sh
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#export GGML_SYCL_DEBUG=1
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@ -18,11 +18,14 @@ MODEL_FILE=models/Meta-Llama-3.1-8B-Instruct-Q4_K_M.gguf
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NGL=99 # Layers offloaded to the GPU. If the device runs out of memory, reduce this value according to the model you are using.
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CONTEXT=4096
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#support malloc device memory more than 4GB.
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export UR_L0_ENABLE_RELAXED_ALLOCATION_LIMITS=1
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if [ $# -gt 0 ]; then
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GGML_SYCL_DEVICE=$1
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echo "Using $GGML_SYCL_DEVICE as the main GPU"
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ZES_ENABLE_SYSMAN=1 ./build/bin/llama-cli -m ${MODEL_FILE} -p "${INPUT_PROMPT}" -n 400 -e -ngl ${NGL} -c ${CONTEXT} -mg $GGML_SYCL_DEVICE -sm none
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ZES_ENABLE_SYSMAN=1 ./build/bin/llama-cli -m ${MODEL_FILE} -p "${INPUT_PROMPT}" -n 400 -e -ngl ${NGL} -s 0 -c ${CONTEXT} -mg $GGML_SYCL_DEVICE -sm none
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else
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#use multiple GPUs with same max compute units
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ZES_ENABLE_SYSMAN=1 ./build/bin/llama-cli -m ${MODEL_FILE} -p "${INPUT_PROMPT}" -n 400 -e -ngl ${NGL} -c ${CONTEXT}
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ZES_ENABLE_SYSMAN=1 ./build/bin/llama-cli -m ${MODEL_FILE} -p "${INPUT_PROMPT}" -n 400 -e -ngl ${NGL} -s 0 -c ${CONTEXT}
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fi
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@ -5,5 +5,7 @@
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set INPUT2="Building a website can be done in 10 simple steps:\nStep 1:"
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@call "C:\Program Files (x86)\Intel\oneAPI\setvars.bat" intel64 --force
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:: support malloc device memory more than 4GB.
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set UR_L0_ENABLE_RELAXED_ALLOCATION_LIMITS=1
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.\build\bin\llama-cli.exe -m models\llama-2-7b.Q4_0.gguf -p %INPUT2% -n 400 -e -ngl 99 -s 0
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@ -5,5 +5,7 @@
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set INPUT2="Building a website can be done in 10 simple steps:\nStep 1:"
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@call "C:\Program Files (x86)\Intel\oneAPI\setvars.bat" intel64 --force
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:: support malloc device memory more than 4GB.
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set UR_L0_ENABLE_RELAXED_ALLOCATION_LIMITS=1
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.\build\bin\llama-cli.exe -m models\Meta-Llama-3.1-8B-Instruct-Q4_K_M.gguf -p %INPUT2% -n 400 -e -ngl 99
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.\build\bin\llama-cli.exe -m models\Meta-Llama-3.1-8B-Instruct-Q4_K_M.gguf -p %INPUT2% -n 400 -s 0 -e -ngl 99
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@ -91,7 +91,10 @@ if (GGML_SYCL_F16)
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add_compile_definitions(GGML_SYCL_F16)
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endif()
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if (GGML_SYCL_TARGET STREQUAL "NVIDIA")
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if (GGML_SYCL_TARGET STREQUAL "INTEL")
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add_compile_definitions(GGML_SYCL_WARP_SIZE=16)
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target_link_options(ggml-sycl PRIVATE -Xs -ze-intel-greater-than-4GB-buffer-required)
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elseif (GGML_SYCL_TARGET STREQUAL "NVIDIA")
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add_compile_definitions(GGML_SYCL_WARP_SIZE=32)
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elseif (GGML_SYCL_TARGET STREQUAL "AMD")
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# INFO: Allowed Sub_group_sizes are not consistent through all
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@ -100,7 +103,8 @@ elseif (GGML_SYCL_TARGET STREQUAL "AMD")
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# Target archs tested working: gfx1030, gfx1031, (Only tested sub_group_size = 32)
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add_compile_definitions(GGML_SYCL_WARP_SIZE=32)
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else()
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add_compile_definitions(GGML_SYCL_WARP_SIZE=16)
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# default for other target
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add_compile_definitions(GGML_SYCL_WARP_SIZE=32)
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endif()
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if (GGML_SYCL_GRAPH)
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@ -515,9 +515,6 @@ void ggml_sycl_cpy(ggml_backend_sycl_context & ctx, const ggml_tensor * src0, co
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const int64_t ne = ggml_nelements(src0);
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GGML_ASSERT(ne == ggml_nelements(src1));
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GGML_ASSERT(ggml_nbytes(src0) <= INT_MAX);
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GGML_ASSERT(ggml_nbytes(src1) <= INT_MAX);
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GGML_TENSOR_BINARY_OP_LOCALS01;
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SYCL_CHECK(ggml_sycl_set_device(ctx.device));
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