src: cpu: aarch64: lowp_matmul_sq: Make weights constant #2212
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| arm_compute::TensorShape(N(), K()), 1, | ||
| acl_utils::get_acl_data_t(wei_d.data_type(), true), | ||
| arm_compute::QuantizationInfo(1.0, 0, true)); | ||
| almc_.wei_tensor_info.set_are_values_constant(true); |
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Hi @fadara01 ,
What does constant mean here? Is it about constant values within the tensor or just that the shape is constant?
If it is about constant values in weights, it does not seem that primitive creation takes the weights, does it mean the primitive state changes upon first execution?
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| status_t execute(const exec_ctx_t &ctx) const { | ||
| std::lock_guard<std::mutex> _lock {this->mtx}; |
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Is the mutex protection only about the quantization parameters that are reset, or is there something else due to constant weights?
mgouicem
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mgouicem
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One general note here, oneDNN primitive API has no concept of constant memory object, so such an optimization is unsafe (there is no guarantee from primitive implementation perspective that weights will not change between execute calls).
oneDNN Graph API has that concept though, and it might make sense to add it to primitive API if it helps. But I would hold off commiting this patch unless we extend API with constant memory objects.
Currently limited to SYCL with L0 backend.
The function will run over every M creating a full brg descriptor for it. For large W it would cause significant slowdown. Reducing the set by checking only relevant M, it improves the execution time.
Setting the weights as constant allows us to avoid redundant pretranspose operations in Arm Compute Library (ACL) every time execute is called (they are now run once and cached). This delives big speedups especially for relatively small matmuls. Note that this is a temp fix that needs to be handled carefully by primitive caches in frameworks, since the ACL object is now holding more state - i.e. we want to make sure that the cahce maps a layer with a specific set of weights to the oneDNN primitive storing those weights. We're currently working on the proper fix for this which involves making lowp_gemm stateless and fixed-format in ACL and oneDNN.
Setting the weights as constant allows us to avoid redundant pretranspose operations in Arm Compute Library (ACL) every time execute is called (they are now run once and cached). This delives big speedups especially for relatively small matmuls. Note that this is a temp fix that needs to be handled carefully by primitive caches in frameworks, since the ACL object is now holding more state - i.e. we want to make sure that the cahce maps a layer with a specific set of weights to the oneDNN primitive storing those weights. We're currently working on the proper fix for this which involves making lowp_gemm stateless and fixed-format in ACL and oneDNN.
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Description
Setting the weights as constant allows us to avoid redundant pretranspose operations in Arm Compute Library (ACL) every time execute is called (they are now run once and cached). This delives big speedups especially for relatively small matmuls.
Note that this is a temp fix that needs to be handled carefully by primitive caches in frameworks, since the ACL object is now holding more state - i.e. we want to make sure that the cahce maps a layer with a specific set of weights to the oneDNN primitive storing those weights.
We're currently working on the proper fix for this which involves making lowp_gemm stateless and fixed-format in ACL and oneDNN.
Fixes # (github issue)
Checklist
General
make testandmake test_benchdnn_*) pass locally for each commit?Performance improvements
New features
Bug fixes
RFC PR