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passes but needs higher ktol of 0.05 vs. 0.0001
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...nels/cpu/aarch64/kleidi/kai_matmul_clamp_f32_qai8dxp1x8_qsi4c32p4x8_1x4x32_neon_dotprod.h
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| // namespace example | ||
| // Copyright (c) Meta Platforms, Inc. and affiliates. | ||
| // All rights reserved. | ||
| // | ||
| // This source code is licensed under the license found in the | ||
| // LICENSE file in the root directory of this source tree. | ||
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| #pragma once | ||
| #include <cassert> | ||
| #include <cstddef> | ||
| #include <limits> | ||
| #include <vector> | ||
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| #include <torchao/experimental/kernels/cpu/aarch64/kleidi/pack.h> | ||
| #include <torchao/experimental/kernels/cpu/aarch64/kleidi/kai_matmul_clamp_f32_qai8dxp_qsi4c32p.h> | ||
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| #include <kai/kai_common.h> | ||
| #include <kai/ukernels/matmul/matmul_clamp_f32_qai8dxp_qsi4c32p/kai_matmul_clamp_f32_qai8dxp1x8_qsi4c32p4x8_1x4x32_neon_dotprod.h> | ||
| #include <kai/ukernels/matmul/matmul_clamp_f32_qai8dxp_qsi4c32p/kai_matmul_clamp_f32_qai8dxp_qsi4c32p_interface.h> | ||
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| namespace torchao::kernels::cpu::aarch64::kleidi { | ||
| namespace kai_matmul_clamp_f32_qai8dxp_qsi4c32p { | ||
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| using ukernel = struct kai_matmul_clamp_f32_qai8dxp_qsi4c32p_ukernel; | ||
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| namespace neon_dotprod_1x4x32 { | ||
| ukernel get_ukernel() { | ||
| return ukernel{ | ||
| .get_m_step = | ||
| kai_get_m_step_matmul_clamp_f32_qai8dxp1x8_qsi4c32p4x8_1x4x32_neon_dotprod, | ||
| .get_n_step = | ||
| kai_get_n_step_matmul_clamp_f32_qai8dxp1x8_qsi4c32p4x8_1x4x32_neon_dotprod, | ||
| .get_mr = | ||
| kai_get_mr_matmul_clamp_f32_qai8dxp1x8_qsi4c32p4x8_1x4x32_neon_dotprod, | ||
| .get_nr = | ||
| kai_get_nr_matmul_clamp_f32_qai8dxp1x8_qsi4c32p4x8_1x4x32_neon_dotprod, | ||
| .get_kr = | ||
| kai_get_kr_matmul_clamp_f32_qai8dxp1x8_qsi4c32p4x8_1x4x32_neon_dotprod, | ||
| .get_sr = | ||
| kai_get_sr_matmul_clamp_f32_qai8dxp1x8_qsi4c32p4x8_1x4x32_neon_dotprod, | ||
| .get_lhs_packed_offset = | ||
| kai_get_lhs_packed_offset_matmul_clamp_f32_qai8dxp1x8_qsi4c32p4x8_1x4x32_neon_dotprod, | ||
| .get_rhs_packed_offset = | ||
| kai_get_rhs_packed_offset_matmul_clamp_f32_qai8dxp1x8_qsi4c32p4x8_1x4x32_neon_dotprod, | ||
| .get_dst_offset = | ||
| kai_get_dst_offset_matmul_clamp_f32_qai8dxp1x8_qsi4c32p4x8_1x4x32_neon_dotprod, | ||
| .get_dst_size = | ||
| kai_get_dst_size_matmul_clamp_f32_qai8dxp1x8_qsi4c32p4x8_1x4x32_neon_dotprod, | ||
| .run_matmul = | ||
| kai_run_matmul_clamp_f32_qai8dxp1x8_qsi4c32p4x8_1x4x32_neon_dotprod}; | ||
| } | ||
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||
| size_t roundup(size_t a, size_t b) { | ||
| return ((a + b - 1) / b) * b; | ||
| } | ||
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| int activation_data_size(int m, int k, int group_size) { | ||
| auto ukernel = get_ukernel(); | ||
| auto lhs_packing = get_lhs_packing(); | ||
| return lhs_packing.get_lhs_packed_size( | ||
| m, k, ukernel.get_mr(), ukernel.get_kr(), ukernel.get_sr()); | ||
| } | ||
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| void prepare_activation_data( | ||
| void* activation_data, | ||
| // Inputs | ||
| int m, | ||
| int k, | ||
| // Ignored if has_weight_zeros = false | ||
| int group_size, | ||
| const float* activations) { | ||
| auto ukernel = get_ukernel(); | ||
| auto lhs_pack = get_lhs_packing(); | ||
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| lhs_pack.run_lhs_pack( | ||
| m, | ||
| k, | ||
| ukernel.get_mr(), | ||
| ukernel.get_kr(), | ||
| ukernel.get_sr(), | ||
| /*m_index_start=*/0, | ||
| activations, | ||
| /*lhs_stride=*/k * sizeof(float), | ||
| activation_data); | ||
| } | ||
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| int weight_data_size(int n, int k, int group_size) { | ||
| auto ukernel = get_ukernel(); | ||
| auto rhs_pack = get_rhs_packing(); | ||
| return rhs_pack.get_rhs_packed_size( | ||
| n, | ||
| k, | ||
| ukernel.get_nr(), | ||
| ukernel.get_kr(), | ||
| ukernel.get_sr(), | ||
| group_size, | ||
| kai_datatype::kai_dt_bf16); | ||
| } | ||
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| inline uint16_t get_bf16_from_float(float f) { | ||
| uint16_t bf16; | ||
| #if __BYTE_ORDER__ == __ORDER_BIG_ENDIAN__ | ||
| memcpy(&bf16, &f, sizeof(uint16_t)); | ||
| #else | ||
| const void* fp = reinterpret_cast<const void*>( | ||
| reinterpret_cast<uintptr_t>(&f) + sizeof(float) - sizeof(uint16_t)); | ||
| memcpy(&bf16, fp, sizeof(uint16_t)); | ||
| #endif // __BYTE_ORDER__ | ||
| return bf16; | ||
| } | ||
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||
| // TODO: move most of these functions in the parent namespace and take in | ||
| // ukernel as a parameter | ||
| void prepare_weight_data( | ||
| void* weight_data, | ||
| // Inputs | ||
| int n, | ||
| int k, | ||
| int group_size, | ||
| const int8_t* weight_qvals, | ||
| const float* weight_scales, | ||
| const int8_t* weight_zeros) { | ||
| // TODO - remove this constraint and pad when possible | ||
| assert(n % 2 == 0); | ||
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| assert(group_size % 32 == 0); | ||
| assert(k % group_size == 0); | ||
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| // Convert scales to bf16 | ||
| // TODO SIMDify this | ||
| size_t n_groups = n * k / group_size; | ||
| auto weight_scales_bf16 = std::vector<uint16_t>(n_groups, 0); | ||
| for (size_t i = 0; i < n_groups; i++) { | ||
| assert(weight_zeros[i] == 0); | ||
| weight_scales_bf16[i] = get_bf16_from_float(weight_scales[i]); | ||
| } | ||
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| // Prepack weights before packing | ||
| // TODO SIMDify this | ||
| auto packed_weight_qvals = std::vector<uint8_t>(n * k / 2, 255); | ||
| uint8_t wzp = 8; | ||
| for (size_t i = 0; i < n * k; i += 2) { | ||
| const uint8_t low = static_cast<uint8_t>(weight_qvals[i] + wzp); | ||
| const uint8_t high = static_cast<uint8_t>(weight_qvals[i+1] + wzp); | ||
| packed_weight_qvals[i / 2] = ((high << 4) | (low & 0xF)); | ||
| } | ||
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| // Parameters for packing | ||
| rhs_packing::qparams_t qparams{ | ||
| .lhs_zero_point=1, .rhs_zero_point=wzp, .scale_dt = kai_datatype::kai_dt_bf16}; | ||
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| auto ukernel = get_ukernel(); | ||
| auto rhs_pack = get_rhs_packing(); | ||
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| rhs_pack.run_rhs_pack( | ||
| /*groups=*/1, | ||
| n, | ||
| k, | ||
| ukernel.get_nr(), | ||
| ukernel.get_kr(), | ||
| ukernel.get_sr(), | ||
| group_size, | ||
| /*rhs=*/reinterpret_cast<const uint8_t*>(packed_weight_qvals.data()), | ||
| /*rhs_stride=*/roundup(k, 2) / 2, | ||
| /*bias=*/nullptr, // TODO fix APIs to move bias here | ||
| /*scale=*/reinterpret_cast<const uint16_t*>(weight_scales_bf16.data()), | ||
| /*scale_stride=*/ sizeof(uint16_t) * (roundup(k, group_size) / group_size), | ||
| /*rhs_packed=*/weight_data, | ||
| /*extra_bytes=*/0, | ||
| /*qparams=*/&qparams); | ||
| } | ||
|
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| void kernel( | ||
| // Outputs | ||
| float32_t* output, | ||
| // Inputs | ||
| int output_m_stride, | ||
| int m, | ||
| int n, | ||
| int k, | ||
| int group_size, | ||
| const void* weight_data, | ||
| const void* activation_data, | ||
| // Not applied if nullptr | ||
| const float* bias, | ||
| // zeros if has_clamp = false | ||
| float clamp_min, | ||
| float clamp_max) { | ||
| assert(output_m_stride == n); | ||
| if (clamp_min == clamp_max && clamp_min == 0) { | ||
| clamp_min = std::numeric_limits<float_t>::lowest(); | ||
| clamp_max = std::numeric_limits<float_t>::max(); | ||
| } | ||
| auto ukernel = get_ukernel(); | ||
| ukernel.run_matmul( | ||
| m, | ||
| n, | ||
| k, | ||
| group_size, | ||
| activation_data, | ||
| weight_data, | ||
| output, | ||
| /*dst_stride_row=*/n * sizeof(float), | ||
| /*dst_stride_col=*/sizeof(float), | ||
| clamp_min, | ||
| clamp_max); | ||
| } | ||
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| size_t get_alignement() { | ||
| return 16; | ||
| } | ||
| } // namespace neon_dotprod_1x4x32 | ||
| } // namespace kai_matmul_clamp_f32_qai8dxp_qsi4c32p | ||
| } // namespace torchao::kernels::cpu::aarch64::kleidi |
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