diff --git a/config/terapool_spatz4_fpu.mk b/config/terapool_spatz4_fpu.mk new file mode 100644 index 000000000..3634f0a2e --- /dev/null +++ b/config/terapool_spatz4_fpu.mk @@ -0,0 +1,62 @@ +# Copyright 2021 ETH Zurich and University of Bologna. +# Licensed under the Apache License, Version 2.0, see LICENSE for details. +# SPDX-License-Identifier: Apache-2.0 + +# Author: Matheus Cavalcante, ETH Zurich + +################ +## TeraPool ## +################ + +# Global Control +terapool ?= 1 + +# Number of cores +num_cores ?= 256 + +# Number of groups +num_groups ?= 4 + +# Number of cores per Terapool tile +num_cores_per_tile ?= 2 + +# Number of sub groups per Terapool group +num_sub_groups_per_group ?= 4 + +# L1 scratchpad banking factor +banking_factor ?= 4 + +# Access latency between remote groups +# Options: "7", "9" or "11": +remote_group_latency_cycles ?= 7 + +# Radix for hierarchical AXI interconnect +axi_hier_radix ?= 9 + +# Number of AXI masters per group +axi_masters_per_group ?= 4 + +# Number of DMA backends in each group +dmas_per_group ?= 4 + +# L2 Banks/Channels +l2_banks = 16 + +# Makefile RTL Filtering Control +subgroup_rtl = 1 + +# Activate Spatz and RVV +spatz ?= 1 + +# Lenght of single vector register +vlen ?= 512 + +# Number of IPUs +n_ipu ?= 4 + +n_fpu ?= 4 + +# Deactivate the XpulpIMG extension +xpulpimg ?= 0 + +rvf ?= 1 diff --git a/hardware/Makefile b/hardware/Makefile index 6722f9f27..b068b675c 100644 --- a/hardware/Makefile +++ b/hardware/Makefile @@ -102,6 +102,9 @@ vlog_defs += -DDMAS_PER_GROUP=$(dmas_per_group) vlog_defs += -DAXI_HIER_RADIX=$(axi_hier_radix) -DAXI_MASTERS_PER_GROUP=$(axi_masters_per_group) vlog_defs += -DSEQ_MEM_SIZE=$(seq_mem_size) -DXQUEUE_SIZE=$(xqueue_size) # This parameter is only used for TeraPool configurations +ifdef terapool + vlog_defs += -DTERAPOOL=$(terapool) +endif vlog_defs += -DNUM_SUB_GROUPS_PER_GROUP=$(num_sub_groups_per_group) -DREMOTE_GROUP_LATENCY_CYCLES=$(remote_group_latency_cycles) ifeq ($(spatz), 1) diff --git a/hardware/scripts/questa/wave.tcl b/hardware/scripts/questa/wave.tcl index fc3bfaf4d..481d08a03 100644 --- a/hardware/scripts/questa/wave.tcl +++ b/hardware/scripts/questa/wave.tcl @@ -30,7 +30,7 @@ add wave /mempool_tb/wfi # Add all cores from group 0 tile 0 for {set core 0} {$core < [examine -radix dec mempool_pkg::NumCoresPerTile]} {incr core} { - if {$config == {terapool}} { + if {$config == {terapool_spatz4_fpu} | $config == {terapool}} { do ../scripts/questa/wave_core.tcl 0 0 0 $core } else { do ../scripts/questa/wave_core.tcl 0 0 $core @@ -38,7 +38,7 @@ for {set core 0} {$core < [examine -radix dec mempool_pkg::NumCoresPerTile]} {i } # Add specific cores from different tiles -if {$config == {terapool}} { +if {$config == {terapool_spatz4_fpu} | $config == {terapool}} { do ../scripts/questa/wave_core.tcl 1 0 0 0 } else { do ../scripts/questa/wave_core.tcl 1 0 0 @@ -47,7 +47,7 @@ if {$config == {terapool}} { # Add groups for {set group 0} {$group < [examine -radix dec /mempool_pkg::NumGroups]} {incr group} { # Add tiles - if {$config == {terapool}} { + if {$config == {terapool_spatz4_fpu} | $config == {terapool}} { for {set subgroup 0} {$subgroup < [expr min(4,[examine -radix dec /mempool_pkg::NumSubGroupsPerGroup])]} {incr subgroup} { for {set tile 0} {$tile < [expr min(4,[examine -radix dec /mempool_pkg::NumTilesPerSubGroup])]} {incr tile} { do ../scripts/questa/wave_tile.tcl $group $subgroup $tile @@ -63,14 +63,14 @@ for {set group 0} {$group < [examine -radix dec /mempool_pkg::NumGroups]} {incr for {set tgtgroup 0} {$tgtgroup < [examine -radix dec /mempool_pkg::NumGroups]} {incr tgtgroup} { if {$tgtgroup != $group} { set interco_idx [expr $group ^ $tgtgroup] - if {$config == {terapool}} { + if {$config == {terapool_spatz4_fpu} | $config == {terapool}} { add wave -group group_[$group] -group interconnect_to_group[$tgtgroup] /mempool_tb/dut/i_mempool_cluster/gen_groups[$group]/gen_rtl_group/i_group/gen_remote_interco[$interco_idx]/i_remote_interco/* } else { add wave -group group_[$group] -group interconnect_to_group[$tgtgroup] /mempool_tb/dut/i_mempool_cluster/gen_groups[$group]/i_group/gen_remote_interco[$interco_idx]/i_remote_interco/* } } } - if {$config != {terapool}} { + if {$config != {terapool} & $config != {terapool_spatz4_fpu}} { add wave -group group_[$group] -group interconnect_local /mempool_tb/dut/i_mempool_cluster/gen_groups[$group]/i_group/i_local_interco/* } } @@ -83,7 +83,7 @@ add wave -Group Control_Registers /mempool_tb/dut/i_ctrl_registers/* add wave -Group DMA /mempool_tb/dut/i_mempool_dma/* add wave -Group DMA -Group Reg /mempool_tb/dut/i_mempool_dma/i_mempool_dma_frontend_reg_top/* for {set group 0} {$group < [examine -radix dec /mempool_pkg::NumGroups]} {incr group} { - if {$config == {terapool}} { + if {$config == {terapool_spatz4_fpu} | $config == {terapool}} { add wave -Group DMA_midend_${group} /mempool_tb/dut/i_mempool_cluster/gen_groups[$group]/gen_rtl_group/i_group/i_idma_distributed_midend/NoMstPorts add wave -Group DMA_midend_${group} /mempool_tb/dut/i_mempool_cluster/gen_groups[$group]/gen_rtl_group/i_group/i_idma_distributed_midend/DmaRegionWidth add wave -Group DMA_midend_${group} /mempool_tb/dut/i_mempool_cluster/gen_groups[$group]/gen_rtl_group/i_group/i_idma_distributed_midend/DmaRegionStart @@ -121,7 +121,7 @@ add wave -Group DMA_midend_cluster /mempool_tb/dut/i_mempool_cluster/i_idma_dist add wave -Group DMA_split /mempool_tb/dut/i_mempool_cluster/i_idma_split_midend/* -if {$config == {terapool}} { +if {$config == {terapool_spatz4_fpu} | $config == {terapool}} { do ../scripts/questa/wave_cache.tcl 0 0 0 0 } else { do ../scripts/questa/wave_cache.tcl 0 0 0 diff --git a/hardware/scripts/questa/wave_cache.tcl b/hardware/scripts/questa/wave_cache.tcl index ecbcd16cc..ae8a0cc7d 100644 --- a/hardware/scripts/questa/wave_cache.tcl +++ b/hardware/scripts/questa/wave_cache.tcl @@ -4,7 +4,7 @@ # Create cache for core $3 from group $1 tile $2 (core_id=NUM_CORES_PER_group*$1+NUM_CORES_PER_TILE*$2+$3) -if {$config == {terapool}} { +if {$config == {terapool_spatz4_fpu} | $config == {terapool}} { add wave -noupdate -group cache[$1][$2][$3][$4] -divider Parameters add wave -noupdate -group cache[$1][$2][$3][$4] /mempool_tb/dut/i_mempool_cluster/gen_groups[$1]/gen_rtl_group/i_group/gen_sub_groups[$2]/gen_rtl_sg/i_sub_group/gen_tiles[$3]/i_tile/gen_caches[$4]/i_snitch_icache/NR_FETCH_PORTS add wave -noupdate -group cache[$1][$2][$3][$4] /mempool_tb/dut/i_mempool_cluster/gen_groups[$1]/gen_rtl_group/i_group/gen_sub_groups[$2]/gen_rtl_sg/i_sub_group/gen_tiles[$3]/i_tile/gen_caches[$4]/i_snitch_icache/L0_LINE_COUNT diff --git a/hardware/scripts/questa/wave_core.tcl b/hardware/scripts/questa/wave_core.tcl index a553c1856..25a3f2a33 100644 --- a/hardware/scripts/questa/wave_core.tcl +++ b/hardware/scripts/questa/wave_core.tcl @@ -3,7 +3,7 @@ # SPDX-License-Identifier: SHL-0.51 # Create group for core $3 from group $1 tile $2 (core_id=NUM_CORES_PER_group*$1+NUM_CORES_PER_TILE*$2+$3) -if {$config == {terapool}} { +if {$config == {terapool_spatz4_fpu} | $config == {terapool}} { add wave -noupdate -group core[$1][$2][$3][$4] -group Params /mempool_tb/dut/i_mempool_cluster/gen_groups[$1]/gen_rtl_group/i_group/gen_sub_groups[$2]/gen_rtl_sg/i_sub_group/gen_tiles[$3]/i_tile/gen_cores[$4]/gen_mempool_cc/riscv_core/BootAddr add wave -noupdate -group core[$1][$2][$3][$4] -group Params /mempool_tb/dut/i_mempool_cluster/gen_groups[$1]/gen_rtl_group/i_group/gen_sub_groups[$2]/gen_rtl_sg/i_sub_group/gen_tiles[$3]/i_tile/gen_cores[$4]/gen_mempool_cc/riscv_core/MTVEC add wave -noupdate -group core[$1][$2][$3][$4] -group Params /mempool_tb/dut/i_mempool_cluster/gen_groups[$1]/gen_rtl_group/i_group/gen_sub_groups[$2]/gen_rtl_sg/i_sub_group/gen_tiles[$3]/i_tile/gen_cores[$4]/gen_mempool_cc/riscv_core/RVE diff --git a/hardware/scripts/questa/wave_tile.tcl b/hardware/scripts/questa/wave_tile.tcl index 4dd08b9ab..bb094d368 100644 --- a/hardware/scripts/questa/wave_tile.tcl +++ b/hardware/scripts/questa/wave_tile.tcl @@ -3,7 +3,7 @@ # SPDX-License-Identifier: SHL-0.51 # Create group for group $1 tile $2 -if {$config == {terapool}} { +if {$config == {terapool_spatz4_fpu} | $config == {terapool}} { add wave -noupdate -group group_[$1] -group sub_group_[$2] -group Tile_[$3] -group Params /mempool_pkg::NumBanksPerTile add wave -noupdate -group group_[$1] -group sub_group_[$2] -group Tile_[$3] -group Params /mempool_pkg::NumTiles add wave -noupdate -group group_[$1] -group sub_group_[$2] -group Tile_[$3] -group Params /mempool_pkg::NumBanks diff --git a/hardware/src/mempool_group.sv b/hardware/src/mempool_group.sv index c6ecf84d7..6eb245765 100644 --- a/hardware/src/mempool_group.sv +++ b/hardware/src/mempool_group.sv @@ -111,18 +111,18 @@ module mempool_group end: gen_tcdm_struct `ifdef TERAPOOL - /********************* - * TeraPool Section * - *********************/ - // ----------------------------------------------------- // - // SubGroups Instantiation. - // PostLayout Simulation: - // - Only SubGroup_0 Replaced by Netlist for simulation. - // TCDM interconnections: - // - Local SubGroups; - // - Remote Groups. - // Most of AXI/DMA logics put into SubGroup level. - // ----------------------------------------------------- // + /********************* + * TeraPool Section * + *********************/ + // ----------------------------------------------------- // + // SubGroups Instantiation. + // PostLayout Simulation: + // - Only SubGroup_0 Replaced by Netlist for simulation. + // TCDM interconnections: + // - Local SubGroups; + // - Remote Groups. + // Most of AXI/DMA logics put into SubGroup level. + // ----------------------------------------------------- // /**************** * Sub_Groups * @@ -579,16 +579,16 @@ module mempool_group ); `else - /****************************** - * MemPool / MinPool Section * - ******************************/ - // ----------------------------------------------------- // - // Tile Instantiation. - // TCDM interconnections: - // - Local Tiles; - // - Remote Groups; - // Most of AXI/DMA logics put on Group level. - // ----------------------------------------------------- // + /****************************** + * MemPool / MinPool Section * + ******************************/ + // ----------------------------------------------------- // + // Tile Instantiation. + // TCDM interconnections: + // - Local Tiles; + // - Remote Groups; + // Most of AXI/DMA logics put on Group level. + // ----------------------------------------------------- // /*********** * Tiles * diff --git a/software/apps/mempool-sp-imatmul/data/data_gemm.h b/software/apps/spatz_apps/imatmul/data/data_gemm.h similarity index 100% rename from software/apps/mempool-sp-imatmul/data/data_gemm.h rename to software/apps/spatz_apps/imatmul/data/data_gemm.h diff --git a/software/apps/mempool-sp-imatmul/data/layer.h b/software/apps/spatz_apps/imatmul/data/layer.h similarity index 100% rename from software/apps/mempool-sp-imatmul/data/layer.h rename to software/apps/spatz_apps/imatmul/data/layer.h diff --git a/software/apps/mempool-sp-imatmul/kernel/sp-imatmul.c b/software/apps/spatz_apps/imatmul/kernel/sp-imatmul.c similarity index 100% rename from software/apps/mempool-sp-imatmul/kernel/sp-imatmul.c rename to software/apps/spatz_apps/imatmul/kernel/sp-imatmul.c diff --git a/software/apps/mempool-sp-imatmul/kernel/sp-imatmul.h b/software/apps/spatz_apps/imatmul/kernel/sp-imatmul.h similarity index 100% rename from software/apps/mempool-sp-imatmul/kernel/sp-imatmul.h rename to software/apps/spatz_apps/imatmul/kernel/sp-imatmul.h diff --git a/software/apps/mempool-sp-imatmul/main.c b/software/apps/spatz_apps/imatmul/main.c similarity index 100% rename from software/apps/mempool-sp-imatmul/main.c rename to software/apps/spatz_apps/imatmul/main.c diff --git a/software/apps/mempool-sp-imatmul/script/gen_data.py b/software/apps/spatz_apps/imatmul/script/gen_data.py similarity index 100% rename from software/apps/mempool-sp-imatmul/script/gen_data.py rename to software/apps/spatz_apps/imatmul/script/gen_data.py diff --git a/software/apps/mempool-sp-imatmul/script/matmul.json b/software/apps/spatz_apps/imatmul/script/matmul.json similarity index 100% rename from software/apps/mempool-sp-imatmul/script/matmul.json rename to software/apps/spatz_apps/imatmul/script/matmul.json diff --git a/software/apps/vector_add/main.c b/software/apps/spatz_apps/ivadd/main.c similarity index 100% rename from software/apps/vector_add/main.c rename to software/apps/spatz_apps/ivadd/main.c diff --git a/software/apps/sp-axpy/data/data_axpy.h b/software/apps/spatz_apps/sp-axpy/data/data_axpy.h similarity index 100% rename from software/apps/sp-axpy/data/data_axpy.h rename to software/apps/spatz_apps/sp-axpy/data/data_axpy.h diff --git a/software/apps/sp-axpy/data/layer.h b/software/apps/spatz_apps/sp-axpy/data/layer.h similarity index 100% rename from software/apps/sp-axpy/data/layer.h rename to software/apps/spatz_apps/sp-axpy/data/layer.h diff --git a/software/apps/sp-axpy/main.c b/software/apps/spatz_apps/sp-axpy/main.c similarity index 98% rename from software/apps/sp-axpy/main.c rename to software/apps/spatz_apps/sp-axpy/main.c index af1845e3e..f94d8ccb4 100644 --- a/software/apps/sp-axpy/main.c +++ b/software/apps/spatz_apps/sp-axpy/main.c @@ -29,8 +29,8 @@ #endif // dump(result, 1); -const unsigned int csize = 256; -const unsigned int core_count = 16; +const unsigned int csize = 64; +const unsigned int core_count = 64; const unsigned int esize = csize*core_count; float x[esize]; diff --git a/software/apps/sp-axpy/script/axpy.json b/software/apps/spatz_apps/sp-axpy/script/axpy.json similarity index 100% rename from software/apps/sp-axpy/script/axpy.json rename to software/apps/spatz_apps/sp-axpy/script/axpy.json diff --git a/software/apps/sp-axpy/script/gen_data.py b/software/apps/spatz_apps/sp-axpy/script/gen_data.py similarity index 100% rename from software/apps/sp-axpy/script/gen_data.py rename to software/apps/spatz_apps/sp-axpy/script/gen_data.py diff --git a/software/apps/float-simple/main.c b/software/apps/spatz_apps/sp-basic/main.c similarity index 100% rename from software/apps/float-simple/main.c rename to software/apps/spatz_apps/sp-basic/main.c diff --git a/software/apps/spatz_apps/sp-fmatmul/data/.gitignore b/software/apps/spatz_apps/sp-fmatmul/data/.gitignore new file mode 100644 index 000000000..dacc7e494 --- /dev/null +++ b/software/apps/spatz_apps/sp-fmatmul/data/.gitignore @@ -0,0 +1 @@ +data_gemm.h diff --git a/software/apps/spatz_apps/sp-fmatmul/data/data_64_128_64.h b/software/apps/spatz_apps/sp-fmatmul/data/data_64_128_64.h new file mode 100644 index 000000000..965cd0b55 --- /dev/null +++ b/software/apps/spatz_apps/sp-fmatmul/data/data_64_128_64.h @@ -0,0 +1,35 @@ +// Copyright 2023 ETH Zurich and University of Bologna. +// Licensed under the Apache License, Version 2.0, see LICENSE for details. +// SPDX-License-Identifier: Apache-2.0 + +// This file was generated automatically. + +#include "layer.h" + +const gemm_layer gemm_l = { + .M = 64, + .N = 128, + .K = 64, + .TA = 0, + .TB = 0, + .ALPHA = 0, + .dtype = FP32, + .expand = 0 +}; + + +float a[64*64] __attribute__((section(".l1"))); +float b[64*128] __attribute__((section(".l1"))); +float c[64*128] __attribute__((section(".l1"))); +static float gemm_A_dram [64*64] __attribute__((section(".data"))) = {1.9269152879714966, 1.4872840642929077, 0.9007171988487244, -2.1055209636688232, 0.6784184575080872, -1.2345448732376099, -0.04306747764348984, -1.6046669483184814, -0.7521352767944336, 1.6487230062484741, -0.3924786448478699, -1.4036071300506592, -0.7278813123703003, -0.5594301819801331, -0.7688388824462891, 0.7624453902244568, 1.6423169374465942, -0.1595974713563919, -0.4973975419998169, 0.439589262008667, -0.7581311464309692, 1.078317642211914, 0.8008005619049072, 1.680620551109314, 1.27912437915802, 1.2964228391647339, 0.610466480255127, 1.334737777709961, -0.2316243201494217, 0.041759490966796875, -0.2515752911567688, 0.859858512878418, -1.3846737146377563, -0.8712361454963684, -0.223365917801857, 1.7173614501953125, 0.3188803195953369, -0.42451897263526917, 0.3057209253311157, -0.7745925188064575, -1.5575724840164185, 0.9956361055374146, -0.8797858357429504, -0.6011420488357544, -1.2741512060165405, 2.1227850914001465, -1.234653115272522, -0.4879138767719269, -0.9138230085372925, -0.6581372618675232, 0.07802387326955795, 0.5258087515830994, -0.48799172043800354, 1.1913690567016602, -0.8140076398849487, -0.7359927892684937, -1.4032478332519531, 0.03600366786122322, -0.06347727030515671, 0.6756148934364319, -0.0978068932890892, 1.8445940017700195, -1.184537410736084, 1.3835493326187134, 1.4451338052749634, 0.8564125299453735, 2.218075752258301, 0.5231655240058899, 0.34664666652679443, -0.19733144342899323, -1.0545889139175415, 1.2779955863952637, -0.1721901297569275, 0.5237884521484375, 0.056621819734573364, 0.4262961447238922, 0.575005054473877, -0.6417241096496582, -2.2063984870910645, -0.7508030533790588, 0.01086814422160387, -0.33874234557151794, -1.3406795263290405, -0.5853705406188965, 0.5361881256103516, 0.5246226191520691, 1.1412016153335571, 0.05164359509944916, 0.7439519762992859, -0.4815843999385834, -1.0494661331176758, 0.603898823261261, -1.7222950458526611, -0.827768862247467, 1.334702968597412, 0.48353928327560425, -2.5095443725585938, 0.4880010485649109, 0.7845868468284607, 0.02864718623459339, 0.640755295753479, 0.5832474231719971, 1.0669267177581787, -0.4501533806324005, -0.18526747822761536, 0.7527588605880737, 0.4047577977180481, 0.17846599221229553, 0.2649095058441162, 1.2731683254241943, -0.0013108636485412717, -0.30360376834869385, 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-1.2821251153945923, 2.523390769958496, 1.3808592557907104, 0.4819633364677429}; + + +static const float gemm_checksum[64] = {106.50713348388672, -43.41827392578125, 88.62898254394531, 206.1454620361328, -86.65469360351562, 47.85114288330078, -83.10395812988281, -0.6398353576660156, -18.263839721679688, -150.13345336914062, -22.446304321289062, 9.95019817352295, -100.16041564941406, -103.33052825927734, -33.98554611206055, 70.34858703613281, -39.1917724609375, -14.04034423828125, 79.12849426269531, -213.2205810546875, 8.674177169799805, 18.772485733032227, 48.36838912963867, -152.58534240722656, 58.96424865722656, -1.9184703826904297, -124.40823364257812, -66.2288589477539, -58.79644012451172, -35.820404052734375, -55.25178527832031, -14.93830680847168, -105.72493743896484, -0.10310649871826172, 71.98310089111328, 57.72700881958008, -80.43113708496094, 130.24502563476562, -35.593284606933594, 86.2574462890625, -61.68738555908203, 3.345569610595703, -144.9003143310547, 2.9203100204467773, -14.018852233886719, 33.92664337158203, 51.765052795410156, 48.26521682739258, -12.355224609375, -217.03515625, -21.477426528930664, 3.749959945678711, 46.99170684814453, 63.960899353027344, -16.157604217529297, 53.231231689453125, -44.57835388183594, 106.66551208496094, 119.85152435302734, -85.81074523925781, 77.12012481689453, -27.079853057861328, 64.79229736328125, 101.60380554199219}; + + diff --git a/software/apps/spatz_apps/sp-fmatmul/data/data_64_64_64.h b/software/apps/spatz_apps/sp-fmatmul/data/data_64_64_64.h new file mode 100644 index 000000000..05622830e --- /dev/null +++ b/software/apps/spatz_apps/sp-fmatmul/data/data_64_64_64.h @@ -0,0 +1,4152 @@ +// Copyright 2023 ETH Zurich and University of Bologna. +// Licensed under the Apache License, Version 2.0, see LICENSE for details. +// SPDX-License-Identifier: Apache-2.0 + +// This file was generated automatically. + +#include "layer.h" + +const gemm_layer gemm_l = { + .M = 64, + .N = 64, + .K = 64, + .TA = 0, + .TB = 0, + .ALPHA = 0, + .dtype = FP32, + .expand = 0 +}; + + +float a[4096] __attribute__((section(".l1"))); +float b[4096] __attribute__((section(".l1"))); +float c[4096] __attribute__((section(".l1"))); +float r[64] __attribute__((section(".l1"))); + +static float gemm_A_dram[64 * 64] __attribute__((section(".data"))) = { + 1.9269152879714966, 1.4872840642929077, 0.9007171988487244, + -2.1055209636688232, 0.6784184575080872, -1.2345448732376099, + -0.04306747764348984, -1.6046669483184814, -0.7521352767944336, + 1.6487230062484741, -0.3924786448478699, -1.4036071300506592, + -0.7278813123703003, -0.5594301819801331, -0.7688388824462891, + 0.7624453902244568, 1.6423169374465942, -0.1595974713563919, + -0.4973975419998169, 0.439589262008667, -0.7581311464309692, + 1.078317642211914, 0.8008005619049072, 1.680620551109314, + 1.27912437915802, 1.2964228391647339, 0.610466480255127, + 1.334737777709961, -0.2316243201494217, 0.041759490966796875, + -0.2515752911567688, 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0.5779557824134827, 0.015540639869868755, 0.5925832390785217, + 0.7594045996665955, 0.3172358274459839, -0.2898426055908203, + -1.691469669342041, 1.6546062231063843, 1.9897799491882324, + 0.9833572506904602, 0.8757314682006836, 0.03340153768658638, + -1.1073716878890991, -1.61149263381958, 1.5818325281143188, + -1.001272439956665, 2.4090232849121094, 0.4967930018901825, + -0.5889718532562256, -1.2327499389648438, -0.933485746383667, + 0.6923277974128723, 2.978621006011963, -1.201478123664856, + -0.5414489507675171, 0.9162774682044983, -0.9938631057739258, + -0.9462518692016602, 2.026346445083618, -0.08148741722106934, + 2.1180760860443115, 2.4568521976470947, 1.308334469795227, + -0.4092293083667755, 1.5199389457702637, 0.2401336431503296, + -0.07672857493162155, 0.19953538477420807, -0.582857072353363, + 1.7541544437408447, 0.3644847571849823, -0.8537748456001282, + 1.333866000175476, 1.1097289323806763, -1.2961713075637817, + -0.2671332359313965, -0.11528171598911285, 0.5364192724227905, + 0.8485555052757263, -0.6455994248390198, -1.61687171459198, + -0.36001577973365784, -0.2978653609752655, -0.07868463546037674, + 0.00238874857313931, 0.8151956796646118, -0.8603678345680237, + 0.590671718120575, -0.1570080667734146, 0.25781306624412537, + 0.5526830554008484, 0.005012926179915667, 0.5645086169242859, + -1.8153542280197144, 0.10933580249547958, 0.914612889289856, + 1.1200610399246216, 1.0505285263061523, -0.002121273661032319, + -0.5658379793167114, 0.6379390954971313, 0.45158031582832336, + -0.7626321315765381, 1.3174611330032349, -0.6234307885169983, + -1.7559058666229248, 1.6489591598510742, -0.5015462636947632, + -0.7464198470115662, 1.30655837059021, 0.4342547059059143, + -0.3332986831665039, -0.16042929887771606, -1.8263144493103027, + 0.7954517006874084, 0.0019316024845466018, -1.085212230682373, + 0.05519922077655792, 0.2578616440296173, 0.2691059708595276, + -0.24719884991645813, -1.1037062406539917, 1.2708667516708374, + -0.6203423738479614, -0.2558014392852783, 0.7869867086410522, + 0.9924377202987671}; + +static float gemm_checksum[64] __attribute__((section(".data"))) = { + 41.76314926147461, -65.81356811523438, 65.55894470214844, + 42.30286407470703, 20.097267150878906, -29.62862777709961, + -21.734668731689453, 87.2625503540039, 37.29133605957031, + 39.95728302001953, -25.613080978393555, -44.487586975097656, + 34.806610107421875, 24.05472183227539, 43.968772888183594, + -10.488407135009766, 86.8247299194336, 50.548866271972656, + -41.23493194580078, 42.71798324584961, -4.739665985107422, + -45.585811614990234, -4.771617412567139, 53.848350524902344, + 37.348716735839844, -7.853451728820801, 8.257918357849121, + -3.611386299133301, -55.8760986328125, 116.66316223144531, + -27.311840057373047, -67.69171142578125, -72.58342742919922, + -98.41576385498047, -81.00123596191406, -76.84272766113281, + -49.544647216796875, -105.94683074951172, 37.21599578857422, + 55.56163024902344, 53.508304595947266, 34.51641082763672, + -23.531394958496094, -11.475716590881348, -32.61056137084961, + 3.52435302734375, 148.31321716308594, 69.42517852783203, + -78.15328216552734, -60.516632080078125, -45.94889831542969, + -96.12492370605469, -93.82456970214844, 16.805723190307617, + -46.46389389038086, -43.06182098388672, 13.753861427307129, + 7.3392791748046875, -64.09297180175781, -64.01337432861328, + -38.037994384765625, -47.68368911743164, 36.84903335571289, + -7.636900424957275}; diff --git a/software/apps/spatz_apps/sp-fmatmul/data/layer.h b/software/apps/spatz_apps/sp-fmatmul/data/layer.h new file mode 100644 index 000000000..ba49ef72f --- /dev/null +++ b/software/apps/spatz_apps/sp-fmatmul/data/layer.h @@ -0,0 +1,134 @@ +// Copyright 2020 ETH Zurich and University of Bologna. +// Licensed under the Apache License, Version 2.0, see LICENSE for details. +// SPDX-License-Identifier: Apache-2.0 + +#pragma once + +#include + +typedef enum { FP64 = 8, FP32 = 4, FP16 = 2, FP8 = 1 } precision_t; + +/** + * @struct gemm_layer_struct + * @brief This structure contains all parameters necessary for GEMM. + * @var gemm_layer_struct::M + * Dimension of matrix product MxK * KxN + * @var gemm_layer_struct::M_p + * M divided by number of compute cores + * @var gemm_layer_struct::N + * Dimension of matrix product MxK * KxN + * @var gemm_layer_struct::K + * Dimension of matrix product MxK * KxN + * @var gemm_layer_struct::TA + * Transpose matrix A + * @var gemm_layer_struct::TB + * Transpose matrix B + * @var gemm_layer_struct::TILE_M + * Tile factor across M dimension + * @var gemm_layer_struct::TILE_N + * Tile factor across N dimension + * @var gemm_layer_struct::TILE_K + * Tile factor across K dimension + * @var gemm_layer_struct::A + * Pointer to matrix A + * @var gemm_layer_struct::B + * Pointer to matrix B + * @var gemm_layer_struct::C + * Pointer to matrix C + * @var gemm_layer_struct::ALPHA + * constant factor: A * B + ALPHA * C + * @var gemm_layer_struct::dtype + * Precision of GEMM + * @var gemm_layer_struct::expand + * Use expanding DOTP instructions + */ +typedef struct gemm_layer_struct { + uint32_t M; + uint32_t M_p; + uint32_t N; + uint32_t K; + + uint32_t TA; + uint32_t TB; + + uint32_t TILE_M; + uint32_t TILE_N; + uint32_t TILE_K; + + double *A; + double *B; + double *C; + + uint32_t ALPHA; + + precision_t dtype; + uint32_t expand; +} gemm_layer; + +/** + * @struct conv_layer_struct + * @brief This structure contains all parameters necessary for Convolutional + * layers + * @var conv_layer_struct::CO + * Number of output channels + * @var conv_layer_struct::CI + * Number of input channels + * @var conv_layer_struct::IH + * Height of input feature map + * @var conv_layer_struct::IW + * Width of input feature map + * @var conv_layer_struct::OH + * Height of output feature map + * @var conv_layer_struct::OW + * Width of output feature map + * @var conv_layer_struct::FH + * Height of filter + * @var conv_layer_struct::FW + * Width of filter + * @var conv_layer_struct::pad + * Padding on all sides + * @var conv_layer_struct::ifmap + * Pointer to input feature map + * @var conv_layer_struct::weights + * Pointer to weights + * @var conv_layer_struct::ofmap + * Pointer to output feature map + * @var conv_layer_struct::TILE_CI + * Tiling factor of input channel + * @var conv_layer_struct::cluster2cluster + * Flag for enabling cluster 2 cluster communication + * @var conv_layer_struct::im2col + * Flag for enabling im2col + GEMM + * @var conv_layer_struct::gamma + * Pointer to gamma for BatchNorm + * @var conv_layer_struct::beta + * Pointer to beta for BatchNorm + * @var gemm_layer_struct::dtype + * Precision of Convolution layer + */ +typedef struct conv_layer_struct { + // CONV2D + uint32_t CO; + uint32_t CI; + uint32_t IH; + uint32_t IW; + uint32_t OH; + uint32_t OW; + uint32_t FH; + uint32_t FW; + uint32_t pad; + + double *ifmap; + double *weights; + double *ofmap; + + uint32_t TILE_CI; + uint32_t cluster2cluster; + uint32_t im2col; + + // BATCHNORM + double *gamma; + double *beta; + + precision_t dtype; +} conv_layer; diff --git a/software/apps/spatz_apps/sp-fmatmul/kernel/sp-fmatmul.c b/software/apps/spatz_apps/sp-fmatmul/kernel/sp-fmatmul.c new file mode 100644 index 000000000..1c9548eef --- /dev/null +++ b/software/apps/spatz_apps/sp-fmatmul/kernel/sp-fmatmul.c @@ -0,0 +1,394 @@ +// Copyright 2023 ETH Zurich and University of Bologna. +// +// SPDX-License-Identifier: Apache-2.0 +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +// Author: Matheus Cavalcante, ETH Zurich + +#include "sp-fmatmul.h" +#include + +#define MIN(a, b) ((a) < (b) ? (a) : (b)) + +void matmul(float *c, const float *a, const float *b, const unsigned int M, + const unsigned int N, const unsigned int P) { + if (M <= 4) { + matmul_2xVL(c, a, b, 0, M, N, P, 0, P); + } else if (M <= 8) { + matmul_4xVL(c, a, b, 0, M, N, P, 0, P); + } else { + matmul_8xVL(c, a, b, 0, M, N, P, 0, P); + } +} + +// --------------- +// 2xVL +// --------------- + +void matmul_2xVL(float *c, const float *a, const float *b, + const unsigned int m_start, const unsigned int m_end, + const unsigned int N, const unsigned int P, + const unsigned int p_start, const unsigned int p_end) { + + unsigned int p = p_start; + while (p < p_end) { + // Calculate the vl + size_t gvl; + asm volatile("vsetvli %[gvl], %[vl], e32, m8, ta, ma" + : [gvl] "=r"(gvl) + : [vl] "r"(p_end - p)); + + const float *b_ = b + p; + float *c_ = c + p; + + for (unsigned int m = m_start; m < m_end; m += 2) { + const float *a_ = a + m * N; + const float *a__ = a_; + + asm volatile("vle32.v v16, (%0);" ::"r"(b_)); + const float *b__ = b_ + P; + + float *c__ = c_ + m * P; + + float t0, t1; + + t0 = *a__; + a__ += N; + t1 = *a__; + + unsigned int n = 0; + + while (n < N) { + a__ = a_ + ++n; + + asm volatile("vle32.v v24, (%0);" ::"r"(b__)); + b__ += P; + + if (n == 1) { + asm volatile("vfmul.vf v0, v16, %0" ::"f"(t0)); + t0 = *a__; + a__ += N; + asm volatile("vfmul.vf v8, v16, %0" ::"f"(t1)); + t1 = *a__; + } else { + asm volatile("vfmacc.vf v0, %0, v16" ::"f"(t0)); + t0 = *a__; + a__ += N; + asm volatile("vfmacc.vf v8, %0, v16" ::"f"(t1)); + t1 = *a__; + } + + a__ = a_ + ++n; + + if (n == N) + break; + + asm volatile("vle32.v v16, (%0);" ::"r"(b__)); + b__ += P; + + asm volatile("vfmacc.vf v0, %0, v24" ::"f"(t0)); + t0 = *a__; + a__ += N; + asm volatile("vfmacc.vf v8, %0, v24" ::"f"(t1)); + t1 = *a__; + } + + asm volatile("vfmacc.vf v0, %0, v24" ::"f"(t0)); + asm volatile("vse32.v v0, (%0);" ::"r"(c__)); + c__ += P; + asm volatile("vfmacc.vf v8, %0, v24" ::"f"(t1)); + asm volatile("vse32.v v8, (%0);" ::"r"(c__)); + } + + p += gvl; + } +} + +// --------------- +// 4xVL +// --------------- + +void matmul_4xVL(float *c, const float *a, const float *b, + const unsigned int m_start, const unsigned int m_end, + const unsigned int N, const unsigned int P, + const unsigned int p_start, const unsigned int p_end) { + + unsigned int p = p_start; + while (p < p_end) { + // Calculate the vl + size_t gvl; + asm volatile("vsetvli %[gvl], %[vl], e32, m4, ta, ma" + : [gvl] "=r"(gvl) + : [vl] "r"(p_end - p)); + + const float *b_ = b + p; + float *c_ = c + p; + + for (unsigned int m = m_start; m < m_end; m += 4) { + const float *a_ = a + m * N; + const float *a__ = a_; + + asm volatile("vle32.v v16, (%0);" ::"r"(b_)); + const float *b__ = b_ + P; + + float *c__ = c_ + m * P; + + float t0, t1, t2, t3; + + t0 = *a__; + a__ += N; + t1 = *a__; + a__ += N; + t2 = *a__; + a__ += N; + t3 = *a__; + + unsigned int n = 0; + + while (n < N) { + asm volatile("vle32.v v20, (%0);" ::"r"(b__)); + b__ += P; + + a__ = a_ + ++n; + + if (n == 1) { + asm volatile("vfmul.vf v0, v16, %0" ::"f"(t0)); + t0 = *a__; + a__ += N; + asm volatile("vfmul.vf v4, v16, %0" ::"f"(t1)); + t1 = *a__; + a__ += N; + asm volatile("vfmul.vf v8, v16, %0" ::"f"(t2)); + t2 = *a__; + a__ += N; + asm volatile("vfmul.vf v12, v16, %0" ::"f"(t3)); + t3 = *a__; + } else { + asm volatile("vfmacc.vf v0, %0, v16" ::"f"(t0)); + t0 = *a__; + a__ += N; + asm volatile("vfmacc.vf v4, %0, v16" ::"f"(t1)); + t1 = *a__; + a__ += N; + asm volatile("vfmacc.vf v8, %0, v16" ::"f"(t2)); + t2 = *a__; + a__ += N; + asm volatile("vfmacc.vf v12, %0, v16" ::"f"(t3)); + t3 = *a__; + } + + a__ = a_ + ++n; + + if (n == N) + break; + + asm volatile("vle32.v v16, (%0);" ::"r"(b__)); + b__ += P; + + asm volatile("vfmacc.vf v0, %0, v20" ::"f"(t0)); + t0 = *a__; + a__ += N; + asm volatile("vfmacc.vf v4, %0, v20" ::"f"(t1)); + t1 = *a__; + a__ += N; + asm volatile("vfmacc.vf v8, %0, v20" ::"f"(t2)); + t2 = *a__; + a__ += N; + asm volatile("vfmacc.vf v12, %0, v20" ::"f"(t3)); + t3 = *a__; + } + + asm volatile("vfmacc.vf v0, %0, v20" ::"f"(t0)); + asm volatile("vse32.v v0, (%0);" ::"r"(c__)); + c__ += P; + asm volatile("vfmacc.vf v4, %0, v20" ::"f"(t1)); + asm volatile("vse32.v v4, (%0);" ::"r"(c__)); + c__ += P; + asm volatile("vfmacc.vf v8, %0, v20" ::"f"(t2)); + asm volatile("vse32.v v8, (%0);" ::"r"(c__)); + c__ += P; + asm volatile("vfmacc.vf v12, %0, v20" ::"f"(t3)); + asm volatile("vse32.v v12, (%0);" ::"r"(c__)); + } + + p += gvl; + } +} + +// --------------- +// 8xVL +// --------------- + +void matmul_8xVL(float *c, const float *a, const float *b, + const unsigned int m_start, const unsigned int m_end, + const unsigned int N, const unsigned int P, + const unsigned int p_start, const unsigned int p_end) { + + unsigned int p = p_start; + while (p < p_end) { + // Calculate the vl + size_t gvl; + asm volatile("vsetvli %[gvl], %[vl], e32, m2, ta, ma" + : [gvl] "=r"(gvl) + : [vl] "r"(p_end - p)); + + const float *b_ = b + p; + float *c_ = c + p; + + for (unsigned int m = m_start; m < m_end; m += 8) { + const float *a_ = a + m * N; + const float *a__ = a_; + + asm volatile("vle32.v v18, (%0);" ::"r"(b_)); + const float *b__ = b_ + P; + + float *c__ = c_ + m * P; + + float t0, t1, t2, t3, t4, t5, t6, t7; + + t0 = *a__; + a__ += N; + t1 = *a__; + a__ += N; + t2 = *a__; + a__ += N; + t3 = *a__; + a__ += N; + t4 = *a__; + a__ += N; + t5 = *a__; + a__ += N; + t6 = *a__; + a__ += N; + t7 = *a__; + + unsigned int n = 0; + + while (n < N) { + a__ = a_ + ++n; + + asm volatile("vle32.v v20, (%0);" ::"r"(b__)); + b__ += P; + + if (n == 1) { + asm volatile("vfmul.vf v0, v18, %0" ::"f"(t0)); + t0 = *a__; + a__ += N; + asm volatile("vfmul.vf v2, v18, %0" ::"f"(t1)); + t1 = *a__; + a__ += N; + asm volatile("vfmul.vf v4, v18, %0" ::"f"(t2)); + t2 = *a__; + a__ += N; + asm volatile("vfmul.vf v6, v18, %0" ::"f"(t3)); + t3 = *a__; + a__ += N; + asm volatile("vfmul.vf v8, v18, %0" ::"f"(t4)); + t4 = *a__; + a__ += N; + asm volatile("vfmul.vf v10, v18, %0" ::"f"(t5)); + t5 = *a__; + a__ += N; + asm volatile("vfmul.vf v12, v18, %0" ::"f"(t6)); + t6 = *a__; + a__ += N; + asm volatile("vfmul.vf v14, v18, %0" ::"f"(t7)); + t7 = *a__; + } else { + asm volatile("vfmacc.vf v0, %0, v18" ::"f"(t0)); + t0 = *a__; + a__ += N; + asm volatile("vfmacc.vf v2, %0, v18" ::"f"(t1)); + t1 = *a__; + a__ += N; + asm volatile("vfmacc.vf v4, %0, v18" ::"f"(t2)); + t2 = *a__; + a__ += N; + asm volatile("vfmacc.vf v6, %0, v18" ::"f"(t3)); + t3 = *a__; + a__ += N; + asm volatile("vfmacc.vf v8, %0, v18" ::"f"(t4)); + t4 = *a__; + a__ += N; + asm volatile("vfmacc.vf v10, %0, v18" ::"f"(t5)); + t5 = *a__; + a__ += N; + asm volatile("vfmacc.vf v12, %0, v18" ::"f"(t6)); + t6 = *a__; + a__ += N; + asm volatile("vfmacc.vf v14, %0, v18" ::"f"(t7)); + t7 = *a__; + } + + a__ = a_ + ++n; + + if (n == N) + break; + + asm volatile("vle32.v v18, (%0);" ::"r"(b__)); + b__ += P; + + asm volatile("vfmacc.vf v0, %0, v20" ::"f"(t0)); + t0 = *a__; + a__ += N; + asm volatile("vfmacc.vf v2, %0, v20" ::"f"(t1)); + t1 = *a__; + a__ += N; + asm volatile("vfmacc.vf v4, %0, v20" ::"f"(t2)); + t2 = *a__; + a__ += N; + asm volatile("vfmacc.vf v6, %0, v20" ::"f"(t3)); + t3 = *a__; + a__ += N; + asm volatile("vfmacc.vf v8, %0, v20" ::"f"(t4)); + t4 = *a__; + a__ += N; + asm volatile("vfmacc.vf v10, %0, v20" ::"f"(t5)); + t5 = *a__; + a__ += N; + asm volatile("vfmacc.vf v12, %0, v20" ::"f"(t6)); + t6 = *a__; + a__ += N; + asm volatile("vfmacc.vf v14, %0, v20" ::"f"(t7)); + t7 = *a__; + } + + asm volatile("vfmacc.vf v0, %0, v20" ::"f"(t0)); + asm volatile("vse32.v v0, (%0);" ::"r"(c__)); + c__ += P; + asm volatile("vfmacc.vf v2, %0, v20" ::"f"(t1)); + asm volatile("vse32.v v2, (%0);" ::"r"(c__)); + c__ += P; + asm volatile("vfmacc.vf v4, %0, v20" ::"f"(t2)); + asm volatile("vse32.v v4, (%0);" ::"r"(c__)); + c__ += P; + asm volatile("vfmacc.vf v6, %0, v20" ::"f"(t3)); + asm volatile("vse32.v v6, (%0);" ::"r"(c__)); + c__ += P; + asm volatile("vfmacc.vf v8, %0, v20" ::"f"(t4)); + asm volatile("vse32.v v8, (%0);" ::"r"(c__)); + c__ += P; + asm volatile("vfmacc.vf v10, %0, v20" ::"f"(t5)); + asm volatile("vse32.v v10, (%0);" ::"r"(c__)); + c__ += P; + asm volatile("vfmacc.vf v12, %0, v20" ::"f"(t6)); + asm volatile("vse32.v v12, (%0);" ::"r"(c__)); + c__ += P; + asm volatile("vfmacc.vf v14, %0, v20" ::"f"(t7)); + asm volatile("vse32.v v14, (%0);" ::"r"(c__)); + } + + p += gvl; + } +} diff --git a/software/apps/spatz_apps/sp-fmatmul/kernel/sp-fmatmul.h b/software/apps/spatz_apps/sp-fmatmul/kernel/sp-fmatmul.h new file mode 100644 index 000000000..f19081ed7 --- /dev/null +++ b/software/apps/spatz_apps/sp-fmatmul/kernel/sp-fmatmul.h @@ -0,0 +1,45 @@ +// Copyright 2023 ETH Zurich and University of Bologna. +// +// SPDX-License-Identifier: Apache-2.0 +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +// Author: Matheus Cavalcante, ETH Zurich + +#ifndef SPFMATMUL_H +#define SPFMATMUL_H + +void matmul(float *c, const float *a, const float *b, const unsigned int M, + const unsigned int N, const unsigned int P); + +inline void matmul_single_unrolled(float *c, const float *a, const float *b, + const unsigned int N, const unsigned int P, + unsigned int vl) + __attribute__((always_inline)); +inline void matmul_2xVL(float *c, const float *a, const float *b, + const unsigned int m_start, const unsigned int m_end, + const unsigned int N, const unsigned int P, + const unsigned int p_start, const unsigned int p_end) + __attribute__((always_inline)); +inline void matmul_4xVL(float *c, const float *a, const float *b, + const unsigned int m_start, const unsigned int m_end, + const unsigned int N, const unsigned int P, + const unsigned int p_start, const unsigned int p_end) + __attribute__((always_inline)); +inline void matmul_8xVL(float *c, const float *a, const float *b, + const unsigned int m_start, const unsigned int m_end, + const unsigned int N, const unsigned int P, + const unsigned int p_start, const unsigned int p_end) + __attribute__((always_inline)); + +#endif diff --git a/software/apps/spatz_apps/sp-fmatmul/main.c b/software/apps/spatz_apps/sp-fmatmul/main.c new file mode 100644 index 000000000..c71078615 --- /dev/null +++ b/software/apps/spatz_apps/sp-fmatmul/main.c @@ -0,0 +1,178 @@ +// Copyright 2023 ETH Zurich and University of Bologna. +// +// SPDX-License-Identifier: Apache-2.0 +// +// Licensed under the Apache License, Version 2.0 (the "License"); +// you may not use this file except in compliance with the License. +// You may obtain a copy of the License at +// +// http://www.apache.org/licenses/LICENSE-2.0 +// +// Unless required by applicable law or agreed to in writing, software +// distributed under the License is distributed on an "AS IS" BASIS, +// WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. +// See the License for the specific language governing permissions and +// limitations under the License. + +// Author: Matheus Cavalcante, ETH Zurich + + +#include +#include +#include + +#include "printf.h" +#ifdef MEMPOOL +#include "alloc.h" +#include "runtime.h" +#include "synchronization.h" +#endif +#include "data/data_64_64_64.h" +#include "kernel/sp-fmatmul.c" + + + +// Initialize the matrices +void init_matrix(float *matrix, const float *src, + const unsigned int size) { + for (unsigned int j = 0; j < size; ++j) + matrix[j] = src[j]; +} + +const unsigned int core_count = 16; + +// void init_matrix(float *matrix, const float *src, +// const unsigned int rows_start, const unsigned int rows_end, +// const unsigned int num_columns) { +// for (unsigned int i = rows_start; i < rows_end; ++i) { +// for (unsigned int j = 0; j < num_columns; ++j) { +// matrix[i * num_columns + j] = src[i * num_columns + j]; +// } +// } +// } + +// Verify the matrices +int verify_matrix(float *matrix, const float *checksum, + const unsigned int num_rows, const unsigned int num_columns) { + int error = 0; + for (unsigned int i = 0; i < num_rows; ++i) { + float sum = 0; + for (unsigned int j = 0; j < num_columns; ++j) { + sum += (float)matrix[i * num_columns + j]; + } + + float diff = sum - (float)checksum[i]; + if (diff < 0) + diff = -diff; + if (diff > 0.001f) { + return i == 0 ? -1 : (int)i; + // error ++; + } + } + return error; +} + +int main() { + const unsigned int num_cores = mempool_get_core_count(); + const unsigned int cid = mempool_get_core_id(); + + const unsigned int measure_iterations = 1; + + unsigned int timer_start, timer_end, timer; + + unsigned int m_start, m_end; + unsigned int p_start, p_end; + unsigned int kernel_size; + + mempool_init(cid); + mempool_barrier_init(cid); + + // Reset timer + timer = (unsigned int)-1; + + // Set matrix dimension + kernel_size = 4; + + // Work over complete P dimension + p_start = 0; + p_end = gemm_l.N; + m_start = (gemm_l.M / core_count) * cid; + m_end = (gemm_l.M / core_count) * (cid + 1); + + // Wait for all cores to finish + mempool_barrier(num_cores); + + if (cid == 0) { + printf("init a\n"); + init_matrix(a, gemm_A_dram, gemm_l.M * gemm_l.K); + printf("init b\n"); + init_matrix(b, gemm_B_dram, gemm_l.K * gemm_l.N); + printf("init c\n"); + init_matrix(c, gemm_C_dram, gemm_l.M * gemm_l.N); + printf("init r\n"); + init_matrix(r, gemm_checksum, gemm_l.M); + } + + if (cid == 0) { + printf("finish\n"); + } + + // Wait for all cores to finish + mempool_barrier(num_cores); + + // Calculate matmul + for (unsigned int i = 0; i < measure_iterations; ++i) { + timer_start = mempool_get_timer(); + + if (cid < core_count) { + if (kernel_size == 2) { + matmul_2xVL(c, a, b, m_start, m_end, gemm_l.K, gemm_l.N, p_start, p_end); + } else if (kernel_size == 4) { + matmul_4xVL(c, a, b, m_start, m_end, gemm_l.K, gemm_l.N, p_start, p_end); + } else if (kernel_size == 8) { + matmul_8xVL(c, a, b, m_start, m_end, gemm_l.K, gemm_l.N, p_start, p_end); + } else { + return -2; + } + } + // Wait for all cores to finish + mempool_barrier(num_cores); + + // End timer and check if new best runtime + timer_end = mempool_get_timer(); + unsigned int timer_temp = timer_end - timer_start; + if (cid == 0) { + if (timer_temp < timer) { + timer = timer_temp; + } + } + } + + // Check and display results + if (cid == 0) { + long unsigned int performance = + 1000 * 2 * gemm_l.M * gemm_l.N * gemm_l.K / timer; + long unsigned int utilization = performance / (2 * num_cores * 8); + + printf("\n----- (%dx%d) sp fmatmul -----\n", gemm_l.M, gemm_l.N); + printf("The execution took %u cycles.\n", timer); + printf("The performance is %ld OP/1000cycle (%ld%%o utilization).\n", + performance, utilization); + } + + if (cid == 0) { + int error = + verify_matrix(c, (const float *)gemm_checksum, gemm_l.M, gemm_l.N); + + if (error != 0) { + // printf("Error core %d: c[%d]=%u\n", cid, error, (int)c[error]); + printf("Error count:%d", error); + return error; + } + } + + // Wait for all cores to finish + mempool_barrier(num_cores); + + return 0; +} diff --git a/software/apps/spatz_apps/sp-fmatmul/script/gen_data.py b/software/apps/spatz_apps/sp-fmatmul/script/gen_data.py new file mode 100755 index 000000000..70397b332 --- /dev/null +++ b/software/apps/spatz_apps/sp-fmatmul/script/gen_data.py @@ -0,0 +1,700 @@ +#!/usr/bin/env python3 +# Copyright 2022 ETH Zurich and University of Bologna. +# Licensed under the Apache License, Version 2.0, see LICENSE for details. +# SPDX-License-Identifier: Apache-2.0 + +# Author: Tim Fischer + +import numpy as np +import torch +import torch.nn as nn +import argparse +import pathlib +import hjson + +np.random.seed(42) +torch.manual_seed(42) + +global verbose + + +def array_to_cstr(a, fmt=float): + out = "{" + if fmt == float: + if isinstance(a, np.ndarray): + a = a.flat + if isinstance(a, torch.Tensor): + a = a.numpy().flat + for el in a: + out += "{}, ".format(el) + else: + for sign, exp, mant in zip( + a["sign"].numpy().flat, + a["exponent"].numpy().flat, + a["mantissa"].numpy().flat, + ): + value = sign * 2**7 + exp * 2**2 + mant + out += "0x{:02x}, ".format(value) + out = out[:-2] + "}" + return out + + +def emit_header_file(layer_type: str, **kwargs): + + file_path = pathlib.Path(__file__).parent.parent / "data" + emit_str = ( + "// Copyright 2023 ETH Zurich and University of Bologna.\n" + + "// Licensed under the Apache License, Version 2.0, see LICENSE for details.\n" + + "// SPDX-License-Identifier: Apache-2.0\n\n" + + "// This file was generated automatically.\n\n" + ) + + if layer_type == "Conv2d": + file = file_path / "data_conv2d.h" + emit_str += emit_conv2d_layer(**kwargs) + elif layer_type == "GEMM": + file = file_path / ("data_" + str(kwargs["M"]) + "_" + str(kwargs["N"]) + "_" + str(kwargs["K"]) + ".h") + emit_str += emit_GEMM_layer(**kwargs) + elif layer_type == "BatchNorm": + file = file_path / "data_batchnorm.h" + emit_str += emit_batchnorm_layer(**kwargs) + elif layer_type == "MaxPool": + file = file_path / "data_maxpool.h" + emit_str += emit_maxpool_layer(**kwargs) + elif layer_type == "FusedConv": + file = file_path / "data_fusedconv.h" + emit_str += emit_fusedconv(**kwargs) + with file.open("w") as f: + f.write(emit_str) + + +def emit_conv2d_layer(name="conv2d", **kwargs): + ifmap = kwargs["ifmap"] + ofmap = kwargs["ofmap"] + weights = kwargs["weights"] + + n, ih, iw, ci = ifmap.shape + _, oh, ow, co = ofmap.shape + _, fh, fw, _ = weights.shape + + layer_str = "" + layer_str += '#include "layer.h"\n\n' + layer_str += f"conv_layer {name}_l = {{\n" + layer_str += f"\t.CO = {co},\n" + layer_str += f"\t.CI = {ci},\n" + layer_str += f"\t.IH = {ih},\n" + layer_str += f"\t.IW = {iw},\n" + layer_str += f"\t.OH = {oh},\n" + layer_str += f"\t.OW = {ow},\n" + layer_str += f"\t.FH = {fh},\n" + layer_str += f"\t.FW = {fw}\n" + layer_str += "};\n\n\n" + + layer_str += f'static double {name}_result[{oh}][{ow}][{co}] __attribute__((section(".data")));\n\n' + layer_str += ( + f"static double {name}_checksum[{oh}][{ow}] = " + + array_to_cstr(torch.sum(ofmap, dim=-1)) + + ";\n\n\n" + ) + layer_str += ( + f"static double {name}_ifmap_dram[{ih}][{iw}][{ci}] = " + + array_to_cstr(ifmap) + + ";\n\n\n" + ) + layer_str += ( + f"static double {name}_weights_dram[{co}][{ci}][{fh}][{fw}] = " + + array_to_cstr(weights) + + ";\n\n\n" + ) + layer_str += ( + f"static double {name}_ofmap_dram[{oh}][{ow}][{co}] = " + + array_to_cstr(ofmap) + + ";\n\n\n" + ) + + return layer_str + + +def emit_linear_layer(input, weights, ofmap): + + layer_str = "" + return layer_str + + +def emit_GEMM_layer(name="gemm", **kwargs): + mat_A = kwargs["A"] + mat_B = kwargs["B"] + mat_C = kwargs["C"] + result = kwargs["result"] + + m = kwargs["M"] + n = kwargs["N"] + k = kwargs["K"] + + layer_str = "" + layer_str += '#include "layer.h"\n\n' + layer_str += f"const gemm_layer {name}_l = {{\n" + layer_str += f"\t.M = {m},\n" + layer_str += f"\t.N = {n},\n" + layer_str += f"\t.K = {k},\n" + layer_str += f'\t.TA = {int(kwargs["ta"])},\n' + layer_str += f'\t.TB = {int(kwargs["tb"])},\n' + layer_str += f'\t.ALPHA = {kwargs["alpha"]},\n' + layer_str += f'\t.dtype = FP{kwargs["prec"]},\n' + layer_str += f'\t.expand = {kwargs["expand"]}\n' + layer_str += "};\n\n\n" + + ctypes = {"64": "double", "32": "float", "16": "__fp16", "8": "char"} + + dtype = ctypes[str(kwargs["prec"])] + if dtype != "char": + layer_str += f'{dtype} a[{m}*{k}] __attribute__((section(".l1")));\n' + layer_str += f'{dtype} b[{k}*{n}] __attribute__((section(".l1")));\n' + layer_str += f'{dtype} c[{m}*{n}] __attribute__((section(".l1")));\n' + layer_str += ( + f'static {dtype} {name}_A_dram [{m}*{k}] __attribute__((section(".data"))) = ' + + array_to_cstr(mat_A) + + ";\n\n\n" + ) + layer_str += ( + f'static {dtype} {name}_B_dram [{k}*{n}] __attribute__((section(".data"))) = ' + + array_to_cstr(mat_B) + + ";\n\n\n" + ) + layer_str += ( + f'static {dtype} {name}_C_dram [{m}*{n}] __attribute__((section(".data"))) = ' + + array_to_cstr(mat_C) + + ";\n\n\n" + ) + layer_str += ( + f"static const {dtype} {name}_checksum[{m}] = " + + array_to_cstr(torch.sum(result, dim=-1)) + + ";\n\n\n" + ) + else: + layer_str += ( + f"static {dtype} {name}_A_dram [{m}][{k}] = " + + array_to_cstr(kwargs["bits_A"], fmt="char") + + ";\n\n\n" + ) + layer_str += ( + f"static {dtype} {name}_B_dram [{k}][{n}] = " + + array_to_cstr(kwargs["bits_B"], fmt="char") + + ";\n\n\n" + ) + layer_str += ( + f"static {dtype} {name}_C_dram [{m}][{n}] = " + + array_to_cstr(kwargs["bits_C"], fmt="char") + + ";\n\n\n" + ) + + return layer_str + + +def emit_batchnorm_layer(name="batchnorm", **kwargs): + + ifmap = kwargs["ifmap"] + ofmap = kwargs["ofmap"] + beta = kwargs["beta"] + gamma = kwargs["gamma"] + + n, ih, iw, ci = ifmap.shape + _, oh, ow, co = ofmap.shape + + layer_str = "" + layer_str += '#include "layer.h"\n\n' + layer_str += f"conv_layer {name}_l = {{\n" + layer_str += f"\t.CO = {co},\n" + layer_str += f"\t.CI = {ci},\n" + layer_str += f"\t.IH = {ih},\n" + layer_str += f"\t.IW = {iw},\n" + layer_str += f"\t.OH = {oh},\n" + layer_str += f"\t.OW = {ow},\n" + layer_str += "};\n\n\n" + + layer_str += f'static double {name}_result[{oh}][{ow}][{co}] __attribute__((section(".data")));\n\n' + layer_str += ( + f"static double {name}_checksum[{oh}][{ow}] = " + + array_to_cstr(torch.sum(ofmap, dim=-1)) + + ";\n\n\n" + ) + layer_str += ( + f"static double {name}_ifmap_dram[{ih}][{iw}][{ci}] = " + + array_to_cstr(ifmap) + + ";\n\n\n" + ) + layer_str += ( + f"static double {name}_beta_dram[{ci}] = " + array_to_cstr(beta) + ";\n\n\n" + ) + layer_str += ( + f"static double {name}_gamma_dram[{ci}] = " + array_to_cstr(gamma) + ";\n\n\n" + ) + layer_str += ( + f"static double {name}_ofmap_dram[{oh}][{ow}][{co}] = " + + array_to_cstr(ofmap) + + ";\n\n\n" + ) + + return layer_str + + +def emit_maxpool_layer(name="maxpool", **kwargs): + + ifmap = kwargs["ifmap"] + ofmap = kwargs["ofmap"] + k = kwargs["kernel_size"] + + n, ih, iw, ci = ifmap.shape + _, oh, ow, co = ofmap.shape + + layer_str = "" + layer_str += '#include "layer.h"\n\n' + layer_str += f"conv_layer {name}_l = {{\n" + layer_str += f"\t.CO = {co},\n" + layer_str += f"\t.CI = {ci},\n" + layer_str += f"\t.IH = {ih},\n" + layer_str += f"\t.IW = {iw},\n" + layer_str += f"\t.OH = {oh},\n" + layer_str += f"\t.OW = {ow},\n" + layer_str += f"\t.FH = {k},\n" + layer_str += f"\t.FW = {k},\n" + layer_str += "};\n\n\n" + + layer_str += f'static double {name}_result[{oh}][{ow}][{co}] __attribute__((section(".data")));\n\n' + layer_str += ( + f"static double {name}_checksum[{oh}][{ow}] = " + + array_to_cstr(torch.sum(ofmap, dim=-1)) + + ";\n\n\n" + ) + layer_str += ( + f"static double {name}_ifmap_dram[{ih}][{iw}][{ci}] = " + + array_to_cstr(ifmap) + + ";\n\n\n" + ) + layer_str += ( + f"static double {name}_ofmap_dram[{oh}][{ow}][{co}] = " + + array_to_cstr(ofmap) + + ";\n\n\n" + ) + + return layer_str + + +def emit_fusedconv(name="fusedconv", **kwargs): + + ifmap = kwargs["ifmap"] + kernel = kwargs["kernel"] + bn_k = kwargs["bn_k"] + bn_l = kwargs["bn_l"] + ofmap = kwargs["ofmap"] + ofmap_before = kwargs["ofmap_before"] + ifmap_padded = kwargs["ifmap_padded"] + + padding = kwargs["padding"] + + if kwargs["depthwise"]: + ih, iw, ci = ifmap.shape + oh, ow, co = ofmap.shape + fh, fw, co = kernel.shape + ci = co + ih_pad, iw_pad, _ = ifmap_padded.shape + elif kwargs["chw_layer"]: + ci, ih, iw = ifmap.shape + oh, ow, co = ofmap.shape + co, ci, fh, fw = kernel.shape + _, ih_pad, iw_pad = ifmap_padded.shape + else: + ih, iw, ci = ifmap.shape + oh, ow, co = ofmap.shape + _, fh, fw, _ = kernel.shape + ih_pad, iw_pad, _ = ifmap_padded.shape + + ctypes = {"64": "double", "32": "float", "16": "__fp16", "8": "char"} + + dtype = ctypes[str(kwargs["prec"])] + + layer_str = "#include \n" + layer_str += '#include "conv2d.h"\n\n' + layer_str += "kernel_fp32 k = {\n" + layer_str += f"\t.ch_in = {ci},\n" + layer_str += f"\t.ch_out = {co},\n" + layer_str += f"\t.dim_in_x = {iw},\n" + layer_str += f"\t.dim_in_y = {ih},\n" + layer_str += f"\t.dim_kernel_x = {fw},\n" + layer_str += f"\t.dim_kernel_y = {fh},\n" + layer_str += f"\t.dim_out_x = {ow},\n" + layer_str += f"\t.dim_out_y = {oh},\n" + layer_str += f'\t.padding_y_top = {padding["padding_y_top"]},\n' + layer_str += f'\t.padding_y_bottom = {padding["padding_y_bottom"]},\n' + layer_str += f'\t.padding_x_left = {padding["padding_x_left"]},\n' + layer_str += f'\t.padding_x_right = {padding["padding_x_right"]},\n' + layer_str += f'\t.stride_x = {kwargs["stride"]["stride_x"]},\n' + layer_str += f'\t.stride_y = {kwargs["stride"]["stride_y"]},\n' + layer_str += f'\t.flag_relu = {kwargs["flags"]["flag_relu"]},\n' + layer_str += f'\t.flag_batch_norm = {kwargs["flags"]["flag_batch_norm"]},\n' + layer_str += ( + f'\t.flag_y_accumulate_start = {kwargs["flags"]["flag_y_accumulate_start"]},\n' + ) + layer_str += ( + f'\t.flag_y_accumulate_end = {kwargs["flags"]["flag_y_accumulate_end"]},\n' + ) + layer_str += "};\n\n" + layer_str += f'uint32_t dw = {kwargs["depthwise"]};\n' + layer_str += f'uint32_t chw_layer = {kwargs["chw_layer"]};\n' + + layer_str += ( + f"static {dtype} {name}_pInBuffer_dram[{ih_pad}][{iw_pad}][{ci}] = " + + array_to_cstr(ifmap_padded) + + ";\n\n" + ) + layer_str += f"static {dtype} {name}_pWeight_dram[{co}][{fh}][{fw}][{ci}] = {array_to_cstr(kernel)};\n\n" + layer_str += f"static {dtype} {name}_lambda_dram[{ci}] = {array_to_cstr(bn_l)};\n\n" + layer_str += f"static {dtype} {name}_kappa_dram[{ci}] = {array_to_cstr(bn_k)};\n\n" + layer_str += f"static {dtype} {name}_pOutBuffer_dram[{oh}][{ow}][{co}] = {array_to_cstr(ofmap_before)};\n\n" + layer_str += f"static {dtype} {name}_pCheckOutBuffer_dram[{oh}][{ow}][{co}] = {array_to_cstr(ofmap)};\n\n" + + return layer_str + + +def rand_data_generator(shape, prec, alt=False): + if prec == 64: + return torch.randn(shape, requires_grad=False, dtype=torch.float64), {} + elif prec == 32: + return torch.randn(shape, requires_grad=False, dtype=torch.float32), {} + elif prec == 16: + if alt: + return torch.randn(shape, requires_grad=False, dtype=torch.bfloat16), {} + else: + return torch.randn(shape, requires_grad=False, dtype=torch.float16), {} + elif prec == 8: + sign = torch.randint( + 0, 2, shape, requires_grad=False, dtype=torch.uint8 + ) # -1 or 1 + exponent = torch.randint( + 0, 16, shape, requires_grad=False, dtype=torch.uint8 + ) # < 0b01111 + mantissa = torch.randint( + 0, 4, shape, requires_grad=False, dtype=torch.uint8 + ) # can be arbitrary + bits = {"sign": sign, "exponent": exponent, "mantissa": mantissa} + # TODO: not actually correct + return ((-1.0) ** sign.double()) * (2.0 ** (exponent.double() - 15.0)) * ( + 1.0 + mantissa.double() / (2**2) + ), bits + + +def conv2d(ifmap, weights, padding=1, stride=1): + n, ci, ih, iw = ifmap.shape + co, _, fh, fw = weights.shape + + conv2d = nn.Conv2d(ci, co, (fh, fw), padding=((fh - 1) // 2, (fw - 1) // 2)) + conv2d.weight = nn.Parameter(weights, requires_grad=False) + conv2d.bias = nn.Parameter( + torch.zeros_like(conv2d.bias, dtype=weights.dtype), requires_grad=False + ) + ofmap = conv2d(ifmap) + + return ofmap + + +def max_pooling(ifmap, kernel): + n, ci, ih, iw = ifmap.shape + max_pool = nn.MaxPool2d(kernel_size=kernel) + ofmap = max_pool(ifmap) + + return ofmap + + +def batchnorm(ifmap): + n, ci, ih, iw = ifmap.shape + bn = torch.nn.BatchNorm2d(ci) + bn.weight.requires_grad = False + bn.bias.requires_grad = False + running_mean = torch.randn_like(bn.running_mean, requires_grad=False) + running_var = torch.rand_like(bn.running_var, requires_grad=False) + gamma = bn.weight / torch.sqrt(running_var + bn.eps) + beta = bn.bias - running_mean * bn.weight / torch.sqrt(running_var + bn.eps) + ofmap = ifmap * gamma.unsqueeze(-1).unsqueeze(-1) + beta.unsqueeze(-1).unsqueeze(-1) + + return ofmap, gamma, beta + + +def fused_conv( + ifmap, weights, bn_k, bn_l, padding, stride, bn, relu, accumulate, depthwise +): + + ih, iw, ci = ifmap.shape + if not depthwise: + co, fh, fw, _ = weights.shape + else: + fh, fw, co = weights.shape + ci = co + + ifmap_padded = torch.zeros( + ih + padding["padding_y_top"] + padding["padding_y_bottom"], + iw + padding["padding_x_left"] + padding["padding_x_right"], + ci, + requires_grad=False, + dtype=ifmap.dtype, + ) + ifmap_padded[ + padding["padding_y_top"] : ih + padding["padding_y_top"], + padding["padding_x_left"] : iw + padding["padding_x_left"], + ] = ifmap + + # Don't cover undefined behaviour when there are steps without a complete kernel window + if (ifmap_padded.shape[0] - (fh - 1) - 1) % stride["stride_y"] != 0: + print("Warning: rounding h output dimension") + if (ifmap_padded.shape[1] - (fw - 1) - 1) % stride["stride_x"] != 0: + print("Warning: rounding w output dimension") + + ofmap = torch.zeros( + (ifmap_padded.shape[0] - (fh - 1) - 1) // stride["stride_y"] + 1, + (ifmap_padded.shape[1] - (fw - 1) - 1) // stride["stride_x"] + 1, + co, + ) + if accumulate: + ofmap_before = torch.randn_like(ofmap, requires_grad=False) + else: + ofmap_before = torch.zeros_like(ofmap, requires_grad=False) + + if verbose: + print(ifmap.shape, ifmap_padded.shape, ofmap.shape) + + if depthwise: + # depthwise Conv2d + for h in range(0, ifmap_padded.shape[0] - (fh - 1), stride["stride_y"]): + for w in range(0, ifmap_padded.shape[1] - (fw - 1), stride["stride_x"]): + for c in range(co): + ofmap[ + h // stride["stride_y"], w // stride["stride_x"], c + ] = torch.dot( + ifmap_padded[h : h + fh, w : w + fw, c].flatten(), + weights[:, :, c].flatten(), + ) + else: + # Conv2d + for h in range(0, ifmap_padded.shape[0] - (fh - 1), stride["stride_y"]): + for w in range(0, ifmap_padded.shape[1] - (fw - 1), stride["stride_x"]): + for c in range(co): + ofmap[ + h // stride["stride_y"], w // stride["stride_x"], c + ] = torch.dot( + ifmap_padded[h : h + fh, w : w + fw].flatten(), + weights[c].flatten(), + ) + + ofmap += ofmap_before + + # BatchNorm + if bn: + ofmap = ofmap * bn_k + bn_l + + # ReLU + if relu: + ofmap = torch.nn.functional.relu(ofmap) + + return ofmap, ofmap_before, ifmap_padded + + +def main(): + + parser = argparse.ArgumentParser(description="Generate data for kernels") + parser.add_argument( + "-c", + "--cfg", + type=pathlib.Path, + required=True, + help="Select param config file kernel", + ) + parser.add_argument("-v", "--verbose", action="store_true", help="Set verbose") + + args = parser.parse_args() + + global verbose + verbose = args.verbose + + with args.cfg.open() as f: + param = hjson.loads(f.read()) + + if param["prec"] == 64: + dtype = torch.float64 + elif param["prec"] == 16: + dtype = torch.float16 + elif param["prec"] == 8: + dtype = None + else: + dtype = torch.float32 + + if param["kernel"] == "Conv2d": + ifmap = torch.randn( + 1, + param["channels"]["in"], + param["input_dim"]["height"], + param["input_dim"]["width"], + requires_grad=False, + dtype=dtype, + ) + weights = torch.randn( + param["channels"]["out"], + param["channels"]["in"], + param["filter"]["height"], + param["filter"]["width"], + requires_grad=False, + dtype=dtype, + ) + + ofmap = conv2d( + ifmap, + weights, + padding=param["filter"]["padding"], + stride=param["filter"]["stride"], + ) + + # convert from CHW to HWC format + ifmap = ifmap.permute(0, 2, 3, 1) + ofmap = ofmap.permute(0, 2, 3, 1) + weights = weights.permute(0, 2, 3, 1) + kwargs = {"ifmap": ifmap, "weights": weights, "ofmap": ofmap} + emit_header_file("Conv2d", **kwargs) + + elif param["kernel"] == "GEMM": + mat_A, bits_A = rand_data_generator((param["M"], param["K"]), param["prec"]) + mat_B, bits_B = rand_data_generator((param["K"], param["N"]), param["prec"]) + mat_C, bits_C = rand_data_generator((param["M"], param["N"]), param["prec"]) + + result = param["alpha"] * mat_C + torch.matmul(mat_A, mat_B) + + if param["transpose_A"]: + mat_A = mat_A.T + if param["transpose_B"]: + mat_B = mat_B.T + + kwargs = { + "A": mat_A, + "B": mat_B, + "C": mat_C, + "result": result, + "M": param["M"], + "N": param["N"], + "K": param["K"], + "ta": param["transpose_A"], + "tb": param["transpose_B"], + "alpha": param["alpha"], + "prec": param["prec"], + "expand": param["expand"], + "bits_A": bits_A, + "bits_B": bits_B, + "bits_C": bits_C, + } + + emit_header_file("GEMM", **kwargs) + + elif param["kernel"] == "BatchNorm": + ifmap = torch.randn( + 1, + param["channels"]["in"], + param["input_dim"]["height"], + param["input_dim"]["width"], + requires_grad=False, + dtype=dtype, + ) + + ofmap, gamma, beta = batchnorm(ifmap) + + # convert from CHW to HWC format + ifmap = ifmap.permute(0, 2, 3, 1) + ofmap = ofmap.permute(0, 2, 3, 1) + + kwargs = {"ifmap": ifmap, "beta": beta, "gamma": gamma, "ofmap": ofmap} + emit_header_file("BatchNorm", **kwargs) + + elif param["kernel"] == "MaxPool": + ifmap = torch.randn( + 1, + param["channels"]["in"], + param["input_dim"]["height"], + param["input_dim"]["width"], + requires_grad=False, + dtype=dtype, + ) + + ofmap = max_pooling(ifmap, param["kernel_size"]) + + # convert from CHW to HWC format + ifmap = ifmap.permute(0, 2, 3, 1) + ofmap = ofmap.permute(0, 2, 3, 1) + + kwargs = {"ifmap": ifmap, "ofmap": ofmap, "kernel_size": param["kernel_size"]} + emit_header_file("MaxPool", **kwargs) + + elif param["kernel"] == "FusedConv": + ifmap = torch.randn( + param["dim_in_y"], + param["dim_in_x"], + param["ch_in"], + requires_grad=False, + dtype=dtype, + ) + if not param["depthwise"]: + kernel = torch.randn( + param["ch_out"], + param["dim_kernel_y"], + param["dim_kernel_x"], + param["ch_in"], + requires_grad=False, + dtype=dtype, + ) + else: + kernel = torch.randn( + param["dim_kernel_y"], + param["dim_kernel_x"], + param["ch_in"], + requires_grad=False, + dtype=dtype, + ) + + bn_k = torch.randn(param["ch_out"], requires_grad=False) + bn_l = torch.randn(param["ch_out"], requires_grad=False) + + ofmap, ofmap_before, ifmap_padded = fused_conv( + ifmap, + kernel, + bn_k, + bn_l, + param["padding"], + param["stride"], + param["flags"]["flag_batch_norm"], + param["flags"]["flag_relu"], + not param["flags"]["flag_y_accumulate_start"], + param["depthwise"], + ) + + if param["chw_layer"]: + ifmap = ifmap.permute(2, 0, 1) + ifmap_padded = ifmap_padded.permute(2, 0, 1) + kernel = kernel.permute(0, 3, 1, 2) + + kwargs = { + "ifmap": ifmap, + "ifmap_padded": ifmap_padded, + "ofmap": ofmap, + "ofmap_before": ofmap_before, + "kernel": kernel, + "bn_k": bn_k, + "bn_l": bn_l, + "padding": param["padding"], + "stride": param["stride"], + "prec": param["prec"], + "flags": param["flags"], + "depthwise": param["depthwise"], + "chw_layer": param["chw_layer"], + } + emit_header_file("FusedConv", **kwargs) + + else: + print("No valid kernel selected") + + +if __name__ == "__main__": + main() diff --git a/software/apps/spatz_apps/sp-fmatmul/script/matmul.json b/software/apps/spatz_apps/sp-fmatmul/script/matmul.json new file mode 100644 index 000000000..47521867f --- /dev/null +++ b/software/apps/spatz_apps/sp-fmatmul/script/matmul.json @@ -0,0 +1,17 @@ +// Copyright 2020 ETH Zurich and University of Bologna. +// Solderpad Hardware License, Version 0.51, see LICENSE for details. +// SPDX-License-Identifier: SHL-0.51 + +// Parameters for a GEMM + +{ + kernel: "GEMM" + M: 64, + N: 128, + K: 64, + alpha: 0, + transpose_A: false, + transpose_B: false, + prec: 32, + expand: 0 +}