-
-
Notifications
You must be signed in to change notification settings - Fork 2
Commit
This commit does not belong to any branch on this repository, and may belong to a fork outside of the repository.
- Loading branch information
1 parent
0241cb3
commit 4860245
Showing
10 changed files
with
247 additions
and
254 deletions.
There are no files selected for viewing
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -1,258 +1,13 @@ | ||
| using NNlib | ||
| using NNlib.ChainRulesCore: rrule | ||
| using Random | ||
|
|
||
| SUITE["nnlib"] = BenchmarkGroup() | ||
|
|
||
| ########## activations ############ | ||
| SUITE["nnlib"]["activations"] = BenchmarkGroup() | ||
| for et in (Float64, Float32, Float16,) | ||
| et_suite = BenchmarkGroup() | ||
| SUITE["nnlib"]["activations"][string(et)] = et_suite | ||
| let x = rand(et, 1024, 1024), y = similar(x) | ||
| for f in NNlib.ACTIVATIONS | ||
| act = @eval($f) | ||
| et_suite[string(f)] = @benchmarkable broadcast!($act, $y, $x) | ||
| end | ||
| end | ||
| end | ||
|
|
||
|
|
||
| ########## softmax ############ | ||
| SUITE["nnlib"]["softmax"] = BenchmarkGroup() | ||
| for (fn!, fn_bw) in [(softmax!, NNlib.∇softmax_data), (logsoftmax!, NNlib.∇logsoftmax_data)] | ||
| fn_suite = BenchmarkGroup() | ||
| SUITE["nnlib"]["softmax"][rstrip(string(fn!), '!')] = fn_suite | ||
| let SIZES = [ | ||
| (12288, 2048, 1), (4096, 4096, 2), (4096, 2048, 2), (2048, 2048, 2), | ||
| (1024, 2048, 4), (768, 1024, 4), (512, 784, 8), (128, 384, 8), | ||
| ] | ||
| for et in (Float32, Float16,) | ||
| et_suite = BenchmarkGroup("fw" => BenchmarkGroup(), "bw" => BenchmarkGroup()) | ||
| fn_suite[string(et)] = et_suite | ||
| for sz in SIZES | ||
| x = randn(et, sz) | ||
| y = similar(x) | ||
| dy = zero(x) | ||
| fn!(y, x) | ||
| et_suite["fw"][string(sz)] = @benchmarkable $fn!($y, $x) | ||
| et_suite["bw"][string(sz)] = @benchmarkable $fn_bw($dy, $y) | ||
| end | ||
| end | ||
| end | ||
| end | ||
|
|
||
|
|
||
| ########## conv ############ | ||
| SUITE["nnlib"]["conv"] = BenchmarkGroup() | ||
| for rank in (3, 2, 1,), N in (512, 256,), K in (3,), | ||
| C_in in (1,), C_out in (1,), | ||
| stride in (1,), dilation in (1,), padding in (2, 0,) | ||
|
|
||
| size_suite = BenchmarkGroup() | ||
| SUITE["nnlib"]["conv"][ | ||
| "$(rank+2)-N($N)-K($K)-in($C_in)-out($C_out)-stride($stride)-dilation($dilation)-padding($padding)" | ||
| ] = size_suite | ||
|
|
||
| conv_items = [ | ||
| (NNlib.conv_direct!, NNlib.∇conv_data_direct!, NNlib.∇conv_filter_direct!, DenseConvDims, "direct"), | ||
| (NNlib.conv_im2col!, NNlib.∇conv_data_im2col!, NNlib.∇conv_filter_im2col!, DenseConvDims, "im2col"), | ||
| (NNlib.depthwiseconv_direct!, NNlib.∇depthwiseconv_data_direct!, NNlib.∇depthwiseconv_filter_direct!, DepthwiseConvDims, "direct"), | ||
| (NNlib.depthwiseconv_im2col!, NNlib.∇depthwiseconv_data_im2col!, NNlib.∇depthwiseconv_filter_im2col!, DepthwiseConvDims, "im2col"), | ||
| ] | ||
|
|
||
| for (conv!, ∇conv_data!, ∇conv_filter!, cdimT, _) in conv_items | ||
| conv_suite = BenchmarkGroup() | ||
| SUITE["nnlib"]["conv"][ | ||
| "$(rank+2)-N($N)-K($K)-in($C_in)-out($C_out)-stride($stride)-dilation($dilation)-padding($padding)" | ||
| ][rstrip(string(conv!), '!')] = conv_suite | ||
|
|
||
| for et in (Float32, Float64) | ||
| et_suite = BenchmarkGroup() | ||
| SUITE["nnlib"]["conv"][ | ||
| "$(rank+2)-N($N)-K($K)-in($C_in)-out($C_out)-stride($stride)-dilation($dilation)-padding($padding)" | ||
| ][rstrip(string(conv!), '!')][string(et)] = et_suite | ||
|
|
||
| x = zeros(et, repeat([N], rank)..., C_in, 1) | ||
| w = (cdimT == DenseConvDims) ? | ||
| zeros(et, repeat([K], rank)..., C_in, C_out) : | ||
| zeros(et, repeat([K], rank)..., C_out, C_in) | ||
|
|
||
| cdims = try | ||
| cdimT(x, w; stride = stride, dilation = dilation, padding = padding) | ||
| catch | ||
| continue | ||
| end | ||
|
|
||
| y = (cdimT == DenseConvDims) ? | ||
| zeros(et, NNlib.output_size(cdims)..., C_out, 1) : | ||
| zeros(et, NNlib.output_size(cdims)..., C_out*C_in, 1) | ||
|
|
||
| dx, dy, dw = similar(x), similar(y), similar(w) | ||
| SUITE["nnlib"]["conv"][ | ||
| "$(rank+2)-N($N)-K($K)-in($C_in)-out($C_out)-stride($stride)-dilation($dilation)-padding($padding)" | ||
| ][rstrip(string(conv!), '!')][string(et)]["conv"] = @benchmarkable $(conv!)($y, $x, $w, $cdims) | ||
| SUITE["nnlib"]["conv"][ | ||
| "$(rank+2)-N($N)-K($K)-in($C_in)-out($C_out)-stride($stride)-dilation($dilation)-padding($padding)" | ||
| ][rstrip(string(conv!), '!')][string(et)]["data"] = @benchmarkable $(∇conv_data!)($dx, $y, $w, $cdims) | ||
| SUITE["nnlib"]["conv"][ | ||
| "$(rank+2)-N($N)-K($K)-in($C_in)-out($C_out)-stride($stride)-dilation($dilation)-padding($padding)" | ||
| ][rstrip(string(conv!), '!')][string(et)]["filter"] = @benchmarkable $(∇conv_filter!)($dw, $x, $y, $cdims) | ||
| end | ||
| end | ||
| end | ||
|
|
||
|
|
||
| ########## pooling ############ | ||
| SUITE["nnlib"]["pooling"] = BenchmarkGroup() | ||
| for rank in (3, 2, 1,), N in (512, 256,), K in (4, 2,), stride in (4, 2, 1,) | ||
| size_suite = BenchmarkGroup() | ||
| SUITE["nnlib"]["pooling"]["$(rank+2)-N($N)-K($K)-stride($stride)"] = size_suite | ||
|
|
||
| x = zeros(Float32, repeat([N], rank)..., 1, 1) | ||
| pdims = PoolDims(x, K; stride = stride) | ||
| y = zeros(Float32, NNlib.output_size(pdims)..., 1, 1) | ||
| dx, dy = similar(x), similar(y) | ||
|
|
||
| pooling_items = [ | ||
| (NNlib.maxpool!, NNlib.∇maxpool!, "maxpool"), | ||
| (NNlib.meanpool!, NNlib.∇meanpool!, "meanpool"), | ||
| (NNlib.lpnormpool!, NNlib.∇lpnormpool!, "lpnormpool"), | ||
| ] | ||
|
|
||
| for (pool, ∇pool, name) in pooling_items | ||
| pooling_suite = BenchmarkGroup() | ||
| SUITE["nnlib"]["pooling"][ | ||
| "$(rank+2)-N($N)-K($K)-stride($stride)" | ||
| ]["$(name)$(rank)d-direct"] = pooling_suite | ||
| SUITE["nnlib"]["pooling"][ | ||
| "$(rank+2)-N($N)-K($K)-stride($stride)" | ||
| ]["$(name)$(rank)d-direct"]["pool"] = @benchmarkable $pool( | ||
| $y, $x, $pdims; p = ($name == "lpnormpool") ? 2 : nothing) | ||
| SUITE["nnlib"]["pooling"][ | ||
| "$(rank+2)-N($N)-K($K)-stride($stride)" | ||
| ]["$(name)$(rank)d-direct"]["data"] = @benchmarkable $(∇pool)( | ||
| $dx, $dy, $y, $x, $pdims; p = ($name == "lpnormpool") ? 2 : nothing) | ||
| end | ||
|
|
||
| if NNlib.is_nnpack_available() && NNlib.nnpack_supported_operation(pdims) | ||
| SUITE["nnlib"]["pooling"][ | ||
| "$(rank+2)-N($N)-K($K)-stride($stride)" | ||
| ]["maxpool$(rank)d-nnpack"]["pool"] = @benchmarkable NNlib.maxpool_nnpack!($y, $x, $pdims) | ||
| end | ||
| end | ||
|
|
||
|
|
||
| ########## dropout ############ | ||
| SUITE["nnlib"]["dropout"] = BenchmarkGroup() | ||
| for rank in (1, 2, 3,), N in (128, 512, 1024,) | ||
| size_suite = BenchmarkGroup() | ||
| SUITE["nnlib"]["dropout"]["$(rank+2)-N($N)"] = size_suite | ||
|
|
||
| x = ones(Float32, repeat([N], rank)..., 1, 1) | ||
| y = zeros(Float32, repeat([N], rank)..., 1, 1) | ||
| p = 0.2 | ||
|
|
||
| dropout_suite = BenchmarkGroup() | ||
| dropout_suite["with-colon"] = @benchmarkable dropout($x, $p) | ||
| dropout_suite["with-dim"] = @benchmarkable dropout($x, $p; dims = 1) | ||
| SUITE["nnlib"]["dropout"]["$(rank+2)-N($N)"]["dropout"] = dropout_suite | ||
|
|
||
| dropout!_suite = BenchmarkGroup() | ||
| dropout!_suite["with-colon"] = @benchmarkable dropout!($y, $x, $p) | ||
| dropout!_suite["with-dim"] = @benchmarkable dropout!($y, $x, $p; dims = 1) | ||
| SUITE["nnlib"]["dropout"]["$(rank+2)-N($N)"]["dropout!"] = dropout!_suite | ||
| end | ||
|
|
||
|
|
||
| ########## upsample ############ | ||
| SUITE["nnlib"]["upsample"] = BenchmarkGroup() | ||
| SUITE["nnlib"]["upsample"]["linear"] = BenchmarkGroup() | ||
| for rank in (3, 2, 1,), et in (Float32, Float16,) | ||
| et_suite = BenchmarkGroup("fw" => BenchmarkGroup(), "bw" => BenchmarkGroup()) | ||
| SUITE["nnlib"]["upsample"]["linear"][string(et)] = et_suite | ||
|
|
||
| inputs_sizes = [ | ||
| (1024, (0.5, 2), false), (256, 8, false), | ||
| (256, 4, true), (128, (1, 2), false), (128, 2, true), | ||
| ] | ||
| for (sz, scale, ac) in inputs_sizes | ||
| x = ones(et, repeat([sz], rank)..., 1, 1) | ||
| et_suite["fw"][ | ||
| "$(rank+2)-N($sz)-scale($scale)" | ||
| ] = @benchmarkable upsample_linear($x, $scale; align_corners = $ac) | ||
| et_suite["bw"][ | ||
| "$(rank+2)-N($sz)-scale($scale)" | ||
| ] = @benchmarkable ∇upsample_linear($x; | ||
| size = (typeof($scale) <: Tuple) ? | ||
| floor.(Integer, $sz .* $scale) : | ||
| ntuple(_ -> floor(Integer, $sz * $scale), $rank), | ||
| align_corners = $ac) | ||
| end | ||
| end | ||
|
|
||
| SUITE["nnlib"]["upsample"]["nearest"] = BenchmarkGroup() | ||
| for rank in (3, 2, 1,), N in (1024, 512, 128,) | ||
| et_suite = BenchmarkGroup() | ||
| for et in (Float64, Float32, Float16,) | ||
| x = zeros(Float32, repeat([N], rank)..., 1, 1) | ||
| et_suite[string(et)] = @benchmarkable upsample_nearest($x; size = (repeat([$N * 10], $rank)..., 1, 1)) | ||
| end | ||
| SUITE["nnlib"]["upsample"]["nearest"]["$(rank+2)-N($N)"] = et_suite | ||
| end | ||
|
|
||
|
|
||
| ########## gemm ############ | ||
| SUITE["nnlib"]["gemm"] = BenchmarkGroup() | ||
| for et in (Float32, Float64) | ||
| et_suite = BenchmarkGroup( | ||
| "gemm!" => BenchmarkGroup(), | ||
| "batched_gemm!" => BenchmarkGroup()) | ||
| SUITE["nnlib"]["gemm"][string(et)] = et_suite | ||
|
|
||
| # transA and transB are not of the main varaints. | ||
| # gemm! meets some memory problem, not included here. | ||
| input_items = [ | ||
| (Val(false), Val(false), 'N', 'N', 1024, 1024, 1024, et(0.5), et(0.0)), | ||
| (Val(false), Val(false), 'N', 'N', 512, 512, 128, et(0.5), et(1.0)), | ||
| (Val(false), Val(false), 'N', 'N', 80, 40, 100, et(1.0), et(0.0)), | ||
| ] | ||
| for (transA, transB, transA_ch, transB_ch, M, N, K, alpha, beta) in input_items | ||
| bA = ones(et, M, N, 1) | ||
| bB = ones(et, N, K, 1) | ||
| bC = zeros(et, M, K, 1) | ||
| et_suite["batched_gemm!"][ | ||
| "trans($transA_ch,$transB_ch)-M($M)-N($N)-K($K)-alpha($alpha)-beta($beta)" | ||
| ] = @benchmarkable NNlib.batched_gemm!( | ||
| $transA_ch, $transB_ch, | ||
| $alpha, $bA, $bB, $beta, $bC) | ||
| end | ||
| end | ||
|
|
||
|
|
||
| ########## attention ############ | ||
| SUITE["nnlib"]["attention"] = BenchmarkGroup() | ||
| for et in (Float16, Float64) | ||
| et_suite = BenchmarkGroup( | ||
| "attention" => BenchmarkGroup(), "score" => BenchmarkGroup()) | ||
| SUITE["nnlib"]["attention"][string(et)] = et_suite | ||
|
|
||
| input_items = [ | ||
| ((16,128,8), (16,512,8), (32,512,8), (512,128), 4), | ||
| ((64,64,16), (64,64,16), (64,64,16), (64,64), 4), | ||
| ((8,6,1), (8,10,1), (4,10,1), nothing, 1), | ||
| ] | ||
| for (q_sz, k_sz, v_sz, bias_sz, nheads) in input_items | ||
| q, q_score = rand(et, q_sz...), rand(et, 8, q_sz...) | ||
| k, k_score = rand(et, k_sz...), rand(et, 8, k_sz...) | ||
| v = rand(et, v_sz...) | ||
| bias = isnothing(bias_sz) ? nothing : rand(et, bias_sz...) | ||
| mask = isnothing(bias_sz) ? nothing : rand(Bool, bias_sz...) | ||
| et_suite["attention"][ | ||
| "q($q_sz)-k($k_sz)-v($v_sz)-bias($bias_sz)-nheads($nheads)" | ||
| ] = @benchmarkable dot_product_attention($q, $k, $v, $bias; nheads = $nheads) | ||
| et_suite["score"][ | ||
| "q(8, $q_sz)-k(8, $k_sz)-bias($bias_sz)-nheads($nheads)" | ||
| ] = @benchmarkable dot_product_attention_scores($q_score, $k_score, $bias; mask = $mask) | ||
| end | ||
| end | ||
| register_benchmark("FLUXML_BENCHMARK_NNLIB_ACTIVATIONS", "nnlib/activations.jl") | ||
| register_benchmark("FLUXML_BENCHMARK_NNLIB_SOFTMAX", "nnlib/softmax.jl") | ||
| register_benchmark("FLUXML_BENCHMARK_NNLIB_CONV", "nnlib/conv.jl") | ||
| register_benchmark("FLUXML_BENCHMARK_NNLIB_POOLING", "nnlib/pooling.jl") | ||
| register_benchmark("FLUXML_BENCHMARK_NNLIB_DROPOUT", "nnlib/dropout.jl") | ||
| register_benchmark("FLUXML_BENCHMARK_NNLIB_UPSAMPLE", "nnlib/upsample.jl") | ||
| register_benchmark("FLUXML_BENCHMARK_NNLIB_GEMM", "nnlib/gemm.jl") | ||
| register_benchmark("FLUXML_BENCHMARK_NNLIB_ATTENTION", "nnlib/attention.jl") |
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,12 @@ | ||
| ########## activations ############ | ||
| SUITE["nnlib"]["activations"] = BenchmarkGroup() | ||
| for et in (Float64, Float32, Float16,) | ||
| et_suite = BenchmarkGroup() | ||
| SUITE["nnlib"]["activations"][string(et)] = et_suite | ||
| let x = rand(et, 1024, 1024), y = similar(x) | ||
| for f in NNlib.ACTIVATIONS | ||
| act = @eval($f) | ||
| et_suite[string(f)] = @benchmarkable broadcast!($act, $y, $x) | ||
| end | ||
| end | ||
| end |
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,26 @@ | ||
| ########## attention ############ | ||
| SUITE["nnlib"]["attention"] = BenchmarkGroup() | ||
| for et in (Float16, Float64) | ||
| et_suite = BenchmarkGroup( | ||
| "attention" => BenchmarkGroup(), "score" => BenchmarkGroup()) | ||
| SUITE["nnlib"]["attention"][string(et)] = et_suite | ||
|
|
||
| input_items = [ | ||
| ((16,128,8), (16,512,8), (32,512,8), (512,128), 4), | ||
| ((64,64,16), (64,64,16), (64,64,16), (64,64), 4), | ||
| ((8,6,1), (8,10,1), (4,10,1), nothing, 1), | ||
| ] | ||
| for (q_sz, k_sz, v_sz, bias_sz, nheads) in input_items | ||
| q, q_score = rand(et, q_sz...), rand(et, 8, q_sz...) | ||
| k, k_score = rand(et, k_sz...), rand(et, 8, k_sz...) | ||
| v = rand(et, v_sz...) | ||
| bias = isnothing(bias_sz) ? nothing : rand(et, bias_sz...) | ||
| mask = isnothing(bias_sz) ? nothing : rand(Bool, bias_sz...) | ||
| et_suite["attention"][ | ||
| "q($q_sz)-k($k_sz)-v($v_sz)-bias($bias_sz)-nheads($nheads)" | ||
| ] = @benchmarkable dot_product_attention($q, $k, $v, $bias; nheads = $nheads) | ||
| et_suite["score"][ | ||
| "q(8, $q_sz)-k(8, $k_sz)-bias($bias_sz)-nheads($nheads)" | ||
| ] = @benchmarkable dot_product_attention_scores($q_score, $k_score, $bias; mask = $mask) | ||
| end | ||
| end |
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,58 @@ | ||
| ########## conv ############ | ||
| SUITE["nnlib"]["conv"] = BenchmarkGroup() | ||
| for rank in (3, 2, 1,), N in (512, 256,), K in (3,), | ||
| C_in in (1,), C_out in (1,), | ||
| stride in (1,), dilation in (1,), padding in (2, 0,) | ||
|
|
||
| size_suite = BenchmarkGroup() | ||
| SUITE["nnlib"]["conv"][ | ||
| "$(rank+2)-N($N)-K($K)-in($C_in)-out($C_out)-stride($stride)-dilation($dilation)-padding($padding)" | ||
| ] = size_suite | ||
|
|
||
| conv_items = [ | ||
| (NNlib.conv_direct!, NNlib.∇conv_data_direct!, NNlib.∇conv_filter_direct!, DenseConvDims, "direct"), | ||
| (NNlib.conv_im2col!, NNlib.∇conv_data_im2col!, NNlib.∇conv_filter_im2col!, DenseConvDims, "im2col"), | ||
| (NNlib.depthwiseconv_direct!, NNlib.∇depthwiseconv_data_direct!, NNlib.∇depthwiseconv_filter_direct!, DepthwiseConvDims, "direct"), | ||
| (NNlib.depthwiseconv_im2col!, NNlib.∇depthwiseconv_data_im2col!, NNlib.∇depthwiseconv_filter_im2col!, DepthwiseConvDims, "im2col"), | ||
| ] | ||
|
|
||
| for (conv!, ∇conv_data!, ∇conv_filter!, cdimT, _) in conv_items | ||
| conv_suite = BenchmarkGroup() | ||
| SUITE["nnlib"]["conv"][ | ||
| "$(rank+2)-N($N)-K($K)-in($C_in)-out($C_out)-stride($stride)-dilation($dilation)-padding($padding)" | ||
| ][rstrip(string(conv!), '!')] = conv_suite | ||
|
|
||
| for et in (Float32, Float64) | ||
| et_suite = BenchmarkGroup() | ||
| SUITE["nnlib"]["conv"][ | ||
| "$(rank+2)-N($N)-K($K)-in($C_in)-out($C_out)-stride($stride)-dilation($dilation)-padding($padding)" | ||
| ][rstrip(string(conv!), '!')][string(et)] = et_suite | ||
|
|
||
| x = zeros(et, repeat([N], rank)..., C_in, 1) | ||
| w = (cdimT == DenseConvDims) ? | ||
| zeros(et, repeat([K], rank)..., C_in, C_out) : | ||
| zeros(et, repeat([K], rank)..., C_out, C_in) | ||
|
|
||
| cdims = try | ||
| cdimT(x, w; stride = stride, dilation = dilation, padding = padding) | ||
| catch | ||
| continue | ||
| end | ||
|
|
||
| y = (cdimT == DenseConvDims) ? | ||
| zeros(et, NNlib.output_size(cdims)..., C_out, 1) : | ||
| zeros(et, NNlib.output_size(cdims)..., C_out*C_in, 1) | ||
|
|
||
| dx, dy, dw = similar(x), similar(y), similar(w) | ||
| SUITE["nnlib"]["conv"][ | ||
| "$(rank+2)-N($N)-K($K)-in($C_in)-out($C_out)-stride($stride)-dilation($dilation)-padding($padding)" | ||
| ][rstrip(string(conv!), '!')][string(et)]["conv"] = @benchmarkable $(conv!)($y, $x, $w, $cdims) | ||
| SUITE["nnlib"]["conv"][ | ||
| "$(rank+2)-N($N)-K($K)-in($C_in)-out($C_out)-stride($stride)-dilation($dilation)-padding($padding)" | ||
| ][rstrip(string(conv!), '!')][string(et)]["data"] = @benchmarkable $(∇conv_data!)($dx, $y, $w, $cdims) | ||
| SUITE["nnlib"]["conv"][ | ||
| "$(rank+2)-N($N)-K($K)-in($C_in)-out($C_out)-stride($stride)-dilation($dilation)-padding($padding)" | ||
| ][rstrip(string(conv!), '!')][string(et)]["filter"] = @benchmarkable $(∇conv_filter!)($dw, $x, $y, $cdims) | ||
| end | ||
| end | ||
| end |
This file contains bidirectional Unicode text that may be interpreted or compiled differently than what appears below. To review, open the file in an editor that reveals hidden Unicode characters.
Learn more about bidirectional Unicode characters
| Original file line number | Diff line number | Diff line change |
|---|---|---|
| @@ -0,0 +1,20 @@ | ||
| ########## dropout ############ | ||
| SUITE["nnlib"]["dropout"] = BenchmarkGroup() | ||
| for rank in (1, 2, 3,), N in (128, 512, 1024,) | ||
| size_suite = BenchmarkGroup() | ||
| SUITE["nnlib"]["dropout"]["$(rank+2)-N($N)"] = size_suite | ||
|
|
||
| x = ones(Float32, repeat([N], rank)..., 1, 1) | ||
| y = zeros(Float32, repeat([N], rank)..., 1, 1) | ||
| p = 0.2 | ||
|
|
||
| dropout_suite = BenchmarkGroup() | ||
| dropout_suite["with-colon"] = @benchmarkable dropout($x, $p) | ||
| dropout_suite["with-dim"] = @benchmarkable dropout($x, $p; dims = 1) | ||
| SUITE["nnlib"]["dropout"]["$(rank+2)-N($N)"]["dropout"] = dropout_suite | ||
|
|
||
| dropout!_suite = BenchmarkGroup() | ||
| dropout!_suite["with-colon"] = @benchmarkable dropout!($y, $x, $p) | ||
| dropout!_suite["with-dim"] = @benchmarkable dropout!($y, $x, $p; dims = 1) | ||
| SUITE["nnlib"]["dropout"]["$(rank+2)-N($N)"]["dropout!"] = dropout!_suite | ||
| end |
Oops, something went wrong.