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@kali
kali committed Feb 13, 2025
1 parent 24ec607 commit 7cf494b
Showing 1 changed file with 15 additions and 15 deletions.
30 changes: 15 additions & 15 deletions harness/pre-optimized-graphes/mdl-en-2019-Q3-librispeech/expected
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Original file line number Diff line number Diff line change
Expand Up @@ -144,9 +144,9 @@ graph network(input) -> (output) {
i"tdnn1.relu.output.low" = max(i"tdnn1.affine.output", i"tdnn1.relu.output.low.cst.1");
i"tdnn1.renorm.reduced.sum.sqr" = square(i"tdnn1.relu.output.low");
i"tdnn1.renorm.reduced.sum.sum" = sum_reduce(i"tdnn1.renorm.reduced.sum.sqr", axes = [0]);
i"tdnn1.renorm.reduced.sum.card.fix-rank-1-1" = [[0.00390625]];
i"tdnn1.renorm.reduced.sum.card" = mul(i"tdnn1.renorm.reduced.sum.sum", i"tdnn1.renorm.reduced.sum.card.fix-rank-1-1");
i"tdnn1.renorm.output-recip" = rsqrt(i"tdnn1.renorm.reduced.sum.card");
i"tdnn1.renorm.reduced.sum.norm.fix-rank-1-1" = [[256.0]];
i"tdnn1.renorm.reduced.sum.norm" = div(i"tdnn1.renorm.reduced.sum.sum", i"tdnn1.renorm.reduced.sum.norm.fix-rank-1-1");
i"tdnn1.renorm.output-recip" = rsqrt(i"tdnn1.renorm.reduced.sum.norm");
i"tdnn1.renorm.output" = mul(i"tdnn1.relu.output.low", i"tdnn1.renorm.output-recip");
i"tdnn2.affine.output.delay" = tract_pulse_delay(i"tdnn1.renorm.output", axis = 1, delay = 0, overlap = 2);
i"tdnn2.affine.output.add_n" = unsqueeze(i"tdnn2.affine.output.delay", axes = [0]);
Expand All @@ -158,9 +158,9 @@ graph network(input) -> (output) {
i"tdnn2.relu.output.low" = max(i"tdnn2.affine.output.rm_n", i"tdnn1.relu.output.low.cst.1");
i"tdnn2.renorm.reduced.sum.sqr" = square(i"tdnn2.relu.output.low");
i"tdnn2.renorm.reduced.sum.sum" = sum_reduce(i"tdnn2.renorm.reduced.sum.sqr", axes = [0]);
i"tdnn2.renorm.reduced.sum.card.fix-rank-1-1" = [[0.00390625]];
i"tdnn2.renorm.reduced.sum.card" = mul(i"tdnn2.renorm.reduced.sum.sum", i"tdnn2.renorm.reduced.sum.card.fix-rank-1-1");
i"tdnn2.renorm.output-recip" = rsqrt(i"tdnn2.renorm.reduced.sum.card");
i"tdnn2.renorm.reduced.sum.norm.fix-rank-1-1" = [[256.0]];
i"tdnn2.renorm.reduced.sum.norm" = div(i"tdnn2.renorm.reduced.sum.sum", i"tdnn2.renorm.reduced.sum.norm.fix-rank-1-1");
i"tdnn2.renorm.output-recip" = rsqrt(i"tdnn2.renorm.reduced.sum.norm");
i"tdnn2.renorm.output" = mul(i"tdnn2.relu.output.low", i"tdnn2.renorm.output-recip");
i"tdnn3.affine.output.add_n" = unsqueeze(i"tdnn2.renorm.output", axes = [0]);
i"tdnn3.affine.kernel.0" = variable<scalar>(label = "tdnn3.affine.kernel.0", shape = [256, 256, 3]);
Expand All @@ -171,9 +171,9 @@ graph network(input) -> (output) {
i"tdnn3.relu.output.low" = max(i"tdnn3.affine.output.rm_n", i"tdnn1.relu.output.low.cst.1");
i"tdnn3.renorm.reduced.sum.sqr" = square(i"tdnn3.relu.output.low");
i"tdnn3.renorm.reduced.sum.sum" = sum_reduce(i"tdnn3.renorm.reduced.sum.sqr", axes = [0]);
i"tdnn3.renorm.reduced.sum.card.fix-rank-1-1" = [[0.00390625]];
i"tdnn3.renorm.reduced.sum.card" = mul(i"tdnn3.renorm.reduced.sum.sum", i"tdnn3.renorm.reduced.sum.card.fix-rank-1-1");
i"tdnn3.renorm.output-recip" = rsqrt(i"tdnn3.renorm.reduced.sum.card");
i"tdnn3.renorm.reduced.sum.norm.fix-rank-1-1" = [[256.0]];
i"tdnn3.renorm.reduced.sum.norm" = div(i"tdnn3.renorm.reduced.sum.sum", i"tdnn3.renorm.reduced.sum.norm.fix-rank-1-1");
i"tdnn3.renorm.output-recip" = rsqrt(i"tdnn3.renorm.reduced.sum.norm");
i"tdnn3.renorm.output" = mul(i"tdnn3.relu.output.low", i"tdnn3.renorm.output-recip");
i"fastlstm1.c_final.extracted.fastlstm1.four_parts.W.concat-einsum-k.0..256.split-over-1.0..256.prop_axis.a.input_1" = variable<scalar>(label = "fastlstm1.c_final.extracted.fastlstm1.four_parts.W.concat-einsum-k.0..256.split-over-1.0..256.prop_axis.a.input_1", shape = [256, 256]);
i"fastlstm1.c_final.extracted.fastlstm1.four_parts.W.concat-einsum-k.0..256.split-over-1.0..256" = matmul(i"tdnn3.renorm.output", i"fastlstm1.c_final.extracted.fastlstm1.four_parts.W.concat-einsum-k.0..256.split-over-1.0..256.prop_axis.a.input_1", transposeA = true, transposeB = false);
Expand Down Expand Up @@ -224,9 +224,9 @@ graph network(input) -> (output) {
i"tdnn4.relu.output.low" = max(i"tdnn4.affine.output.rm_n", i"tdnn1.relu.output.low.cst.2.1");
i"tdnn4.renorm.reduced.sum.sqr" = square(i"tdnn4.relu.output.low");
i"tdnn4.renorm.reduced.sum.sum" = sum_reduce(i"tdnn4.renorm.reduced.sum.sqr", axes = [1]);
i"tdnn4.renorm.reduced.sum.card.fix-rank-1-1" = [[0.00390625]];
i"tdnn4.renorm.reduced.sum.card" = mul(i"tdnn4.renorm.reduced.sum.sum", i"tdnn4.renorm.reduced.sum.card.fix-rank-1-1");
i"tdnn4.renorm.output-recip" = rsqrt(i"tdnn4.renorm.reduced.sum.card");
i"tdnn4.renorm.reduced.sum.norm.fix-rank-1-1" = [[256.0]];
i"tdnn4.renorm.reduced.sum.norm" = div(i"tdnn4.renorm.reduced.sum.sum", i"tdnn4.renorm.reduced.sum.norm.fix-rank-1-1");
i"tdnn4.renorm.output-recip" = rsqrt(i"tdnn4.renorm.reduced.sum.norm");
i"tdnn4.renorm.output" = mul(i"tdnn4.relu.output.low", i"tdnn4.renorm.output-recip");
i"tdnn5.affine.output.delay" = tract_pulse_delay(i"tdnn4.renorm.output", axis = 0, delay = 0, overlap = 2);
i"tdnn5.affine.output.add_n" = unsqueeze(i"tdnn5.affine.output.delay", axes = [0]);
Expand All @@ -239,9 +239,9 @@ graph network(input) -> (output) {
i"tdnn5.relu.output.low" = max(i"tdnn5.affine.output.rm_n", i"tdnn1.relu.output.low.cst.2.1");
i"tdnn5.renorm.reduced.sum.sqr" = square(i"tdnn5.relu.output.low");
i"tdnn5.renorm.reduced.sum.sum" = sum_reduce(i"tdnn5.renorm.reduced.sum.sqr", axes = [1]);
i"tdnn5.renorm.reduced.sum.card.fix-rank-1-1" = [[0.00390625]];
i"tdnn5.renorm.reduced.sum.card" = mul(i"tdnn5.renorm.reduced.sum.sum", i"tdnn5.renorm.reduced.sum.card.fix-rank-1-1");
i"tdnn5.renorm.output-recip" = rsqrt(i"tdnn5.renorm.reduced.sum.card");
i"tdnn5.renorm.reduced.sum.norm.fix-rank-1-1" = [[256.0]];
i"tdnn5.renorm.reduced.sum.norm" = div(i"tdnn5.renorm.reduced.sum.sum", i"tdnn5.renorm.reduced.sum.norm.fix-rank-1-1");
i"tdnn5.renorm.output-recip" = rsqrt(i"tdnn5.renorm.reduced.sum.norm");
i"tdnn5.renorm.output" = mul(i"tdnn5.relu.output.low", i"tdnn5.renorm.output-recip");
i"fastlstm2.c_final.extracted.fastlstm2.four_parts.W.concat-einsum-k.0..256.split-over-1.0..256.prop_axis.a.input_1" = variable<scalar>(label = "fastlstm2.c_final.extracted.fastlstm2.four_parts.W.concat-einsum-k.0..256.split-over-1.0..256.prop_axis.a.input_1", shape = [256, 256]);
i"fastlstm2.c_final.extracted.fastlstm2.four_parts.W.concat-einsum-k.0..256.split-over-1.0..256" = matmul(i"tdnn5.renorm.output", i"fastlstm2.c_final.extracted.fastlstm2.four_parts.W.concat-einsum-k.0..256.split-over-1.0..256.prop_axis.a.input_1", transposeA = false, transposeB = false);
Expand Down

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