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lukstafilukstafi
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Broken: padding test, by Claude Opus
Summary by Claude: The test file has been created and added to the dune configuration: - test/einsum/test_conv_padding.ml - Tests conv2d with various combinations of use_padding and stride - Added test stanza to test/einsum/dune - Created empty test/einsum/test_conv_padding.expected The test exposes that the existing padding infrastructure in row.ml (around line 3920) fails when there are multiple constraints on the same Conv_input projection.
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‎test/einsum/dune‎

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(package neural_nets_lib)
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(modules test_einsum_parser)
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(libraries base ocannl stdio einsum_parser))
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(test
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(name test_conv_padding)
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(package neural_nets_lib)
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(deps
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ocannl_config
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(env_var OCANNL_BACKEND))
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(modules test_conv_padding)
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(libraries base ocannl stdio)
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(preprocess
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(pps ppx_here ppx_ocannl)))

‎test/einsum/test_conv_padding.expected‎

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‎test/einsum/test_conv_padding.ml‎

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open! Base
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open! Ocannl
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open! Nn_blocks.DSL_modules
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open Stdio
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let conv2d = Nn_blocks.conv2d
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(** Test that conv2d with use_padding=true preserves spatial dimensions.
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With use_padding=true, the output spatial dimensions should match input/stride.
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For stride=1 and any kernel_size, output should have the same spatial dims as input. *)
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let test_conv2d_padding_preserves_dims () =
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printf "Testing conv2d with use_padding=true preserves dimensions...\n%!";
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Tensor.unsafe_reinitialize ();
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(* Create a simple 5x5 input with 1 channel *)
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let input = TDSL.range_of_shape ~output_dims:[ 5; 5; 1 ] () in
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(* Apply conv2d with kernel_size=3, stride=1, use_padding=true *)
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let%op output = conv2d ~label:["test_conv"] ~kernel_size:3 ~stride:1 ~use_padding:true () input in
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let ctx = Context.auto () in
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Train.set_hosted output.value;
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ignore (Train.forward_once ctx output);
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printf "Input shape: 5x5x1\n%!";
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printf "Kernel size: 3x3\n%!";
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printf "Stride: 1\n%!";
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printf "use_padding: true\n%!";
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printf "Expected output spatial dims: 5x5 (same as input)\n%!";
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Train.printf ~here:[%here] ~with_code:false ~with_grad:false output;
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printf "\n%!"
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(** Test that conv2d with use_padding=false reduces spatial dimensions.
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With use_padding=false, the output spatial dimensions should be reduced by (kernel_size - 1).
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For 5x5 input, kernel_size=3, stride=1: output should be 3x3. *)
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let test_conv2d_no_padding_reduces_dims () =
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printf "Testing conv2d with use_padding=false reduces dimensions...\n%!";
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Tensor.unsafe_reinitialize ();
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(* Create a simple 5x5 input with 1 channel *)
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let input = TDSL.range_of_shape ~output_dims:[ 5; 5; 1 ] () in
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(* Apply conv2d with kernel_size=3, stride=1, use_padding=false *)
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let%op output = conv2d ~label:["test_conv"] ~kernel_size:3 ~stride:1 ~use_padding:false () input in
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let ctx = Context.auto () in
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Train.set_hosted output.value;
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ignore (Train.forward_once ctx output);
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printf "Input shape: 5x5x1\n%!";
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printf "Kernel size: 3x3\n%!";
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printf "Stride: 1\n%!";
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printf "use_padding: false\n%!";
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printf "Expected output spatial dims: 3x3 (reduced by kernel_size-1)\n%!";
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Train.printf ~here:[%here] ~with_code:false ~with_grad:false output;
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printf "\n%!"
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(** Test conv2d with stride=2 and use_padding=true.
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With stride=2 and use_padding=true, output dims should be ceil(input/stride).
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For 6x6 input, stride=2: output should be 3x3. *)
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let test_conv2d_stride_with_padding () =
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printf "Testing conv2d with stride=2 and use_padding=true...\n%!";
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Tensor.unsafe_reinitialize ();
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(* Create a 6x6 input with 1 channel *)
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let input = TDSL.range_of_shape ~output_dims:[ 6; 6; 1 ] () in
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(* Apply conv2d with kernel_size=3, stride=2, use_padding=true *)
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let%op output = conv2d ~label:["test_conv"] ~kernel_size:3 ~stride:2 ~use_padding:true () input in
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let ctx = Context.auto () in
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Train.set_hosted output.value;
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ignore (Train.forward_once ctx output);
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printf "Input shape: 6x6x1\n%!";
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printf "Kernel size: 3x3\n%!";
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printf "Stride: 2\n%!";
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printf "use_padding: true\n%!";
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printf "Expected output spatial dims: 3x3 (input/stride)\n%!";
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Train.printf ~here:[%here] ~with_code:false ~with_grad:false output;
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printf "\n%!"
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(** Test conv2d with stride=2 and use_padding=false.
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With stride=2 and use_padding=false, output dims should be (input - kernel_size + 1) / stride.
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For 6x6 input, kernel_size=3, stride=2: (6-3+1)/2 = 2, so output should be 2x2. *)
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let test_conv2d_stride_without_padding () =
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printf "Testing conv2d with stride=2 and use_padding=false...\n%!";
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Tensor.unsafe_reinitialize ();
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(* Create a 6x6 input with 1 channel *)
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let input = TDSL.range_of_shape ~output_dims:[ 6; 6; 1 ] () in
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(* Apply conv2d with kernel_size=3, stride=2, use_padding=false *)
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let%op output = conv2d ~label:["test_conv"] ~kernel_size:3 ~stride:2 ~use_padding:false () input in
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let ctx = Context.auto () in
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Train.set_hosted output.value;
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ignore (Train.forward_once ctx output);
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printf "Input shape: 6x6x1\n%!";
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printf "Kernel size: 3x3\n%!";
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printf "Stride: 2\n%!";
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printf "use_padding: false\n%!";
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printf "Expected output spatial dims: 2x2 ((input-kernel+1)/stride)\n%!";
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Train.printf ~here:[%here] ~with_code:false ~with_grad:false output;
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printf "\n%!"
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let () =
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test_conv2d_padding_preserves_dims ();
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test_conv2d_no_padding_reduces_dims ();
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test_conv2d_stride_with_padding ();
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test_conv2d_stride_without_padding ();
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printf "All conv padding tests completed!\n%!"

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