[bug] javacpp -pytorch EmbeddingBagImpl maybe need add one override forward method ,maybe also have bug

[mullerhai]

Hi , @saudet EmbeddingBagImpl also in javacpp can not work ,same logic parameter in python could work. please fix up,thanks

in python

   # an EmbeddingBag module containing 10 tensors of size 3
    embedding_sum = nn.EmbeddingBag(10, 3, mode='sum')
    # a batch of 2 samples of 4 indices each
    input = torch.tensor([1, 2, 4, 5, 4, 3, 2, 9], dtype=torch.long)
    offsets = torch.tensor([0, 4], dtype=torch.long)
    out = embedding_sum(input, offsets)
    print(out)

console

tensor([[ 2.4000, -1.5095, -2.0853],
        [ 2.5163,  0.0308, -1.2409]], grad_fn=<EmbeddingBagBackward0>)

in pure javacpp

 val options = new EmbeddingBagOptions(10, 3)
    options.mode().put(new kSum)

//    val m123 = nn.EmbeddingBag(num_embeddings = val options = new EmbeddingBagOptions(numEmbeddings.toLong, embeddingDim.toLong)10, embedding_dim = 3, mode = "sum") // (5, 7), padding_mode = "reflect")
    val input23 = torch.Tensor(Seq(1, 2, 4, 5, 4, 3, 2, 9)).to(torch.float32) // 16, 10)) //
    val offsets = torch.Tensor(Seq(0, 4))
    val model = EmbeddingBagImpl(options)
    val output = model.forward(input23.native,offsets.native,torch.zeros(input23.size).native)
    println(s"output ${fromNative(output).shape}")

javacpp console

java.lang.RuntimeException: Expected tensor for argument #1 'indices' to have one of the following scalar types: Long, Int; but got CPUFloatType instead (while checking arguments for embedding_bag)
Exception raised from checkScalarTypes at D:\a\javacpp-presets\javacpp-presets\pytorch\cppbuild\windows-x86_64-gpu\pytorch\aten\src\ATen\TensorUtils.cpp:203 (most recent call first):

[saudet]

Like the error says, you need a integer tensor, not a float tensor for this to work

[mullerhai]

Like the error says, you need a integer tensor, not a float tensor for this to work

HI, @saudet ,now all tensor I cast to integer tensor , but can not work

import org.bytedeco.pytorch.global.torch as tch
class EmbeddingBagRawSuite extends munit.FunSuite {
  test("ConvTranspose2d output shapes") {
    val options = new EmbeddingBagOptions(10, 3)
    options.mode().put(new kSum)
    options.embedding_dim().put(3)
    options.num_embeddings().put(10)
    options.norm_type().put(2.0)
    options.include_last_offset().put(false)
    options.scale_grad_by_freq().put(false)
    options.sparse().put(false)
    //    options.max_norm().put(2d)
//    options.padding_idx().put()
//    val m123 = nn.EmbeddingBag(num_embeddings = val options = new EmbeddingBagOptions(numEmbeddings.toLong, embeddingDim.toLong)10, embedding_dim = 3, mode = "sum") // (5, 7), padding_mode = "reflect")

    val input23 = torch.Tensor(Seq(1, 2, 4, 5, 4, 3, 2, 9)).to(torch.int32) // 16, 10)) //
    val offsets = torch.Tensor(Seq(0, 4)).to(torch.int32)
    val pre =torch.zeros(input23.size).to(torch.int32)
//    val offset = tch.new
    val model = EmbeddingBagImpl(options)
    println(s"input type ${input23.native.dtype().name().getString()} offsets ${offsets.native.dtype().name().getString()} pre ${pre.native.dtype().name().getString()}")
    val output = model.forward(input23.native,offsets.native,pre.native)
    println(s"output ${fromNative(output).shape}")

console log

C:\Users\hai71\.jdks\openjdk-23.0.2\bin\java.exe -Didea.test.cyclic.buffer.size=1048576 "-javaagent:C:\Program Files\JetBrains\IntelliJ IDEA Community Edition 2024.3.3\lib\idea_rt.jar=9708:C:\Program Files\JetBrains\IntelliJ IDEA Community Edition 2024.3.3\bin" -javaagent:C:\Users\hai71\AppData\Local\JetBrains\IdeaIC2024.3\captureAgent\debugger-agent.jar -Dkotlinx.coroutines.debug.enable.creation.stack.trace=false -Ddebugger.agent.enable.coroutines=true -Dkotlinx.coroutines.debug.enable.flows.stack.trace=true -Dkotlinx.coroutines.debug.enable.mutable.state.flows.stack.trace=true -Dfile.encoding=UTF-8 -Dsun.stdout.encoding=UTF-8 -Dsun.stderr.encoding=UTF-8 -classpath "C:\Program Files\JetBrains\IntelliJ IDEA Community Edition 2024.3.3\lib\idea_rt.jar;C:\Program Files\JetBrains\IntelliJ IDEA Community Edition 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ns-io\2.11.0\commons-io-2.11.0.jar;C:\Users\hai71\AppData\Local\Coursier\cache\v1\https\repo1.maven.org\maven2\ar\com\hjg\pngj\2.1.0\pngj-2.1.0.jar;C:\Users\hai71\AppData\Local\Coursier\cache\v1\https\repo1.maven.org\maven2\org\apache\commons\commons-lang3\3.12.0\commons-lang3-3.12.0.jar;C:\Users\hai71\AppData\Local\Coursier\cache\v1\https\repo1.maven.org\maven2\org\slf4j\slf4j-api\2.0.6\slf4j-api-2.0.6.jar;C:\Users\hai71\AppData\Local\Coursier\cache\v1\https\repo1.maven.org\maven2\org\scala-sbt\test-interface\1.0\test-interface-1.0.jar;C:\Users\hai71\AppData\Local\Coursier\cache\v1\https\repo1.maven.org\maven2\org\hamcrest\hamcrest-core\1.3\hamcrest-core-1.3.jar;C:\Users\hai71\AppData\Local\Coursier\cache\v1\https\repo1.maven.org\maven2\org\typelevel\cats-kernel_3\2.8.0\cats-kernel_3-2.8.0.jar;C:\Users\hai71\AppData\Local\Coursier\cache\v1\https\repo1.maven.org\maven2\com\twelvemonkeys\common\common-lang\3.9.4\common-lang-3.9.4.jar;C:\Users\hai71\AppData\Local\Coursier\cache\v1\https\repo1.maven.org\maven2\com\twelvemonkeys\common\common-io\3.9.4\common-io-3.9.4.jar;C:\Users\hai71\AppData\Local\Coursier\cache\v1\https\repo1.maven.org\maven2\com\twelvemonkeys\common\common-image\3.9.4\common-image-3.9.4.jar;C:\Users\hai71\AppData\Local\Coursier\cache\v1\https\repo1.maven.org\maven2\com\twelvemonkeys\imageio\imageio-metadata\3.9.4\imageio-metadata-3.9.4.jar;C:\Users\hai71\AppData\Local\Coursier\cache\v1\https\repo1.maven.org\maven2\com\adobe\xmp\xmpcore\6.1.11\xmpcore-6.1.11.jar" com.intellij.rt.junit.JUnitStarter -ideVersion5 -junit4 @w@C:\Users\hai71\AppData\Local\Temp\idea_working_dirs_munit1.tmp @C:\Users\hai71\AppData\Local\Temp\idea_munit1.tmp
Testing started at 22:34 ...
input type int offsets int pre int

java.lang.RuntimeException: Expected tensor for argument #1 'weight' to have the same type as tensor for argument #1 'per_sample_weights'; but type CPUFloatType does not equal CPUIntType (while checking arguments for embedding_bag)
Exception raised from checkSameType at D:\a\javacpp-presets\javacpp-presets\pytorch\cppbuild\windows-x86_64-