Tensorflow Model to MLIR

[Aruna Kote]

Hello,

I want convert tensorflow model to MLIR .

I saved tensorflow model using model.save() as saved_model.pb.

How can lower the convert model to MLIR (affine/std dialect)?
What are the steps to do so?

Thank You

[Uday Bondhugula]

This thread has information on it:

https://groups.google.com/a/tensorflow.org/g/mlir/c/quu_s4KMzuM/m/Yn1ztQqqBAAJ

[Aruna Kote]

Hi,

    I saved tf model with signature as  predict and I used tf-mlir-translate --savedmodel-objectgraph-to-mlir --tf-savedmodel-exported-names=saved_model /data/aruna/tf_models -o tf_executor.mlir   and then I tired to convert it to HLO using tf-opt -tf-executor-island-coarsening -canonicalize --tf-device-decompose-resource-ops-in-cluster -tf-promote-resources-to-args  --tf-promote-var-handles-to-args --tf-readonly-references-to-resources --tf-resource-device-inference --tf-resource-op-lifting-for-main-function --tf-parallel-execute-to-islands -canonicalize --xla-legalize-tf  tf_executor.mlir >sample.mlir

But I am getting error as :

error: The following operations cannot be legalized: tf.ReadVariableOp (count: 320). These legalization failure(s) may be due to missing TF to HLO lowerings and/or unsupported attributes, etc.
  func private @__inference__wrapped_model_74180(%arg0: tensor<?x224x224x3xf32> {tf._user_specified_name = "input_1"}, %arg1: tensor<!tf.resource>, %arg2: tensor<!tf.resource>, %arg3: tensor<!tf.resource>, %arg4: tensor<!tf.resource>, %arg5: tensor<!tf.resource>, %arg6: tensor<!tf.resource>, %arg7: tensor<!tf.resource>, %arg8: tensor<!tf.resource>, %arg9: tensor<!tf.resource>, %arg10: tensor<!tf.resource>, %arg11: tensor<!tf.resource>, %arg12: tensor<!tf.resource>, %arg13: tensor<!tf.resource>, %arg14: tensor<!tf.resource>, %arg15: tensor<!tf.resource>, %arg16: tensor<!tf.resource>, %arg17: tensor<!tf.resource>, %arg18: tensor<!tf.resource>, %arg19: tensor<!tf.resource>, %arg20: tensor<!tf.resource>, %arg21: tensor<!tf.resource>, %arg22: tensor<!tf.resource>, %arg23: tensor<!tf.resource>, %arg24: tensor<!tf.resource>, %arg25: tensor<!tf.resource>, %arg26: tensor<!tf.resource>, %arg27: tensor<!tf.resource>, %arg28: tensor<!tf.resource>, %arg29: tensor<!tf.resource>, %arg30: tensor<!tf.resource>, %arg31: tensor<!tf.resource>, %arg32: tensor<!tf.resource>, %arg33: tensor<!tf.resource>, %arg34: tensor<!tf.resource>, %arg35: tensor<!tf.resource>, %arg36: tensor<!tf.resource>, %arg37: tensor<!tf.resource>, %arg38: tensor<!tf.resource>, %arg39: tensor<!tf.resource>, %arg40: tensor<!tf.resource>, %arg41: tensor<!tf.resource>, %arg42: tensor<!tf.resource>, %arg43: tensor<!tf.resource>, %arg44: tensor<!tf.resource>, %arg45: tensor<!tf.resource>, %arg46: tensor<!tf.resource>, %arg47: tensor<!tf.resource>, %arg48: tensor<!tf.resource>, %arg49: tensor<!tf.resource>, %arg50: tensor<!tf.resource>, %arg51: tensor<!tf.resource>, %arg52: tensor<!tf.resource>, %arg53: tensor<!tf.resource>, %arg54: tensor<!tf.resource>, %arg55: tensor<!tf.resource>, %arg56: tensor<!tf.resource>, %arg57: tensor<!tf.resource>, %arg58: tensor<!tf.resource>, %arg59: tensor<!tf.resource>, %arg60: tensor<!tf.resource>, %arg61: tensor<!tf.resource>, %arg62: tensor<!tf.resource>, %arg63: 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tensor<!tf.resource>) -> tensor<?x1000xf32> attributes {tf._construction_context = "kEagerRuntime", tf._input_shapes = [#tf.shape<?x224x224x3>, #tf.shape<>, #tf.shape<>, #tf.shape<>, #tf.shape<>, #tf.shape<>, #tf.shape<>, #tf.shape<>, #tf.shape<>, #tf.shape<>, #tf.shape<>, #tf.shape<>, #tf.shape<>, #tf.shape<>, #tf.shape<>, #tf.shape<>, #tf.shape<>, #tf.shape<>, #tf.shape<>, #tf.shape<>, #tf.shape<>, #tf.shape<>, #tf.shape<>, #tf.shape<>, #tf.shape<>, #tf.shape<>, #tf.shape<>, #tf.shape<>, #tf.shape<>, #tf.shape<>, #tf.shape<>, #tf.shape<>, #tf.shape<>, #tf.shape<>, #tf.shape<>, #tf.shape<>, #tf.shape<>, #tf.shape<>, #tf.shape<>, #tf.shape<>, #tf.shape<>, #tf.shape<>, #tf.shape<>, #tf.shape<>, #tf.shape<>, #tf.shape<>, #tf.shape<>, #tf.shape<>, #tf.shape<>, #tf.shape<>, #tf.shape<>, #tf.shape<>, #tf.shape<>, #tf.shape<>, #tf.shape<>, #tf.shape<>, #tf.shape<>, #tf.shape<>, #tf.shape<>, #tf.shape<>, #tf.shape<>, #tf.shape<>, #tf.shape<>, #tf.shape<>, #tf.shape<>, #tf.shape<>, 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  ^
tf_executor.mlir:322:3: error: Emitting more detail about one op that failed to legalize...
  func private @__inference__wrapped_model_74180(%arg0: tensor<?x224x224x3xf32> {tf._user_specified_name = "input_1"}, %arg1: tensor<!tf.resource>, %arg2: tensor<!tf.resource>, %arg3: tensor<!tf.resource>, %arg4: tensor<!tf.resource>, %arg5: tensor<!tf.resource>, %arg6: tensor<!tf.resource>, %arg7: tensor<!tf.resource>, %arg8: tensor<!tf.resource>, %arg9: tensor<!tf.resource>, %arg10: tensor<!tf.resource>, %arg11: tensor<!tf.resource>, %arg12: tensor<!tf.resource>, %arg13: tensor<!tf.resource>, %arg14: tensor<!tf.resource>, %arg15: tensor<!tf.resource>, %arg16: tensor<!tf.resource>, %arg17: tensor<!tf.resource>, %arg18: tensor<!tf.resource>, %arg19: tensor<!tf.resource>, %arg20: tensor<!tf.resource>, %arg21: tensor<!tf.resource>, %arg22: tensor<!tf.resource>, %arg23: tensor<!tf.resource>, %arg24: tensor<!tf.resource>, %arg25: tensor<!tf.resource>, %arg26: tensor<!tf.resource>, %arg27: tensor<!tf.resource>, %arg28: tensor<!tf.resource>, %arg29: tensor<!tf.resource>, %arg30: 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tensor<!tf.resource>) -> tensor<?x1000xf32> attributes {tf._construction_context = "kEagerRuntime", tf._input_shapes = [#tf.shape<?x224x224x3>, #tf.shape<>, #tf.shape<>, #tf.shape<>, #tf.shape<>, #tf.shape<>, #tf.shape<>, #tf.shape<>, #tf.shape<>, #tf.shape<>, #tf.shape<>, #tf.shape<>, #tf.shape<>, #tf.shape<>, #tf.shape<>, #tf.shape<>, #tf.shape<>, #tf.shape<>, #tf.shape<>, #tf.shape<>, #tf.shape<>, #tf.shape<>, #tf.shape<>, #tf.shape<>, #tf.shape<>, #tf.shape<>, #tf.shape<>, #tf.shape<>, #tf.shape<>, #tf.shape<>, #tf.shape<>, #tf.shape<>, #tf.shape<>, #tf.shape<>, #tf.shape<>, #tf.shape<>, #tf.shape<>, #tf.shape<>, #tf.shape<>, #tf.shape<>, #tf.shape<>, #tf.shape<>, #tf.shape<>, #tf.shape<>, #tf.shape<>, #tf.shape<>, #tf.shape<>, #tf.shape<>, #tf.shape<>, #tf.shape<>, #tf.shape<>, #tf.shape<>, #tf.shape<>, #tf.shape<>, #tf.shape<>, #tf.shape<>, #tf.shape<>, #tf.shape<>, #tf.shape<>, #tf.shape<>, #tf.shape<>, #tf.shape<>, #tf.shape<>, #tf.shape<>, #tf.shape<>, #tf.shape<>, 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#tf.shape<>, #tf.shape<>, #tf.shape<>, #tf.shape<>, #tf.shape<>, #tf.shape<>, #tf.shape<>, #tf.shape<>, #tf.shape<>, #tf.shape<>, #tf.shape<>, #tf.shape<>, #tf.shape<>, #tf.shape<>, #tf.shape<>, #tf.shape<>, #tf.shape<>, #tf.shape<>, #tf.shape<>, #tf.shape<>, #tf.shape<>, #tf.shape<>, #tf.shape<>, #tf.shape<>], tf.signature.is_stateful} {
  ^
tf_executor.mlir:789:61: error: 'tf.ReadVariableOp' op is not legalizable
      %outputs_928, %control_929 = tf_executor.island wraps "tf.ReadVariableOp"(%arg270) {device = ""} : (tensor<!tf.resource>) -> tensor<512xf32>
                                                            ^
tf_executor.mlir:789:61: note: see current operation: %1767 = "tf.ReadVariableOp"(%arg270) {device = ""} : (tensor<!tf.resource>) -> tensor<512xf32>
tf_executor.mlir:900:3: error: The following operations cannot be legalized: tf.AssignVariableOp (count: 2); tf.FusedBatchNormV3 (count: 1); tf.ReadVariableOp (count: 4). These legalization failure(s) may be due to missing TF to HLO lowerings and/or unsupported attributes, etc.
  func private @__inference_conv1_bn_layer_call_and_return_conditional_losses_132020(%arg0: tensor<?x112x112x64xf32> {tf._user_specified_name = "inputs"}, %arg1: tensor<!tf.resource>, %arg2: tensor<!tf.resource>, %arg3: tensor<!tf.resource>, %arg4: tensor<!tf.resource>) -> tensor<?x112x112x64xf32> attributes {tf._construction_context = "kEagerRuntime", tf._input_shapes = [#tf.shape<?x112x112x64>, #tf.shape<>, #tf.shape<>, #tf.shape<>, #tf.shape<>], tf.signature.is_stateful} {

Am I giving command line options correctly?

Thanks

[Jacques Pienaar]

@Catherine Payne FYI

Hey,

Seems like you are missing shape inference and so the variable hoisting couldn't happen. Either that or you have a variable which is not uniform and so can't be converted to XLA usage (`%outputs_928, %control_929 = tf_executor.island wraps "tf.ReadVariableOp"(%arg270) {device = ""} : (tensor<!tf.resource>) -> tensor<512xf32>` seems to be missing shape on tf.resource). Run with or look at passes from tf-tpu-bridge (that is first phase of bridge), and then -tf-to-hlo-pipeline (for 2nd phase of bridge - you probably need to run these on different graphs though, as the latter is intended on subgraph to be compiled).

-- Jacques

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