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Sunny-Anandchentong319tungldMike EssenmacherAlexandreEichenberger
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[Do not merge in main]Fix link and test models. (#2940)
* Change lowering of onnx.IF to Krnl (#2932) * implementation Signed-off-by: chentong319 <[email protected]> * test case change Signed-off-by: chentong319 <[email protected]> * format Signed-off-by: chentong319 <[email protected]> * add test for If back Signed-off-by: chentong319 <[email protected]> * format Signed-off-by: chentong319 <[email protected]> --------- Signed-off-by: chentong319 <[email protected]> Co-authored-by: Tung D. Le <[email protected]> Signed-off-by: Sunny-Anand <[email protected]> * Update c style cast to c++ style cast (#2934) Signed-off-by: Mike Essenmacher <[email protected]> Signed-off-by: Sunny-Anand <[email protected]> * Change c style cast to c++ style cast (#2936) Signed-off-by: Mike Essenmacher <[email protected]> Signed-off-by: Sunny-Anand <[email protected]> * Add coding practices for onnx-mlir (#2935) Signed-off-by: Mike Essenmacher <[email protected]> Signed-off-by: Sunny-Anand <[email protected]> * try to use new buffer deallocation (#2919) * implementation Signed-off-by: Chen Tong <[email protected]> * comments Signed-off-by: Chen Tong <[email protected]> * format Signed-off-by: Chen Tong <[email protected]> --------- Signed-off-by: Chen Tong <[email protected]> Co-authored-by: Tung D. Le <[email protected]> Co-authored-by: Alexandre Eichenberger <[email protected]> Signed-off-by: Sunny-Anand <[email protected]> * fix requirements.txt link Signed-off-by: Sunny-Anand <[email protected]> * Reuse input buffer in lowering to krnl (#2939) * first step Signed-off-by: chentong319 <[email protected]> * cpu Signed-off-by: chentong319 <[email protected]> * options Signed-off-by: chentong319 <[email protected]> * unify Signed-off-by: chentong319 <[email protected]> * simd Signed-off-by: chentong319 <[email protected]> * comments Signed-off-by: chentong319 <[email protected]> * lit test Signed-off-by: chentong319 <[email protected]> * fix test Signed-off-by: chentong319 <[email protected]> * format Signed-off-by: chentong319 <[email protected]> * response Signed-off-by: chentong319 <[email protected]> --------- Signed-off-by: chentong319 <[email protected]> Signed-off-by: Sunny-Anand <[email protected]> * Fix GroupNorm to support Opset21 (#2928) * Group norm for opset 21 * Testing phase * Fix GroupNorm to support Opset21 --------- Signed-off-by: hamptonm1 <[email protected]> Co-authored-by: Megan Hampton <[email protected]> Signed-off-by: Sunny-Anand <[email protected]> * Update Ops documentation for ONNX 1.16.2 (#2942) * Update Ops documentation for ONNX 1.16.2 * Fix format --------- Co-authored-by: Megan Hampton <[email protected]> Signed-off-by: Sunny-Anand <[email protected]> * LLVM/StableHLO Upgrade eaa95a1 (#2943) Co-authored-by: Megan Hampton <[email protected]> Signed-off-by: Sunny-Anand <[email protected]> * added support for no-zero-point quantization (#2938) Signed-off-by: Alexandre Eichenberger <[email protected]> Co-authored-by: Tung D. Le <[email protected]> Signed-off-by: Sunny-Anand <[email protected]> * update with main Signed-off-by: Sunny-Anand <[email protected]> --------- Signed-off-by: chentong319 <[email protected]> Signed-off-by: Sunny-Anand <[email protected]> Signed-off-by: Mike Essenmacher <[email protected]> Signed-off-by: Chen Tong <[email protected]> Signed-off-by: hamptonm1 <[email protected]> Signed-off-by: Alexandre Eichenberger <[email protected]> Signed-off-by: Sunny Anand <[email protected]> Co-authored-by: Tong Chen <[email protected]> Co-authored-by: Tung D. Le <[email protected]> Co-authored-by: Mike Essenmacher <[email protected]> Co-authored-by: Alexandre Eichenberger <[email protected]> Co-authored-by: hamptonm1 <[email protected]> Co-authored-by: Megan Hampton <[email protected]>
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‎docs/BuildOnLinuxOSX.md

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@@ -15,7 +15,7 @@ Firstly, install MLIR (as a part of LLVM-Project):
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``` bash
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git clone -n https://github.com/llvm/llvm-project.git
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# Check out a specific branch that is known to work with ONNX-MLIR.
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cd llvm-project && git checkout f142f8afe21bceb00fb495468aa0b5043e98c419 && cd ..
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cd llvm-project && git checkout eaa95a1c2bd38332c1a4e634595f29d22b28ffea && cd ..
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```
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[same-as-file]: <> (utils/build-mlir.sh)

‎docs/BuildOnWindows.md

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```shell
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git clone -n https://github.com/llvm/llvm-project.git
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# Check out a specific branch that is known to work with ONNX-MLIR.
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cd llvm-project && git checkout f142f8afe21bceb00fb495468aa0b5043e98c419 && cd ..
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cd llvm-project && git checkout eaa95a1c2bd38332c1a4e634595f29d22b28ffea && cd ..
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```
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[same-as-file]: <> (utils/build-mlir.cmd)

‎docs/Dialects/onnx.md

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@@ -3589,6 +3589,63 @@ where the mean and variance are computed per instance per group of channels, and
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groups `num_groups` should be divisible by the number of channels so that there are
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an equal number of channels per group.
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The overall computation has two stages: the first stage normalizes the elements to
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have zero mean and unit variance for each instance in each group, and the second
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stage scales and shifts the results of the first stage. The floating-point precision
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used in the first stage is determined by the `stash_type` attribute. For example,
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if `stash_type` is 1, the operator casts all input variables to 32-bit float,
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performs the computation, and finally casts the normalized results back to the
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original type of `X`. The second stage does not depend on `stash_type`.
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When the number of groups is the same as the number of channels, this operator is
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equivalent to InstanceNormalization. When there is only one group, this operator
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is equivalent to LayerNormalization.
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Traits: `AlwaysSpeculatableImplTrait`
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Interfaces: `ConditionallySpeculatable`, `NoMemoryEffect (MemoryEffectOpInterface)`, `ShapeHelperOpInterface`, `ShapeInferenceOpInterface`
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Effects: `MemoryEffects::Effect{}`
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#### Attributes:
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<table>
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<tr><th>Attribute</th><th>MLIR Type</th><th>Description</th></tr>
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<tr><td><code>epsilon</code></td><td>::mlir::FloatAttr</td><td>32-bit float attribute</td></tr>
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<tr><td><code>num_groups</code></td><td>::mlir::IntegerAttr</td><td>64-bit signed integer attribute</td></tr>
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<tr><td><code>stash_type</code></td><td>::mlir::IntegerAttr</td><td>64-bit signed integer attribute</td></tr>
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</table>
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#### Operands:
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| Operand | Description |
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| :-----: | ----------- |
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| `X` | tensor of bfloat16 type values or tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values
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| `scale` | tensor of bfloat16 type values or tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values
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| `bias` | tensor of bfloat16 type values or tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values
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#### Results:
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| Result | Description |
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| :----: | ----------- |
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| `Y` | tensor of bfloat16 type values or tensor of 16-bit float values or tensor of 32-bit float values or tensor of 64-bit float values
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### `onnx.GroupNormalizationV18` (ONNXGroupNormalizationV18Op)
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_ONNX GroupNormalization operation_
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A GroupNormalization function. Carries out group normalization as described in
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the paper https://arxiv.org/abs/1803.08494
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This operator transforms input according to
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```
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y = scale * (x - mean) / sqrt(variance + epsilon) + bias,
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```
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where the mean and variance are computed per instance per group of channels, and
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`scale` and `bias` should be specified for each group of channels. The number of
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groups `num_groups` should be divisible by the number of channels so that there are
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an equal number of channels per group.
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When the number of groups is the same as the number of channels, this operator is
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equivalent to InstanceNormalization. When there is only one group, this operator
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is equivalent to LayerNormalization.

‎docs/Prerequisite.md

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Java SDK can be installed with distro specific package manager on Linux such as yum on RHEL/Fedora, apt on Debian/Ubuntu, or brew on MacOS. Java SDK is only required if you plan to use the onnx-mlir `--EmitJNI` option to compile a model into a jar file for use in a Java environment. Note that the jar file contains native model runtime library called through JNI so it is not portable across different architectures. To check the java version, run `java --version`.
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All the `PyPi` package dependencies and their appropriate versions are captured in [requirements.txt](requirements.txt).
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All the `PyPi` package dependencies and their appropriate versions are captured in [requirements.txt](https://github.com/onnx/onnx-mlir/blob/main/requirements.txt).

‎docs/SupportedONNXOps-NNPA.md

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# Supported ONNX Operation for Target *NNPA*.
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Onnx-mlir currently supports ONNX operations targeting up to opset 20. Limitations are listed when applicable. This documentation highlights the minimum and maximum opset versions that are fully supported by onnx-mlir and not the version changes.
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Onnx-mlir currently supports ONNX operations targeting up to opset 21. Limitations are listed when applicable. This documentation highlights the minimum and maximum opset versions that are fully supported by onnx-mlir and not the version changes.
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* Operations are defined by the [ONNX Standard](https://github.com/onnx/onnx/blob/main/docs/Operators.md).
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* **Supported Opsets** indicates the lowest and highest opset a model may have for onnx-mlir to support compiling a model with the operator.
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* A * indicates onnx-mlir is compatible with the latest version of that operator available as of opset 20.
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* A * indicates onnx-mlir is compatible with the latest version of that operator available as of opset 21.
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NNPA has hardware limitations in dimension index size and tensor size, which are described in [NNPALimit.hpp](../src/Accelerators/NNPA/Support/NNPALimit.hpp). They are large enough for normal use cases, but if your model exceeds the limitations, CPU is used instead of NNPA.

‎docs/SupportedONNXOps-cpu.md

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# Supported ONNX Operation for Target *cpu*.
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Onnx-mlir currently supports ONNX operations targeting up to opset 20. Limitations are listed when applicable. This documentation highlights the minimum and maximum opset versions that are fully supported by onnx-mlir and not the version changes.
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Onnx-mlir currently supports ONNX operations targeting up to opset 21. Limitations are listed when applicable. This documentation highlights the minimum and maximum opset versions that are fully supported by onnx-mlir and not the version changes.
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* Operations are defined by the [ONNX Standard](https://github.com/onnx/onnx/blob/main/docs/Operators.md).
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* **Supported Opsets** indicates the lowest and highest opset a model may have for onnx-mlir to support compiling a model with the operator.
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* A * indicates onnx-mlir is compatible with the latest version of that operator available as of opset 20.
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* A * indicates onnx-mlir is compatible with the latest version of that operator available as of opset 21.
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| Op |Supported Opsets (inclusive) |Limitations |Notes |
@@ -36,8 +36,8 @@ Onnx-mlir currently supports ONNX operations targeting up to opset 20. Limitatio
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| **BitwiseOr** |18 - * | | |
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| **BitwiseXor** |18 - * | | |
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| **BlackmanWindow** |none | | | |
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| **Cast** |6 - * |Cast only between float and double types. Only ppc64le and MacOS platforms support float16. | |
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| **CastLike** |19 - * |CastLike only between float and double types. Only ppc64le and MacOS platforms support float16. | |
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| **Cast** |6 - * |Cast only between float and double types. Only ppc64le and MacOS platforms support float16. Does not support int4 and uint4. | |
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| **CastLike** |19 - * |CastLike only between float and double types. Only ppc64le and MacOS platforms support float16. Does not support int4 and uint4. | |
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| **CastMap** |none | | | |
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| **CategoryMapper** |none | | | |
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| **Ceil** |6 - * | | |
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| **Compress** |9 - * | | |
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| **Concat** |6 - * | | |
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| **ConcatFromSequence** |none | | | |
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| **Constant** |6 - * | | |
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| **ConstantOfShape** |9 - * | | |
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| **Constant** |6 - * |Does not support int4 and uint4. | |
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| **ConstantOfShape** |9 - * |Does not support int4 and uint4. | |
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| **Conv** |6 - * | | |
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| **ConvInteger** |none | | | |
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| **ConvTranspose** |6 - * |Spatial dimensions (H and W in input `X`, and kH and kW in input `W`) must be static dimension. | |
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| **DFT** |17 - * | | |
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| **DeformConv** |none | | | |
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| **DepthToSpace** |13 - * | | |
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| **DequantizeLinear** |10 - * |Only support for per-tensor or layer dequantization. No support for per-axis dequantization. | |
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| **DequantizeLinear** |10 - * |Only support for per-tensor or layer dequantization. No support for per-axis dequantization. Does not support int4 and uint4. | |
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| **Det** |none | | | |
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| **DictVectorizer** |none | | | |
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| **Div** |6 - * |No support for short integers. | |
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| **Expand** |8 - * |Input `shape` must have static shape. | |
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| **EyeLike** |none | | | |
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| **FeatureVectorizer** |none | | | |
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| **Flatten** |6 - * | | |
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| **Flatten** |6 - * |Does not support int4 and uint4. | |
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| **Floor** |6 - * | | |
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| **GRU** |7 - * |W, B and R must be constants. | |
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| **Gather** |6 - * | | |
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| **HardSigmoid** |6 - * | | |
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| **HardSwish** |none | | | |
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| **Hardmax** |6 - * | | |
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| **Identity** |16 - * |Sequence identity not supported. | |
98-
| **If** |16 - * |Sequence and Optional outputs are not supported. | |
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| **Identity** |16 - * |Sequence identity not supported. Does not support int4 and uint4. | |
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| **If** |16 - * |Sequence and Optional outputs are not supported. Does not support int4 and uint4. | |
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| **Imputer** |none | | | |
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| **InstanceNormalization** |6 - * | | |
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| **IsInf** |20 - * |Currently no support for float16 infinity value. Only for float32 and float64. | |
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| **LinearRegressor** |none | | | |
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| **Log** |6 - * | | |
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| **LogSoftmax** |13 - * |Axis 0, 1, and default currently disabled due to changes in ONNX 1.8.1/Opset 13. |Temporally removed due to changes in onnx 1.8.1. |
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| **Loop** |6 - * |Input must have static shape. | |
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| **Loop** |6 - * |Input must have static shape. Does not support int4 and uint4. | |
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| **LpNormalization** |none | | | |
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| **LpPool** |none | | | |
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| **MatMul** |6 - * | | |
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| **OptionalHasElement** |none | | | |
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| **Or** |7 - * | | |
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| **PRelu** |6 - * | | |
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| **Pad** |6 - * |axes input not supported. | |
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| **Pad** |6 - * |axes input not supported. Does not support int4 and uint4. | |
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| **Pow** |7 - * |No support for power with integer types. | |
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| **QLinearConv** |none | | | |
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| **QLinearMatMul** |none | | | |
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| **QuantizeLinear** |10 - * |Do not support per-axis and i8 quantization. | |
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| **QuantizeLinear** |10 - * |Does not support per-axis and i8 quantization. Does not support int4 and uint4. | |
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| **RNN** |7 - * |W, B and R must be constants. | |
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| **RandomNormal** |none | | | |
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| **RandomNormalLike** |none | | | |
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| **ReduceSum** |6 - * |Default axis and do_not_keep_dim not supported. |Default axis and do_not_keep_dim temporarily removed due to changes in onnx 1.8.1. |
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| **ReduceSumSquare** |13 - * |Default axis and do_not_keep_dim not supported. | |
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| **Relu** |6 - * | | |
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| **Reshape** |6 - * |allowzero not supported. Input `shape` must have static dimension. | |
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| **Reshape** |6 - * |allowzero not supported. Input `shape` must have static dimension. Does not support int4 and uint4. | |
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| **Resize** |10 - * |Missing support for linear, cubic, crop, pytorch_half_pixel, and floor. Attributes antialias, axes and keep_aspect_ratio_policy are not supported. `scales` and `sizes` must have static dimension. | |
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| **ReverseSequence** |10 - * | | |
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| **RoiAlign** |none | | | |
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| **SVMClassifier** |none | | | |
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| **SVMRegressor** |none | | | |
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| **Scaler** |none | | | |
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| **Scan** |8 - * |Does not support dynamic shapes. |Precision issue with newer opset, maybe just unsupported. Dynamic shape?. |
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| **Scan** |8 - * |Does not support dynamic shapes. Does not support int4 and uint4. |Precision issue with newer opset, maybe just unsupported. Dynamic shape?. |
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| **Scatter** |none | | | |
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| **ScatterElements** |11 - * |Does not support duplicate indices. | |
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| **ScatterND** |11 - * |Does not support scatternd add/multiply. | |
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| **SequenceInsert** |11 - * |Does not support unranked sequence element. | |
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| **SequenceLength** |none | | | |
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| **SequenceMap** |none | | | |
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| **Shape** |15 - * |Does not support start and end attributes. | |
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| **Shape** |15 - * |Does not support start and end attributes. Does not support int4 and uint4. | |
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| **Shrink** |none | | | |
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| **Sigmoid** |6 - * | | |
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| **Sign** |9 - * | | |
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| **Sin** |7 - * | | |
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| **Sinh** |9 - * | | |
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| **Size** |13 - * | | |
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| **Size** |13 - * |Does not support int4 and uint4. | |
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| **Slice** |13 - * |Axis must be a constant argument. |Add tests to slices, currently have none. |
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| **Softmax** |6 - * | | |
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| **SoftmaxCrossEntropyLoss** |none | | | |
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| **Split** |6 - * |Does not support static and dynamic shape, zero size splits. |Temporally removed due to changes in onnx 1.8.1. |
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| **SplitToSequence** |none | | | |
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| **Sqrt** |6 - * | | |
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| **Squeeze** |6 - * |Does not support static and dynamic shape. |Temporally removed due to changes in onnx 1.8.1. |
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| **Squeeze** |6 - * |Does not support static and dynamic shape. Does not support int4 and uint4. |Temporally removed due to changes in onnx 1.8.1. |
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| **StringNormalizer** |none | | | |
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| **Sub** |6 - * |Does not support short integers. | |
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| **Sum** |6 - * | | |
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| **ThresholdedRelu** |none | | | |
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| **Tile** |6 - * | | |
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| **TopK** |10 - * |`K`, the number of top elements to retrieve, must have static shape. | |
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| **Transpose** |6 - * | | |
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| **Transpose** |6 - * |Does not support int4 and uint4. | |
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| **TreeEnsembleClassifier** |none | | | |
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| **TreeEnsembleRegressor** |none | | | |
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| **Trilu** |14 - * | | |
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| **Unique** |11 - * | | |
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| **Unsqueeze** |6 - * |Does not support static and dynamic shape. |Temporally removed due to changes in onnx 1.8.1. |
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| **Unsqueeze** |6 - * |Does not support static and dynamic shape. Does not support int4 and uint4. |Temporally removed due to changes in onnx 1.8.1. |
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| **Upsample** |7 - * |Input `X` and `Y` must have static shape. | |
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| **Where** |9 - * | | |
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| **Xor** |7 - * | | |

‎requirements.txt

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lit~=15.0
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# numpy 1.24 deprecates np.object, np.bool, np.float, np.complex, np.str,
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# and np.int which are used heavily in onnx-mlir.
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numpy~=1.22.2, <=1.23.5
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numpy==2.0.1
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onnx==1.16.2
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protobuf==4.21.12
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pytest~=7.2
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pytest-xdist~=3.0
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pytest==8.3.2
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pytest-xdist==3.6.1

‎src/Builder/OpBuildTable.inc

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op_dialect_version_map_["Greater"] = {13};
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op_dialect_version_map_["GreaterOrEqual"] = {16};
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op_dialect_version_map_["GridSample"] = {16};
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op_dialect_version_map_["GroupNormalization"] = {18};
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op_dialect_version_map_["GroupNormalization"] = {21, 18};
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op_dialect_version_map_["HammingWindow"] = {17};
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op_dialect_version_map_["HannWindow"] = {17};
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op_dialect_version_map_["HardSigmoid"] = {6};
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&onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXGridSampleOp>;
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import_handler_map_["GroupNormalization"] =
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import_handler_map_["GroupNormalizationV18"] =
362+
&onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXGroupNormalizationV18Op>;
361363
import_handler_map_["HammingWindow"] =
362364
&onnx_mlir::detail::FrontendGenImpl::buildOperation<mlir::ONNXHammingWindowOp>;
363365
import_handler_map_["HannWindow"] =

‎src/Compiler/CompilerOptions.cpp

Lines changed: 22 additions & 4 deletions
Original file line numberDiff line numberDiff line change
@@ -42,6 +42,8 @@ bool enableONNXHybridPass; // common for both
4242
std::vector<std::string> functionsToDecompose; // common for both
4343
std::string opsForCall; // common for both
4444
bool disableKrnlOpFusion; // common for both
45+
bool disableQuantZeroPoint; // common for both
46+
bool enableKrnlBufferReuse; // common for both
4547
bool disableMemRefPrefetch; // common for both
4648
EmissionTargetType emissionTarget; // onnx-mlir only
4749
bool invokeOnnxVersionConverter; // onnx-mlir only
@@ -194,7 +196,7 @@ static llvm::cl::list<std::string, std::vector<std::string>>
194196
llvm::cl::cat(OnnxMlirCommonOptions));
195197

196198
static llvm::cl::opt<bool, true> enableONNXHybridPassOpt("onnx-hybrid-pass",
197-
llvm::cl::desc("Enable ONNX hybrid pass (default=true)\n"
199+
llvm::cl::desc("Enable ONNX hybrid pass (default=true).\n"
198200
"Set to 'false' if you want to disable ONNX hybrid pass."),
199201
llvm::cl::location(enableONNXHybridPass), llvm::cl::init(true),
200202
llvm::cl::cat(OnnxMlirCommonOptions));
@@ -207,14 +209,31 @@ static llvm::cl::list<std::string, std::vector<std::string>>
207209

208210
static llvm::cl::opt<bool, true> disableKrnlOpFusionOpt(
209211
"disable-krnl-op-fusion",
210-
llvm::cl::desc("disable op fusion in onnx-to-krnl pass (default=false)\n"
212+
llvm::cl::desc("Disable op fusion in onnx-to-krnl pass (default=false).\n"
211213
"Set to 'true' if you want to disable fusion."),
212214
llvm::cl::location(disableKrnlOpFusion), llvm::cl::init(false),
213215
llvm::cl::cat(OnnxMlirCommonOptions));
214216

217+
static llvm::cl::opt<bool, true> disable_quantization_zero_point(
218+
"disable-quantization-zero-point",
219+
llvm::cl::desc(
220+
"Disable the use of zero-point in quantization (default=false).\n"
221+
"Set to 'true' if you want to disable the use of zero-point\n"
222+
"in dyn/static quantization/dequantization."),
223+
llvm::cl::location(disableQuantZeroPoint), llvm::cl::init(false),
224+
llvm::cl::cat(OnnxMlirCommonOptions));
225+
226+
static llvm::cl::opt<bool, true> enableKrnlBufferReuseOpt(
227+
"enable-krnl-buffer-reuse",
228+
llvm::cl::desc("enable buffer reuse within an op in onnx-to-krnl pass"
229+
"(default=false)\n"
230+
"Set to 'true' if you want to enable buffer reuse."),
231+
llvm::cl::location(enableKrnlBufferReuse), llvm::cl::init(false),
232+
llvm::cl::cat(OnnxMlirCommonOptions));
233+
215234
static llvm::cl::opt<bool, true> disableMemRefPrefetchOpt(
216235
"disable-memref-prefetch",
217-
llvm::cl::desc("disable generation of memref.prefetch (default=false)\n"
236+
llvm::cl::desc("Disable generation of memref.prefetch (default=false).\n"
218237
"Set to 'true' if you want to disable prefetch."),
219238
llvm::cl::location(disableMemRefPrefetch), llvm::cl::init(false),
220239
llvm::cl::cat(OnnxMlirCommonOptions));
@@ -1136,7 +1155,6 @@ std::string getLibraryPath() {
11361155
// as lrodataScript.
11371156
std::string getToolPath(
11381157
const std::string &tool, bool flag /*false by default*/) {
1139-
11401158
if (!flag) {
11411159
std::string execDir = llvm::sys::path::parent_path(getExecPath()).str();
11421160
llvm::SmallString<8> toolPath(execDir);

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