Update mlir-aie with simplified installation of python extras

.github/actions/prereqs/action.yaml

@@ -20,5 +20,5 @@ runs:
         python3 -m venv ${{ inputs.env_name }}
         source ${{ inputs.env_name }}/bin/activate
         pip install --upgrade pip
-        env MLIR_PYTHON_EXTRAS_SET_VERSION="0.0.8.3" HOST_MLIR_PYTHON_PACKAGE_PREFIX="aie" pip install -r requirements.txt
+        pip install -r requirements.txt
         echo "Prerequisites installed into ${{ inputs.env_name }}"

.github/actions/test/action.yaml

@@ -30,10 +30,10 @@ runs:
 
         case "${{ inputs.test_suite }}" in
           operators)
-            TEST_PATH="operators/"
+            TEST_PATH="iron/operators/"
             ;;
           applications)
-            TEST_PATH="applications/"
+            TEST_PATH="iron/applications/"
             ;;
           *)
             echo "Invalid test_suite: ${{ inputs.test_suite }}"
@@ -46,4 +46,3 @@ runs:
         else
           pytest -m "not extensive" $TEST_PATH --csv-output=tests_latest.csv ${{ inputs.test_flags }}
         fi
-

README.md

@@ -35,31 +35,31 @@ The IRON Python API for Ryzen™ AI NPUs is described in the following paper:
 
 | Section | Description | Datatype | AIE2 | AIE2P | Status | Design Example |
 |:--------|:------------|:---------|:-----|:------|:-------|:-------------|
-| [Element-wise Add](./aie_kernels/generic/add.cc) | Element-wise addition kernel | bfloat16 | ✓ | ✓ | 🟢 | [operators/elementwise_add/](./operators/elementwise_add/) |
-| [Element-wise Mul](./aie_kernels/generic/mul.cc) | Element-wise multiplication kernel | bfloat16 | ✓ | ✓ | 🟢 | [operators/elementwise_mul/](./operators/elementwise_mul/) |
-| [GEMM](./aie_kernels/aie2p/mm.cc) | General Matrix Multiplication kernel | bfloat16 | ✓ | ✓ | 🟢 | [operators/gemm/](./operators/gemm/) |
-| [GEMV](./aie_kernels/generic/mv.cc) | General Matrix-Vector Multiplication kernel | bfloat16 | ✓ | ✓ | 🟢 | [operators/gemv/](./operators/gemv/) |
-| [GQA](./aie_kernels/aie2p/mha.cc) | Grouped Query Attention kernel (Single pipeline) | bfloat16 | | ✓ | 🟢 | [operators/mha/](./operators/mha/) |
-| [MHA](./aie_kernels/aie2p/mha.cc) | Multi-Head Attention kernel & Grouped Query Attention | bfloat16 | | ✓ | 🟢 | [operators/mha/](./operators/mha/) |
-| [RMSNorm](./aie_kernels/aie2/rms_norm.cc) | RMSNorm kernel | bfloat16 | ✓ | ✓ | 🟢 | [operators/rms_norm/](./operators/rms_norm/) |
-| [RoPE](./aie_kernels/generic/rope.cc) | Rotary Positional Embedding kernel | bfloat16 | ✓ | ✓ | 🟢 | [operators/rope/](./operators/rope/) |
-| [SiLU](./aie_kernels/aie2/silu.cc) | Sigmoid Linear Unit activation kernel | bfloat16 | ✓ | ✓ | 🟢 | [operators/silu/](./operators/silu/) |
-| [Softmax](./aie_kernels/aie2/softmax.cc) | Softmax kernel | bfloat16 | ✓ | ✓ | 🟢 | [operators/softmax/](./operators/softmax/) |
-| [Weighted RMSNorm](./aie_kernels/aie2/rms_norm.cc) | Weighted RMSNorm kernel | bfloat16 | ✓ | ✓ | 🟢 | [operators/rms_norm/](./operators/rms_norm/) |
-| [Copy](./aie_kernels/generic/passThrough.cc) | Copy | bfloat16 | ✓ | ✓ | 🟢 | [operators/mem_copy/](./operators/mem_copy/) |
-| [Transpose](./aie_kernels/generic/transpose.cc) | Transpose | bfloat16 | ✓ | ✓ | 🟢 | [operators/transpose/](./operators/transpose/) |
-| [AXPY](./aie_kernels/generic/axpy.cc) | AXPY | bfloat16 | ✓ | ✓ | 🟢 | [operators/axpy/](./operators/axpy/) |
+| [Element-wise Add](./aie_kernels/generic/add.cc) | Element-wise addition kernel | bfloat16 | ✓ | ✓ | 🟢 | [iron/operators/elementwise_add/](./iron/operators/elementwise_add/) |
+| [Element-wise Mul](./aie_kernels/generic/mul.cc) | Element-wise multiplication kernel | bfloat16 | ✓ | ✓ | 🟢 | [iron/operators/elementwise_mul/](./iron/operators/elementwise_mul/) |
+| [GEMM](./aie_kernels/aie2p/mm.cc) | General Matrix Multiplication kernel | bfloat16 | ✓ | ✓ | 🟢 | [iron/operators/gemm/](./iron/operators/gemm/) |
+| [GEMV](./aie_kernels/generic/mv.cc) | General Matrix-Vector Multiplication kernel | bfloat16 | ✓ | ✓ | 🟢 | [iron/operators/gemv/](./iron/operators/gemv/) |
+| [GQA](./aie_kernels/aie2p/mha.cc) | Grouped Query Attention kernel (Single pipeline) | bfloat16 | | ✓ | 🟢 | [iron/operators/mha/](./iron/operators/mha/) |
+| [MHA](./aie_kernels/aie2p/mha.cc) | Multi-Head Attention kernel & Grouped Query Attention | bfloat16 | | ✓ | 🟢 | [iron/operators/mha/](./iron/operators/mha/) |
+| [RMSNorm](./aie_kernels/aie2/rms_norm.cc) | RMSNorm kernel | bfloat16 | ✓ | ✓ | 🟢 | [iron/operators/rms_norm/](./iron/operators/rms_norm/) |
+| [RoPE](./aie_kernels/generic/rope.cc) | Rotary Positional Embedding kernel | bfloat16 | ✓ | ✓ | 🟢 | [iron/operators/rope/](./iron/operators/rope/) |
+| [SiLU](./aie_kernels/aie2/silu.cc) | Sigmoid Linear Unit activation kernel | bfloat16 | ✓ | ✓ | 🟢 | [iron/operators/silu/](./iron/operators/silu/) |
+| [Softmax](./aie_kernels/aie2/softmax.cc) | Softmax kernel | bfloat16 | ✓ | ✓ | 🟢 | [iron/operators/softmax/](./iron/operators/softmax/) |
+| [Weighted RMSNorm](./aie_kernels/aie2/rms_norm.cc) | Weighted RMSNorm kernel | bfloat16 | ✓ | ✓ | 🟢 | [iron/operators/rms_norm/](./iron/operators/rms_norm/) |
+| [Copy](./aie_kernels/generic/passThrough.cc) | Copy | bfloat16 | ✓ | ✓ | 🟢 | [iron/operators/mem_copy/](./iron/operators/mem_copy/) |
+| [Transpose](./aie_kernels/generic/transpose.cc) | Transpose | bfloat16 | ✓ | ✓ | 🟢 | [iron/operators/transpose/](./iron/operators/transpose/) |
+| [AXPY](./aie_kernels/generic/axpy.cc) | AXPY | bfloat16 | ✓ | ✓ | 🟢 | [iron/operators/axpy/](./iron/operators/axpy/) |
 | [Reduction]() | Reduction | bfloat16 | | | 🟡 |  |
-| [Dequant](./aie_kernels/generic/expand.cc) | Dequant Q4NX from [AWQ](https://github.com/mit-han-lab/llm-awq) to bfloat16 | bfloat16 | ✓ | ✓ | 🟢 | [operators/dequant/](./operators/dequant/) |
-| [RELU](./aie_kernels/aie2/relu.cc) | RELU | bfloat16 | ✓ | ✓ | 🟢 | [operators/relu/](./operators/relu/) |
-| [Leaky RELU](./aie_kernels/aie2p/leaky_relu.cc) (WIP) | Leaky RELU kernel | bfloat16 | | ✓ | ⚪ | [operators/leaky_relu/](./operators/leaky_relu/) |
-| [GELU](./aie_kernels/aie2/gelu.cc) | GELU | bfloat16 | ✓ | ✓ | 🟢 | [operators/gelu/](./operators/gelu/) |
-| [LayerNorm](./aie_kernels/aie2/layer_norm.cc) | LayerNorm | bfloat16 | ✓ | ✓ | 🟢 | [operators/layer_norm/](./operators/layer_norm/) |
+| [Dequant](./aie_kernels/generic/expand.cc) | Dequant Q4NX from [AWQ](https://github.com/mit-han-lab/llm-awq) to bfloat16 | bfloat16 | ✓ | ✓ | 🟢 | [iron/operators/dequant/](./iron/operators/dequant/) |
+| [RELU](./aie_kernels/aie2/relu.cc) | RELU | bfloat16 | ✓ | ✓ | 🟢 | [iron/operators/relu/](./iron/operators/relu/) |
+| [Leaky RELU](./aie_kernels/aie2p/leaky_relu.cc) (WIP) | Leaky RELU kernel | bfloat16 | | ✓ | ⚪ | [iron/operators/leaky_relu/](./iron/operators/leaky_relu/) |
+| [GELU](./aie_kernels/aie2/gelu.cc) | GELU | bfloat16 | ✓ | ✓ | 🟢 | [iron/operators/gelu/](./iron/operators/gelu/) |
+| [LayerNorm](./aie_kernels/aie2/layer_norm.cc) | LayerNorm | bfloat16 | ✓ | ✓ | 🟢 | [iron/operators/layer_norm/](./iron/operators/layer_norm/) |
 | [Convolution]() | Convolution | bfloat16 | | | 🟡 |  |
 | [MaxPool]() | MaxPool | bfloat16 | | | ⚪ |  |
 | [AveragePool]() | AveragePool | bfloat16 | | | ⚪ |  |
-| [Tanh](./aie_kernels/aie2/tanh.cc) | Tanh kernel | bfloat16 | ✓ | ✓ | 🟢 | [operators/tanh/](./operators/tanh/) |
-| [Sigmoid](./aie_kernels/aie2/sigmoid.cc) | Sigmoid kernel | bfloat16 | ✓ | ✓ | 🟢 | [operators/sigmoid/](./operators/sigmoid/) |
+| [Tanh](./aie_kernels/aie2/tanh.cc) | Tanh kernel | bfloat16 | ✓ | ✓ | 🟢 | [iron/operators/tanh/](./iron/operators/tanh/) |
+| [Sigmoid](./aie_kernels/aie2/sigmoid.cc) | Sigmoid kernel | bfloat16 | ✓ | ✓ | 🟢 | [iron/operators/sigmoid/](./iron/operators/sigmoid/) |
 
 > Use this dashboard to quickly check the status of each kernel and locate relevant setup, build, and usage information.
 
@@ -114,17 +114,17 @@ If starting from `Ubuntu 24.04` you may need to update the Linux kernel to 6.11+
 
 1. Install required Python packages (from requirements.txt):
    ```bash
-   MLIR_PYTHON_EXTRAS_SET_VERSION="0.0.8.3" HOST_MLIR_PYTHON_PACKAGE_PREFIX="aie" pip install -r requirements.txt
+   pip install -r requirements.txt
    ```
 
 1. To test your installation, you can try to build and run the example below:
    ```bash
-   ./operators/axpy/test.py
+   ./iron/operators/axpy/test.py
    ```
 
 ### Building/Using & Testing Operators
 
-All available operators can be found in `operators`. These each contain:
+All available operators can be found in `iron/operators`. These each contain:
 
 * `op.py`: The Python operator interface -- an easy access point to integrate operators into your project that prescribes how to compile the operator (build artifacts) and how to call it at runtime (buffer sizes, etc.)
 * `design.py`: The implementation of the operator's NPU code. Often references a kernel in `aie_kernels` for the compute core code and describes the data movement using ObjectFIFOs.
@@ -137,17 +137,17 @@ All available operators can be found in `operators`. These each contain:
 
 To build and test all the operators:
 ``` bash
-pytest operators/ -m "not extensive"
+pytest iron/operators/ -m "not extensive"
 ``` 
 
 To run the extensive test suite:
 ``` bash
-pytest operators/
+pytest iron/operators/
 ```
 
 To run a specific operator's tests:
 ``` bash
-pytest operators/axpy/
+pytest iron/operators/axpy/
 ```
 
 ### Git Hooks (Optional but Recommended)

REUSE.toml

@@ -4,7 +4,7 @@ SPDX-PackageSupplier = "Advanced Micro Devices, Inc."
 SPDX-PackageDownloadLocation = "https://github.com/AARInternal/ironclad"
 
 [[annotations]]
-path = "applications/llama_3.2_1b/prompt.txt"
+path = "iron/applications/llama_3.2_1b/prompt.txt"
 precedence = "closest"
 SPDX-FileCopyrightText = "Public Domain"
 SPDX-License-Identifier = "CC0-1.0"