Update README, LICENSE and add some basic code

Docs and initial script for installing torch-mlir for AMD/NV/Intel

Author: rengolin

LICENSE

@@ -1,10 +1,14 @@
+==============================================================================
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@@ -63,14 +67,14 @@
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@@ -86,7 +90,7 @@
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@@ -127,20 +131,20 @@
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@@ -162,7 +166,7 @@
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@@ -173,9 +177,9 @@
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@@ -186,16 +190,89 @@
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README.md

@@ -1,2 +1,49 @@
-# lighthouse
-MLIR Lighthouse Project
+# MLIR Lighthouse Project
+
+This project implements the RFC: https://discourse.llvm.org/t/rfc-mlir-project-lighthouse/86738
+
+_"In essence, this project should guide you through using MLIR for your own projects, showing the way, but not forcing you to follow a particular path. Essentially, the role of a lighthouse."_
+
+### Disclaimer
+
+This project uses LLVM to test and validate various pipelines and assumptions about the MLIR project, but it is not part of any LLVM releases, nor is a dependency for MLIR to operate.
+
+You should use this project to guide you through MLIR pipelines, schedules and transforms, as well as understanding how to connect ingress frameworks and how to execute the egress on appropriate hardware.
+
+## Project Overview
+
+The project is separated into three parts:
+* **Ingress:** Frameworks that convert source objects (code, models, designs) into MLIR files that the MLIR project can consume.
+* **Scheduler**: The core MLIR component that allows one to choose particular schedules, transforms, dialects to construct pipelines and pass that MLIR ingress through and reach some egress format (LLVM IR, SPIR-V, etc).
+* **Runtime**: Environments that can consume the egress format, install/load the appropriate libraries and tools, and execute the output on some target (hardware, simulator, further tools).
+
+The upstream _lighthouse_ project needs to keep the three parts restricted to upstream / publicly available technology. In essence, public ingress and conversion projects, upstream MLIR dialects and transforms, and public execution engines that can be automatically installed and executed without going through private repositories, license agreement, etc.
+
+A downstream fork of the _lighthouse_ project could extend any and all of the three parts to reach private repositories and tools, execute downstream schedules, load private dialects, etc.
+
+The main purposes of this project, in chronological order, are:
+1. To **test and validate the existing assumptions in the upstream MLIR repository**, by encoding common ingress paths, transform schedules, pipelines, target differentiation and basic execution.
+2. To help MLIR developers **find common patterns on their pipelines**, and propose actions to merge and reuse upstream code for the same purposes.
+3. Once common patterns are detected, to **discuss and agree on dialect design, canonical shapes, and common invariants**, to promote upstream and downstream collaboration on the same grounds.
+4. Build a solid base to guide downstream projects (open or closed source) to **fork this project and build on top of it**, making it easier to separate upstream/downstream parts and make it easier to upstream the delta to MLIR and/or the _lighthouse_.
+5. In time, this could eventually be the **seed for official upstream tooling that uses MLIR in production environments**, like Clang is to LLVM.
+
+### Upstream MLIR / upstream Lighthouse
+
+One key point in the proposal was to not hold _"load bearing"_ code in this repository, but instead, upstream it to MLIR proper and _use_ it here.
+
+It should be fine to have schedule descriptions, aggregation transforms and passes that _use_ the upstream MLIR transforms and passes, but we should _not_ add actual transforms, dialects and passes here to _complement_ the MLIR story.
+
+## Testing Framework
+
+This project should have the same initial purpose as the LLVM Test Suite [https://github.com/llvm/llvm-test-suite], but for MLIR.
+
+### How To Run Tests
+
+#### Choose Your Ingress
+
+#### Choose Your Schedules
+
+#### Choose Your Target
+
+#### Run The Tests

ingress/Torch-MLIR/generate-mlir.py

@@ -0,0 +1,72 @@
+#!/usr/bin/env python3
+
+import argparse
+import os
+import torch
+import torch.nn as nn
+from torch_mlir import fx
+from torch_mlir.fx import OutputType
+
+# Parse arguments for selecting which model to load and which MLIR dialect to generate
+def parse_args():
+    parser = argparse.ArgumentParser(description="Generate MLIR for Torch-MLIR models.")
+    parser.add_argument(
+        "--model",
+        type=str,
+        required=True,
+        help="Path to the Torch model file.",
+    )
+    parser.add_argument(
+        "--dialect",
+        type=str,
+        choices=["torch", "linalg", "stablehlo", "tosa"],
+        default="linalg",
+        help="MLIR dialect to generate.",
+    )
+    return parser.parse_args()
+
+# Functin to load the Torch model
+def load_torch_model(model_path):
+
+    if not os.path.exists(model_path):
+        raise FileNotFoundError(f"Model file {model_path} does not exist.")
+    
+    model = torch.load(model_path)
+    return model
+
+# Function to generate MLIR from the Torch model
+# See: https://github.com/MrSidims/PytorchExplorer/blob/main/backend/server.py#L237
+def generate_mlir(model, dialect):
+
+    # Convert the Torch model to MLIR
+    output_type = None
+    if dialect == "torch":
+        output_type = OutputType.TORCH
+    elif dialect == "linalg":
+        output_type = OutputType.LINALG
+    elif dialect == "stablehlo":
+        output_type = OutputType.STABLEHLO
+    elif dialect == "tosa":
+        output_type = OutputType.TOSA
+    else:
+        raise ValueError(f"Unsupported dialect: {dialect}")
+
+    module = fx.export_and_import(model, "", output_type=output_type)
+    return module
+
+# Main function to execute the script
+def main():
+    args = parse_args()
+    
+    # Load the Torch model
+    model = load_torch_model(args.model)
+    
+    # Generate MLIR from the model
+    mlir_module = generate_mlir(model, args.dialect)
+    
+    # Print or save the MLIR module
+    print(mlir_module)
+
+# Entry point for the script
+if __name__ == "__main__":
+    main()

ingress/Torch-MLIR/generate-mlir.sh

@@ -0,0 +1,42 @@
+#!/usr/bin/env bash
+
+# Command line argument for model to load and MLIR dialect to generate
+while getopts "m:d:" opt; do
+  case $opt in
+    m)
+      MODEL=$OPTARG
+      ;;
+    d)
+      DIALECT=$OPTARG
+      ;;
+    *)
+      echo "Usage: $0 [-m model] [-d dialect]"
+      exit 1
+      ;;
+  esac
+done
+if [ -z "$MODEL" ]; then
+  echo "Model not specified. Please provide a model using -m option."
+  exit 1
+fi
+if [ -z "$DIALECT" ]; then
+  DIALECT="linalg"
+fi
+
+# Enable local virtualenv created by install-virtualenv.sh
+if [ ! -d "torch-mlir-venv" ]; then
+  echo "Virtual environment not found. Please run install-virtualenv.sh first."
+  exit 1
+fi
+source torch-mlir-venv/bin/activate
+
+# Find script directory
+SCRIPT_DIR=$(dirname "$(readlink -f "$0")")
+
+# Use the Python script to generate MLIR
+echo "Generating MLIR for model '$MODEL' with dialect '$DIALECT'..."
+python $SCRIPT_DIR/generate-mlir.py --model "$MODEL" --dialect "$DIALECT"
+if [ $? -ne 0 ]; then
+  echo "Failed to generate MLIR for model '$MODEL'."
+  exit 1
+fi