added pytorch with jit

Dockerfile

@@ -45,9 +45,6 @@ RUN cmake -G Ninja ../llvm \
   -DLLVM_INSTALL_UTILS=ON && \
   ninja
 
-# Add binaries to PATH
-#  ENV PATH=$HOME/llvm-dcir/usr/local/bin:$PATH
-
 # Build mlir-dace
 WORKDIR $HOME/mlir-dace/build
 

benchmarks/pytorch/mish/pytorch_jit.py

@@ -0,0 +1,78 @@
+#!/usr/bin/python3
+
+# Desc: Runs the Mish benchmark using Pytorch with JIT
+# Usage: python3 pytorch_jit.py <Repetitions> <Test Output (T/F)>
+
+import sys
+import numpy as np
+import torch
+from torch import nn
+import time
+import torch_mlir
+
+from torch_mlir_e2e_test.mhlo_backends.linalg_on_tensors import LinalgOnTensorsMhloBackend
+
+if len(sys.argv) != 3:
+    print("PyTorch(JIT) Mish Benchmarking Tool")
+    print("Arguments:")
+    print("  Repetitions: How many times to run the benchmark")
+    print("  Test Output (T/F): If 'T', tests the output against Torch-MLIR")
+    exit(1)
+
+repetitions = int(sys.argv[1])
+test_output = sys.argv[2] == 'T'
+
+
+# Load model
+class Mish(nn.Module):
+
+    def __init__(self):
+        super().__init__()
+
+    def forward(self, x):
+        x = torch.log(1 + torch.exp(x))
+        return x
+
+
+data = torch.rand(8, 32, 224, 224)
+model = torch.jit.trace(Mish(), data).to(torch.device('cpu'))
+model.eval()
+
+# Benchmark
+for i in range(repetitions):
+    data = torch.rand(8, 32, 224, 224)
+    start = time.time()
+    model.forward(data)
+    runtime = time.time() - start
+    print(runtime * 1000)
+
+# Test output
+if test_output:
+    data = torch.zeros(8, 32, 224, 224)
+
+    for i in range(8):
+        for j in range(32):
+            for k in range(224):
+                for l in range(224):
+                    data[i, j, k, l] = (i + j + k + l) / (8 + 32 + 224 + 224)
+
+    prediction_pytorch = model.forward(data).numpy()
+
+    # Generate MHLO
+    backend = LinalgOnTensorsMhloBackend()
+
+    model_mlir = nn.Sequential(Mish()).to(torch.device('cpu'))
+    model_mlir.eval()
+    module = torch_mlir.compile(model_mlir,
+                                data,
+                                output_type=torch_mlir.OutputType.MHLO)
+
+    compiled = backend.compile(module)
+    jit_module = backend.load(compiled)
+
+    prediction_torch_mlir = jit_module.forward(data.numpy())
+
+    # Compare
+    if not np.allclose(
+            prediction_pytorch, prediction_torch_mlir, rtol=1e-5, atol=1e-8):
+        exit(1)

scripts/pytorch/pytorch_jit.sh

@@ -0,0 +1,76 @@
+#!/bin/bash
+
+# Desc: Runs a pytorch benchmark using Pytorch with JIT. The output contains any
+#       intermediate results and the times in the CSV format
+# Usage: ./pytorch_jit.sh <Benchmark File> <Output Dir> <Repetitions>
+
+# Be safe
+set -e          # Fail script when subcommand fails
+set -u          # Disallow using undefined variables
+set -o pipefail # Prevent errors from being masked
+
+# Check args
+if [ $# -ne 3 ]; then
+  echo "Usage: ./pytorch_jit.sh <Benchmark File> <Output Dir> <Repetitions>"
+  exit 1
+fi
+
+# Read args
+input_file=$1
+output_dir=$2
+repetitions=$3
+
+# Check tools
+check_tool() {
+  if ! command -v "$1" &>/dev/null; then
+    echo "$1 could not be found"
+    exit 1
+  fi
+}
+
+check_tool python3
+
+# Create output directory
+if [ ! -d "$output_dir" ]; then
+  mkdir -p "$output_dir"
+fi
+
+# Silence Python warnings
+export PYTHONWARNINGS="ignore"
+
+# Helpers
+input_dir=$(dirname "$input_file")
+input_name=$(basename "$input_dir")
+input_file=$input_dir/pytorch_jit.py
+timings_file=$output_dir/${input_name}_timings.csv
+touch "$timings_file"
+
+# Adds a value to the timings file, jumps to the next row after a write
+csv_line=1
+add_csv() {
+  while [[ $(grep -c ^ "$timings_file") -lt $csv_line ]]; do
+    echo '' >>"$timings_file"
+  done
+
+  if [ -n "$(sed "${csv_line}q;d" "$timings_file")" ]; then
+    sed -i "${csv_line}s/$/,/" "$timings_file"
+  fi
+
+  sed -i "${csv_line}s/$/$1/" "$timings_file"
+  csv_line=$((csv_line + 1))
+}
+
+# Check output
+if ! python3 "$input_file" 0 T; then
+  echo "Output incorrect!"
+  exit 1
+fi
+
+# Running the benchmark
+runtimes=$(OMP_NUM_THREADS=1 taskset -c 0 python3 "$input_file" "$repetitions" F)
+
+add_csv "PyTorch (JIT)"
+
+for i in $runtimes; do
+  add_csv "$i"
+done

scripts/pytorch/run_all.sh

@@ -37,7 +37,8 @@ benchmarks_dir=$(dirname "$0")/../../benchmarks/pytorch
 benchmarks=$(find "$benchmarks_dir"/* -name 'pytorch.py')
 total=$(echo "$benchmarks" | wc -l)
 
-runners="$runners_dir/pytorch.sh $runners_dir/torch-mlir.sh $runners_dir/dcir.sh"
+runners="$runners_dir/pytorch.sh $runners_dir/pytorch_jit.sh \
+        $runners_dir/torch-mlir.sh $runners_dir/dcir.sh"
 
 for runner in $runners; do
   count=0