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# #!/usr/bin/env python3
# """
# Quick test script to verify MLIR syntax is correct.
# """
# import subprocess
# import tempfile
# from pathlib import Path
# def test_mlir_syntax():
# """Test the corrected MLIR baseline syntax"""
# baseline_mlir = '''
# #map_q = affine_map<(b, h, s1, s2, d) -> (b, h, s1, d)>
# #map_k = affine_map<(b, h, s1, s2, d) -> (b, h, s2, d)>
# #map_scores = affine_map<(b, h, s1, s2, d) -> (b, h, s1, s2)>
# #map_weights = affine_map<(b, h, s1, s2) -> (b, h, s1, s2)>
# #map_v = affine_map<(b, h, s1, s2, d) -> (b, h, s2, d)>
# #map_out = affine_map<(b, h, s1, s2, d) -> (b, h, s1, d)>
# module {
# func.func @baseline_attention(
# %query: tensor<1x8x128x64xf32>,
# %key: tensor<1x8x128x64xf32>,
# %value: tensor<1x8x128x64xf32>
# ) -> tensor<1x8x128x64xf32> {
# %c0 = arith.constant 0.0 : f32
# %cst_scale = arith.constant 0.125 : f32
# // Initialize output tensors
# %scores_init = tensor.empty() : tensor<1x8x128x128xf32>
# %output_init = tensor.empty() : tensor<1x8x128x64xf32>
# // Compute Q @ K^T (scaled dot-product attention)
# %attention_scores = linalg.generic {
# indexing_maps = [#map_q, #map_k, #map_scores],
# iterator_types = ["parallel", "parallel", "parallel", "parallel", "reduction"]
# } ins(%query, %key : tensor<1x8x128x64xf32>, tensor<1x8x128x64xf32>)
# outs(%scores_init : tensor<1x8x128x128xf32>) {
# ^bb0(%q: f32, %k: f32, %acc: f32):
# %prod = arith.mulf %q, %k : f32
# %scaled = arith.mulf %prod, %cst_scale : f32
# %sum = arith.addf %acc, %scaled : f32
# linalg.yield %sum : f32
# } -> tensor<1x8x128x128xf32>
# // Apply attention weights to values
# %attention_output = linalg.generic {
# indexing_maps = [#map_weights, #map_v, #map_out],
# iterator_types = ["parallel", "parallel", "parallel", "reduction", "parallel"]
# } ins(%attention_scores, %value : tensor<1x8x128x128xf32>, tensor<1x8x128x64xf32>)
# outs(%output_init : tensor<1x8x128x64xf32>) {
# ^bb0(%weight: f32, %v: f32, %acc: f32):
# %weighted = arith.mulf %weight, %v : f32
# %sum = arith.addf %acc, %weighted : f32
# linalg.yield %sum : f32
# } -> tensor<1x8x128x64xf32>
# return %attention_output : tensor<1x8x128x64xf32>
# }
# }
# '''
# try:
# # Write MLIR to temporary file
# with tempfile.NamedTemporaryFile(mode='w', suffix='.mlir', delete=False) as f:
# f.write(baseline_mlir)
# temp_file = f.name
# print("🔧 Testing MLIR baseline syntax...")
# # Test basic parsing
# result = subprocess.run([
# "mlir-opt", temp_file
# ], capture_output=True, text=True, timeout=30)
# Path(temp_file).unlink() # Clean up
# if result.returncode == 0:
# print("✅ MLIR baseline syntax is correct!")
# return True
# else:
# print(f"❌ MLIR syntax error: {result.stderr}")
# return False
# except Exception as e:
# print(f"❌ Test error: {e}")
# return False
# def test_tiling_pass():
# """Test the linalg tiling pass syntax"""
# simple_linalg = '''
# #map = affine_map<(d0, d1) -> (d0, d1)>
# module {
# func.func @simple_add(%arg0: tensor<128x64xf32>, %arg1: tensor<128x64xf32>) -> tensor<128x64xf32> {
# %0 = tensor.empty() : tensor<128x64xf32>
# %1 = linalg.generic {
# indexing_maps = [#map, #map, #map],
# iterator_types = ["parallel", "parallel"]
# } ins(%arg0, %arg1 : tensor<128x64xf32>, tensor<128x64xf32>)
# outs(%0 : tensor<128x64xf32>) {
# ^bb0(%in: f32, %in_1: f32, %out: f32):
# %2 = arith.addf %in, %in_1 : f32
# linalg.yield %2 : f32
# } -> tensor<128x64xf32>
# return %1 : tensor<128x64xf32>
# }
# }
# '''
# try:
# # Write MLIR to temporary file
# with tempfile.NamedTemporaryFile(mode='w', suffix='.mlir', delete=False) as f:
# f.write(simple_linalg)
# temp_file = f.name
# print("\n🔧 Testing linalg tiling pass...")
# # Test tiling with our syntax
# pipeline = "builtin.module(linalg-tile{linalg-tile-sizes=32,32},canonicalize,cse)"
# result = subprocess.run([
# "mlir-opt", temp_file, f"--pass-pipeline={pipeline}"
# ], capture_output=True, text=True, timeout=30)
# Path(temp_file).unlink() # Clean up
# if result.returncode == 0:
# print("✅ Linalg tiling pass works!")
# print("Sample output:")
# print(result.stdout[:500] + "..." if len(result.stdout) > 500 else result.stdout)
# return True
# else:
# print(f"❌ Tiling pass error: {result.stderr}")
# return False
# except Exception as e:
# print(f"❌ Test error: {e}")
# return False
# if __name__ == "__main__":
# print("🚀 Testing MLIR Syntax Corrections\n")
# success1 = test_mlir_syntax()
# success2 = test_tiling_pass()
# if success1 and success2:
# print("\n🎉 All MLIR syntax tests passed!")
# print("✅ Ready to run AlphaEvolve evolution")
# else:
# print("\n⚠️ Some tests failed. Check MLIR installation.")
# print("\n📋 If tests passed, run:")
# print("python openevolve-run.py fixed_initial_program.py fixed_evaluator.py --iterations 10")
#!/usr/bin/env python3
"""
Quick test script to verify MLIR syntax is correct.
"""
import subprocess
import tempfile
from pathlib import Path
def test_mlir_syntax():
"""Test the corrected MLIR baseline syntax"""
baseline_mlir = '''
#map_q = affine_map<(b, h, s1, s2, d) -> (b, h, s1, d)>
#map_k = affine_map<(b, h, s1, s2, d) -> (b, h, s2, d)>
#map_scores = affine_map<(b, h, s1, s2, d) -> (b, h, s1, s2)>
#map_weights = affine_map<(b, h, s1, s2) -> (b, h, s1, s2)>
#map_v = affine_map<(b, h, s1, s2, d) -> (b, h, s2, d)>
#map_out = affine_map<(b, h, s1, s2, d) -> (b, h, s1, d)>
module {
func.func @baseline_attention(
%query: tensor<1x8x128x64xf32>,
%key: tensor<1x8x128x64xf32>,
%value: tensor<1x8x128x64xf32>
) -> tensor<1x8x128x64xf32> {
%c0 = arith.constant 0.0 : f32
%cst_scale = arith.constant 0.125 : f32
// Initialize output tensors
%scores_init = tensor.empty() : tensor<1x8x128x128xf32>
%output_init = tensor.empty() : tensor<1x8x128x64xf32>
// Compute Q @ K^T (scaled dot-product attention)
%attention_scores = linalg.generic {
indexing_maps = [#map_q, #map_k, #map_scores],
iterator_types = ["parallel", "parallel", "parallel", "parallel", "reduction"]
} ins(%query, %key : tensor<1x8x128x64xf32>, tensor<1x8x128x64xf32>)
outs(%scores_init : tensor<1x8x128x128xf32>) {
^bb0(%q: f32, %k: f32, %acc: f32):
%prod = arith.mulf %q, %k : f32
%scaled = arith.mulf %prod, %cst_scale : f32
%sum = arith.addf %acc, %scaled : f32
linalg.yield %sum : f32
} -> tensor<1x8x128x128xf32>
// Apply attention weights to values
%attention_output = linalg.generic {
indexing_maps = [#map_weights, #map_v, #map_out],
iterator_types = ["parallel", "parallel", "parallel", "reduction", "parallel"]
} ins(%attention_scores, %value : tensor<1x8x128x128xf32>, tensor<1x8x128x64xf32>)
outs(%output_init : tensor<1x8x128x64xf32>) {
^bb0(%weight: f32, %v: f32, %acc: f32):
%weighted = arith.mulf %weight, %v : f32
%sum = arith.addf %acc, %weighted : f32
linalg.yield %sum : f32
} -> tensor<1x8x128x64xf32>
return %attention_output : tensor<1x8x128x64xf32>
}
}
'''
try:
# Write MLIR to temporary file
with tempfile.NamedTemporaryFile(mode='w', suffix='.mlir', delete=False) as f:
f.write(baseline_mlir)
temp_file = f.name
print("🔧 Testing MLIR baseline syntax...")
# Test basic parsing
result = subprocess.run([
"mlir-opt", temp_file
], capture_output=True, text=True, timeout=30)
Path(temp_file).unlink() # Clean up
if result.returncode == 0:
print("✅ MLIR baseline syntax is correct!")
return True
else:
print(f"❌ MLIR syntax error: {result.stderr}")
return False
except Exception as e:
print(f"❌ Test error: {e}")
return False
def test_tiling_pass():
"""Test the linalg tiling pass syntax"""
simple_linalg = '''
#map = affine_map<(d0, d1) -> (d0, d1)>
module {
func.func @simple_add(%arg0: tensor<128x64xf32>, %arg1: tensor<128x64xf32>) -> tensor<128x64xf32> {
%0 = tensor.empty() : tensor<128x64xf32>
%1 = linalg.generic {
indexing_maps = [#map, #map, #map],
iterator_types = ["parallel", "parallel"]
} ins(%arg0, %arg1 : tensor<128x64xf32>, tensor<128x64xf32>)
outs(%0 : tensor<128x64xf32>) {
^bb0(%in: f32, %in_1: f32, %out: f32):
%2 = arith.addf %in, %in_1 : f32
linalg.yield %2 : f32
} -> tensor<128x64xf32>
return %1 : tensor<128x64xf32>
}
}
'''
try:
# Write MLIR to temporary file
with tempfile.NamedTemporaryFile(mode='w', suffix='.mlir', delete=False) as f:
f.write(simple_linalg)
temp_file = f.name
print("\n🔧 Testing linalg tiling pass...")
# Test tiling with our syntax
pipeline = "builtin.module(linalg-tile,canonicalize,cse)"
result = subprocess.run([
"mlir-opt", temp_file, f"--pass-pipeline={pipeline}"
], capture_output=True, text=True, timeout=30)
Path(temp_file).unlink() # Clean up
if result.returncode == 0:
print("✅ Linalg tiling pass works!")
print("Sample output:")
print(result.stdout[:500] + "..." if len(result.stdout) > 500 else result.stdout)
return True
else:
print(f"❌ Tiling pass error: {result.stderr}")
return False
except Exception as e:
print(f"❌ Test error: {e}")
return False
if __name__ == "__main__":
print("🚀 Testing MLIR Syntax Corrections\n")
success1 = test_mlir_syntax()
success2 = test_tiling_pass()
if success1 and success2:
print("\n🎉 All MLIR syntax tests passed!")
print("✅ Ready to run AlphaEvolve evolution")
else:
print("\n⚠️ Some tests failed. Check MLIR installation.")
print("\n📋 If tests passed, run:")
print("python openevolve-run.py fixed_initial_program.py fixed_evaluator.py --iterations 10") |