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Python wip hacks #481

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97833cd
Simplify transforms by dropping PrintPipeline, SaveIR and PrintIR
nicolasvasilache May 9, 2022
2041f5a
Add SANDBOX_DUMP_MODULE_TO env var to dump MLIR IR + schedule
nicolasvasilache May 9, 2022
aae3c41
WIP
nicolasvasilache May 9, 2022
620a7e3
make the linalg_transform_test.py pass
ftynse May 11, 2022
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3 changes: 3 additions & 0 deletions python/CMakeLists.txt
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Original file line number Diff line number Diff line change
Expand Up @@ -89,6 +89,9 @@ set(_addl_sources)
list(APPEND _addl_sources
IREEDialectsPythonSources
IREEDialectsPythonExtensions
# TODO: Properly auto-generate dialect extensions with Tablegen and avoid
# manual exporting.
${TmpTransformDialect}
)

################################################################################
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98 changes: 48 additions & 50 deletions python/examples/conv/conv_1d_bench.py
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Original file line number Diff line number Diff line change
Expand Up @@ -27,56 +27,54 @@


def all_experts(fun_name):
return [
# Note: `\` char at the end of next line prevents formatter reflows, keep it.
e.print_ir(after_all=False, llvm=False) for e in [ \
Tile(fun_name,
op_name,
# N W C KW F
tile_sizes=[1, 8, 32, 1, 8],
peel=[0, 1, 2, 3, 4])
.then(Vectorize(fun_name, ''))
.then(LoweringOnlyExpert('', '')),
Tile(fun_name,
op_name,
# N W C KW F
tile_sizes=[1, 8, 32, 1, 8])
.then(Pad(fun_name,
op_name,
padding_values=[0.0, 0.0, 0.0],
padding_dimensions=[0, 1, 2, 3, 4],
pack_paddings=[1, 1, 0],
hoist_paddings=[3, 0, 0]))
.then(Vectorize(fun_name, ''))
.then(LoweringOnlyExpert('', '')),
Tile(fun_name,
op_name,
# N W C KW F
tile_sizes=[1, 32, 128, 3, 32])
.then(Tile(fun_name,
op_name,
# N W C KW F
tile_sizes=[1, 8, 32, 1, 8],
peel=[0, 1, 2, 3, 4]))
.then(Vectorize(fun_name, ''))
.then(LoweringOnlyExpert('', '')),
Tile(fun_name,
op_name,
# N W C KW F
tile_sizes=[1, 32, 128, 3, 32])
.then(Tile(fun_name,
op_name,
# N W C KW F
tile_sizes=[1, 8, 32, 1, 8]))
.then(Pad(fun_name,
op_name,
padding_values=[0.0, 0.0, 0.0],
padding_dimensions=[0, 1, 2, 3, 4],
pack_paddings=[1, 1, 0],
hoist_paddings=[3, 0, 0]))
.then(Vectorize(fun_name, ''))
.then(LoweringOnlyExpert('', '')),
]
# Note: `\` char at the end of next line prevents formatter reflows, keep it.
return [ \
Tile(fun_name,
op_name,
# N W C KW F
tile_sizes=[1, 8, 32, 1, 8],
peel=[0, 1, 2, 3, 4])
.then(Vectorize(fun_name, ''))
.then(LoweringOnlyExpert('', '')),
Tile(fun_name,
op_name,
# N W C KW F
tile_sizes=[1, 8, 32, 1, 8])
.then(Pad(fun_name,
op_name,
padding_values=[0.0, 0.0, 0.0],
padding_dimensions=[0, 1, 2, 3, 4],
pack_paddings=[1, 1, 0],
hoist_paddings=[3, 0, 0]))
.then(Vectorize(fun_name, ''))
.then(LoweringOnlyExpert('', '')),
Tile(fun_name,
op_name,
# N W C KW F
tile_sizes=[1, 32, 128, 3, 32])
.then(Tile(fun_name,
op_name,
# N W C KW F
tile_sizes=[1, 8, 32, 1, 8],
peel=[0, 1, 2, 3, 4]))
.then(Vectorize(fun_name, ''))
.then(LoweringOnlyExpert('', '')),
Tile(fun_name,
op_name,
# N W C KW F
tile_sizes=[1, 32, 128, 3, 32])
.then(Tile(fun_name,
op_name,
# N W C KW F
tile_sizes=[1, 8, 32, 1, 8]))
.then(Pad(fun_name,
op_name,
padding_values=[0.0, 0.0, 0.0],
padding_dimensions=[0, 1, 2, 3, 4],
pack_paddings=[1, 1, 0],
hoist_paddings=[3, 0, 0]))
.then(Vectorize(fun_name, ''))
.then(LoweringOnlyExpert('', '')),
]


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109 changes: 53 additions & 56 deletions python/examples/conv/conv_2d_bench.py
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Original file line number Diff line number Diff line change
Expand Up @@ -26,62 +26,59 @@
"DoubleTile3DPad",
]

all_experts = [
# Note: `\` char at the end of next line prevents formatter reflows, keep it.
e.print_ir(after_all=False, at_begin=False, llvm=False) for e in [ \
Tile(fun_name=fun_name,
op_name=op_name,
# N H W C KH KW F
tile_sizes=[1, 1, 8, 32, 1, 1, 8],
peel=[0, 1, 2, 3, 4, 5, 6])
.then(DecomposeToLowerDimensionalNamedOp())
.then(Vectorize(fun_name, ''))
.then(LoweringOnlyExpert('', '', transpose_lowering='shuffle')),
Tile(fun_name=fun_name,
op_name=op_name,
# N H W C KH KW F
tile_sizes=[1, 1, 8, 32, 1, 1, 8])
.then(Pad(fun_name=fun_name,
op_name=op_name,
padding_values=[0.0, 0.0, 0.0],
padding_dimensions=[0, 1, 2, 3, 4, 5, 6],
hoist_paddings=[5, 0, 0]))
.then(DecomposeToLowerDimensionalNamedOp())
.then(Vectorize(fun_name, ''))
.then(LoweringOnlyExpert('', '', transpose_lowering='shuffle')),
Tile(fun_name,
op_name,
# N H W C KH KW F
tile_sizes=[1, 32, 32, 32, 1, 3, 64],
peel=[0, 1, 2, 3, 4, 5, 6])
.then(Tile(fun_name,
op_name,
tile_sizes=[1, 1, 8, 32, 1, 1, 8],
peel=[0, 1, 2, 3, 4, 5, 6]))
.then(DecomposeToLowerDimensionalNamedOp())
.then(Vectorize(fun_name, ''))
.then(LoweringOnlyExpert('', '')),
Tile(fun_name,
op_name,
# N H W C KH KW F
tile_sizes=[1, 32, 32, 32, 3, 3, 64])
.then(Tile(fun_name,
op_name,
tile_sizes=[1, 1, 8, 32, 1, 1, 8]))
.then(Pad(fun_name,
op_name,
padding_values=[0.0, 0.0, 0.0],
padding_dimensions=[0, 1, 2, 3, 4, 5, 6],
pack_paddings=[1, 0, 0],
hoist_paddings=[4, 0, 0]))
.then(DecomposeToLowerDimensionalNamedOp())
.then(Vectorize(fun_name, ''))
.then(LoweringOnlyExpert(fun_name,
op_name,
split_transfers='none',
transpose_lowering='shuffle',
unroll_vector_transfers=False)),
]
all_experts = [ \
Tile(fun_name=fun_name,
op_name=op_name,
# N H W C KH KW F
tile_sizes=[1, 1, 8, 32, 1, 1, 8],
peel=[0, 1, 2, 3, 4, 5, 6])
.then(DecomposeToLowerDimensionalNamedOp())
.then(Vectorize(fun_name, ''))
.then(LoweringOnlyExpert('', '', transpose_lowering='shuffle')),
Tile(fun_name=fun_name,
op_name=op_name,
# N H W C KH KW F
tile_sizes=[1, 1, 8, 32, 1, 1, 8])
.then(Pad(fun_name=fun_name,
op_name=op_name,
padding_values=[0.0, 0.0, 0.0],
padding_dimensions=[0, 1, 2, 3, 4, 5, 6],
hoist_paddings=[5, 0, 0]))
.then(DecomposeToLowerDimensionalNamedOp())
.then(Vectorize(fun_name, ''))
.then(LoweringOnlyExpert('', '', transpose_lowering='shuffle')),
Tile(fun_name,
op_name,
# N H W C KH KW F
tile_sizes=[1, 32, 32, 32, 1, 3, 64],
peel=[0, 1, 2, 3, 4, 5, 6])
.then(Tile(fun_name,
op_name,
tile_sizes=[1, 1, 8, 32, 1, 1, 8],
peel=[0, 1, 2, 3, 4, 5, 6]))
.then(DecomposeToLowerDimensionalNamedOp())
.then(Vectorize(fun_name, ''))
.then(LoweringOnlyExpert('', '')),
Tile(fun_name,
op_name,
# N H W C KH KW F
tile_sizes=[1, 32, 32, 32, 3, 3, 64])
.then(Tile(fun_name,
op_name,
tile_sizes=[1, 1, 8, 32, 1, 1, 8]))
.then(Pad(fun_name,
op_name,
padding_values=[0.0, 0.0, 0.0],
padding_dimensions=[0, 1, 2, 3, 4, 5, 6],
pack_paddings=[1, 0, 0],
hoist_paddings=[4, 0, 0]))
.then(DecomposeToLowerDimensionalNamedOp())
.then(Vectorize(fun_name, ''))
.then(LoweringOnlyExpert(fun_name,
op_name,
split_transfers='none',
transpose_lowering='shuffle',
unroll_vector_transfers=False)),
]

################################################################################
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18 changes: 8 additions & 10 deletions python/examples/conv/conv_3d_bench.py
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Original file line number Diff line number Diff line change
Expand Up @@ -22,16 +22,14 @@
"Conv3DExpert"
]

all_experts = [
# Note: `\` char at the end of next line prevents formatter reflows, keep it.
e.print_ir(after_all=False) for e in [ \
Tile(fun_name=fun_name,
op_name=op_name,
# N D, H W C KH KW F
tile_sizes=[1, 2, 1, 8, 32, 1, 1, 8])
.then(Vectorize(fun_name, ''))
.then(LoweringOnlyExpert(fun_name, op_name))
]
# Note: `\` char at the end of next line prevents formatter reflows, keep it.
all_experts = [ \
Tile(fun_name=fun_name,
op_name=op_name,
# N D, H W C KH KW F
tile_sizes=[1, 2, 1, 8, 32, 1, 1, 8])
.then(Vectorize(fun_name, ''))
.then(LoweringOnlyExpert(fun_name, op_name))
]

################################################################################
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24 changes: 11 additions & 13 deletions python/examples/copy/copy_1d_bench.py
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Original file line number Diff line number Diff line change
Expand Up @@ -19,19 +19,17 @@
### Compilation strategies.
################################################################################

all_experts = [
# Note: `\` char at the end of next line prevents formatter reflows, keep it.
e.print_ir(after_all=False, at_begin=False, llvm=False) for e in [ \
Tile(fun_name=fun_name,
op_name=op_name,
tile_sizes=[16],
peel=[0])
.then(Bufferize())
.then(Vectorize(fun_name=fun_name, op_name=''))
.then(LowerVectors())
.then(LowerToLLVM())
]
]
# Note: `\` char at the end of next line prevents formatter reflows, keep it.
all_experts = [ \
Tile(fun_name=fun_name,
op_name=op_name,
tile_sizes=[16],
peel=[0])
.then(Bufferize())
.then(Vectorize(fun_name=fun_name, op_name=''))
.then(LowerVectors())
.then(LowerToLLVM())
]

################################################################################
### Problem instantiations.
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4 changes: 1 addition & 3 deletions python/examples/copy/copy_2d_bench.py
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Original file line number Diff line number Diff line change
Expand Up @@ -62,9 +62,7 @@ def all_experts(problem_sizes: List[int]):
.then(LowerVectors())
.then(LowerToLLVM())
)
return [
e.print_ir(after_all=False, at_begin=False, llvm=False) for e in results
]
return results


################################################################################
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20 changes: 9 additions & 11 deletions python/examples/copy/custom_copy_2d_bench.py
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Original file line number Diff line number Diff line change
Expand Up @@ -35,17 +35,15 @@ def all_experts(fun_name: str, problem_sizes: List[int]):
# linalg.generic)
# We want to make more these copies more efficient.
# In the case of a single copy benchmark it is the one true thing to optimize.
return [
# Note: `\` char at the end of next line prevents formatter reflows, keep it.
e.print_ir(after_all=False, at_begin=False, llvm=False) for e in [ \
Tile(fun_name=fun_name,
op_name=op_name,
tile_sizes=sizes2)
.then(Bufferize())
.then(Vectorize(fun_name=fun_name, op_name=''))
.then(LowerVectors())
.then(LowerToLLVM())
]
# Note: `\` char at the end of next line prevents formatter reflows, keep it.
return [ \
Tile(fun_name=fun_name,
op_name=op_name,
tile_sizes=sizes2)
.then(Bufferize())
.then(Vectorize(fun_name=fun_name, op_name=''))
.then(LowerVectors())
.then(LowerToLLVM())
]


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64 changes: 31 additions & 33 deletions python/examples/depthwise_conv/depthwise_conv_1d_bench.py
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Original file line number Diff line number Diff line change
Expand Up @@ -23,39 +23,37 @@
"SingleTiling3D8x16x3Peel", \
]

all_experts = [
# Note: `\` char at the end of next line prevents formatter reflows, keep it.
e.print_ir(after_all=False, at_begin=False, llvm=False) for e in [ \
Tile(fun_name=fun_name,
op_name=op_name,
# N W C KW
tile_sizes=[1, 4, 16, 3],
peel=[0,1,2,3])
.then(Vectorize(fun_name, ''))
.then(LoweringOnlyExpert(fun_name, op_name)),
Tile(fun_name=fun_name,
op_name=op_name,
# N W C KW
tile_sizes=[1, 5, 16, 3],
peel=[0,1,2,3])
.then(Vectorize(fun_name, ''))
.then(LoweringOnlyExpert(fun_name, op_name)),
Tile(fun_name=fun_name,
op_name=op_name,
# N W C KW
tile_sizes=[1, 6, 16, 3],
peel=[0,1,2,3])
.then(Vectorize(fun_name, ''))
.then(LoweringOnlyExpert(fun_name, op_name)),
Tile(fun_name=fun_name,
op_name=op_name,
# N W C KW
tile_sizes=[1, 8, 16, 3],
peel=[0,1,2,3])
.then(Vectorize(fun_name, ''))
.then(LoweringOnlyExpert(fun_name, op_name)),
]
]
# Note: `\` char at the end of next line prevents formatter reflows, keep it.
all_experts = [ \
Tile(fun_name=fun_name,
op_name=op_name,
# N W C KW
tile_sizes=[1, 4, 16, 3],
peel=[0,1,2,3])
.then(Vectorize(fun_name, ''))
.then(LoweringOnlyExpert(fun_name, op_name)),
Tile(fun_name=fun_name,
op_name=op_name,
# N W C KW
tile_sizes=[1, 5, 16, 3],
peel=[0,1,2,3])
.then(Vectorize(fun_name, ''))
.then(LoweringOnlyExpert(fun_name, op_name)),
Tile(fun_name=fun_name,
op_name=op_name,
# N W C KW
tile_sizes=[1, 6, 16, 3],
peel=[0,1,2,3])
.then(Vectorize(fun_name, ''))
.then(LoweringOnlyExpert(fun_name, op_name)),
Tile(fun_name=fun_name,
op_name=op_name,
# N W C KW
tile_sizes=[1, 8, 16, 3],
peel=[0,1,2,3])
.then(Vectorize(fun_name, ''))
.then(LoweringOnlyExpert(fun_name, op_name)),
]

################################################################################
# Problem instantiation
Expand Down
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