def apply(self, transform_data: TransformData):
# Greedy strategy to merge multi-stages
merged_blocks = [transform_data.blocks[0]]
for candidate in transform_data.blocks[1:]:
merged = merged_blocks[-1]
if self._are_compatible_multi_stages(
merged, candidate, transform_data.has_sequential_axis):
merged.id = transform_data.id_generator.new
self._merge_domain_blocks(merged, candidate)
else:
merged_blocks.append(candidate)
# Greedy strategy to merge stages
# assert transform_data.has_sequential_axis
for block in merged_blocks:
merged_ijs = [block.ij_blocks[0]]
for ij_candidate in block.ij_blocks[1:]:
merged = merged_ijs[-1]
if self._are_compatible_stages(
merged, ij_candidate,
transform_data.min_k_interval_sizes):
merged.id = transform_data.id_generator.new
self._merge_ij_blocks(merged, ij_candidate, transform_data)
else:
merged_ijs.append(ij_candidate)
block.ij_blocks = merged_ijs
transform_data.blocks = merged_blocks
return transform_data
def apply(self, transform_data: TransformData) -> TransformData:
merged_blocks = greedy_merging_with_wrapper(
transform_data.blocks, MultiStageMergingWrapper, parent=transform_data
)
for block in merged_blocks:
block.ij_blocks = greedy_merging_with_wrapper(
block.ij_blocks, StageMergingWrapper, parent=transform_data, parent_block=block
)
transform_data.blocks = merged_blocks
return transform_data
def apply(self, transform_data: TransformData):
# Greedy strategy to merge multi-stages
merged_blocks = [transform_data.blocks[0]]
for candidate in transform_data.blocks[1:]:
merged = merged_blocks[-1]
if self._are_compatible_multi_stages(
merged, candidate, transform_data.has_sequential_axis
):
merged.id = transform_data.id_generator.new
self._merge_blocks(merged, candidate, "ij")
else:
merged_blocks.append(candidate)
# Greedy strategy to merge stages
# assert transform_data.has_sequential_axis
for block in merged_blocks:
merged_ijs = [block.ij_blocks[0]]
for ij_candidate in block.ij_blocks[1:]:
merged = merged_ijs[-1]
if self._are_compatible_stages(
merged, ij_candidate, transform_data.min_k_interval_sizes
):
merged.id = transform_data.id_generator.new
self._merge_blocks(merged, ij_candidate, "interval")
else:
merged_ijs.append(ij_candidate)
block.ij_blocks = merged_ijs
# Greedy strategy to merge apply methods
for block in merged_blocks:
for ij_block in block.ij_blocks:
merged_ints = [ij_block.interval_blocks[0]]
for int_candidate in ij_block.interval_blocks[1:]:
merged_int = merged_ints[-1]
if int_candidate.interval == merged_int.interval:
merged_int.id = transform_data.id_generator.new
merged_int.stmts.append(int_candidate.stmts)
for name, extent in int_candidate.inputs.items():
if name in merged_int.inputs:
merged_int.inputs[name] |= extent
else:
merged_int.inputs[name] = extent
merged_int.outputs |= int_candidate.outputs
else:
merged_ints.append(int_candidate)
ij_block.interval_blocks = merged_ints
transform_data.blocks = merged_blocks
return transform_data
def apply(self, transform_data: TransformData):
zero_extent = Extent.zeros(transform_data.ndims)
blocks = []
for block in transform_data.blocks:
if block.iteration_order == gt_ir.IterationOrder.PARALLEL:
# Put every statement in a single stage
for ij_block in block.ij_blocks:
for interval_block in ij_block.interval_blocks:
for stmt_info in interval_block.stmts:
interval = interval_block.interval
new_interval_block = IntervalBlockInfo(
transform_data.id_generator.new,
interval,
[stmt_info],
stmt_info.inputs,
stmt_info.outputs,
)
new_ij_block = IJBlockInfo(
transform_data.id_generator.new,
{interval},
[new_interval_block],
{**new_interval_block.inputs},
set(new_interval_block.outputs),
compute_extent=zero_extent,
)
new_block = DomainBlockInfo(
transform_data.id_generator.new,
block.iteration_order,
set(new_ij_block.intervals),
[new_ij_block],
{**new_ij_block.inputs},
set(new_ij_block.outputs),
)
blocks.append(new_block)
else:
blocks.append(block)
transform_data.blocks = blocks
return transform_data
def apply(self, transform_data: TransformData):
transform_data.blocks = self.SplitBlocksVisitor().visit(transform_data)
return transform_data
