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⚡️ Speed up function retrieve_batch_compatibility_of_input_selectors by 30% in PR #1504 (feature/try-to-beat-the-limitation-of-ee-in-terms-of-singular-elements-pushed-into-batch-inputs) #1507

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⚡️ Speed up function retrieve_batch_compatibility_of_input_selectors by 30% in PR #1504 (feature/try-to-beat-the-limitation-of-ee-in-terms-of-singular-elements-pushed-into-batch-inputs) #1507

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18 changes: 15 additions & 3 deletions inference/core/workflows/execution_engine/v1/compiler/graph_constructor.py
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Original file line number Diff line number Diff line change
Expand Up @@ -735,25 +735,31 @@ def denote_data_flow_for_step(
)
)
input_dimensionality_offsets = manifest.get_input_dimensionality_offsets()
print("input_dimensionality_offsets", input_dimensionality_offsets)
verify_step_input_dimensionality_offsets(
step_name=step_name,
input_dimensionality_offsets=input_dimensionality_offsets,
)
print("scalar_parameters_to_be_batched", scalar_parameters_to_be_batched)
inputs_dimensionalities = get_inputs_dimensionalities(
step_name=step_name,
step_type=manifest.type,
input_data=input_data,
scalar_parameters_to_be_batched=scalar_parameters_to_be_batched,
input_dimensionality_offsets=input_dimensionality_offsets,
)
print("inputs_dimensionalities", inputs_dimensionalities)
logger.debug(
f"For step: {node}, detected the following input dimensionalities: {inputs_dimensionalities}"
)
parameters_with_batch_inputs = grab_parameters_defining_batch_inputs(
inputs_dimensionalities=inputs_dimensionalities,
)
print("parameters_with_batch_inputs", parameters_with_batch_inputs)
dimensionality_reference_property = manifest.get_dimensionality_reference_property()
print("dimensionality_reference_property", dimensionality_reference_property)
output_dimensionality_offset = manifest.get_output_dimensionality_offset()
print("output_dimensionality_offset", output_dimensionality_offset)
verify_step_input_dimensionality_offsets(
step_name=step_name,
input_dimensionality_offsets=input_dimensionality_offsets,
Expand Down Expand Up @@ -812,6 +818,8 @@ def denote_data_flow_for_step(
scalar_parameters_to_be_batched=scalar_parameters_to_be_batched,
)
step_node_data.auto_batch_casting_lineage_supports = lineage_supports
print("lineage_supports", lineage_supports)
print("Data lineage of block output", data_lineage)
if data_lineage:
on_top_level_lineage_denoted(data_lineage[0])
step_node_data.data_lineage = data_lineage
Expand Down Expand Up @@ -1563,10 +1571,10 @@ def retrieve_batch_compatibility_of_input_selectors(
) -> Dict[str, Set[bool]]:
batch_compatibility_of_properties = defaultdict(set)
for parsed_selector in input_selectors:
property_name = parsed_selector.definition.property_name
target_set = batch_compatibility_of_properties[property_name]
for reference in parsed_selector.definition.allowed_references:
batch_compatibility_of_properties[
parsed_selector.definition.property_name
].update(reference.points_to_batch)
target_set |= reference.points_to_batch
return batch_compatibility_of_properties


Expand Down Expand Up @@ -1606,6 +1614,9 @@ def verify_declared_batch_compatibility_against_actual_inputs(
)
if batch_compatibility == {True} and False in actual_input_is_batch:
scalar_parameters_to_be_batched.add(property_name)
print(
f"property_name: {property_name}, batch_compatibility={batch_compatibility}, actual_input_is_batch={actual_input_is_batch}, step_accepts_batch_input={step_accepts_batch_input}"
)
return scalar_parameters_to_be_batched


Expand Down Expand Up @@ -1654,6 +1665,7 @@ def get_lineage_support_for_auto_batch_casted_parameters(
casted_dimensionality=parameter_dimensionality,
lineage_support=lineage_support,
)
print("DUMMY", result)
return result


Expand Down