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Refactor get valid prompts - for memory optimization #170

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refactor get valid prompts
Ssukriti Nov 14, 2025
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Ssukriti Nov 14, 2025
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rebase on main
Ssukriti Nov 17, 2025
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merge main
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Ssukriti Dec 23, 2025
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Ssukriti Dec 23, 2025
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189 changes: 97 additions & 92 deletions aiu_fms_testing_utils/scripts/drive_paged_programs.py
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Original file line number Diff line number Diff line change
Expand Up @@ -518,112 +518,111 @@ def parse_program_limit(limit_str: str) -> tuple[int, str]:
for v in program_map.values():
random.Random(42).shuffle(v)


# select prompts that fit the batch size criteria
valid_prompts = []
if custom_shape:
for program_criteria_seq, valid_prompt_shapes in program_map.items():
for valid_prompt_shape in valid_prompt_shapes:
if valid_prompt_shape == custom_shape:
enforce_sizes = [valid_prompt_shape[1]]
input_ids, extra_kwargs, sample_key = __prepare_inputs(
valid_prompt_shape[0],
valid_prompt_shape[1],
tokenizer,
enforce_sizes=enforce_sizes,
)
valid_prompts = [
(
def get_program_prompt_list():
if custom_shape:
prompt_found = 0
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@Ssukriti Ssukriti Nov 17, 2025 •
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since its hard to see git diff changes made in this PR:,
change 1 - use prompt_found flag as we are yielding instead of storing in list

for program_criteria_seq, valid_prompt_shapes in program_map.items():
for valid_prompt_shape in valid_prompt_shapes:
if valid_prompt_shape == custom_shape:
enforce_sizes = [valid_prompt_shape[1]]
input_ids, extra_kwargs, sample_key = __prepare_inputs(
valid_prompt_shape[0],
valid_prompt_shape[1],
tokenizer,
enforce_sizes=enforce_sizes,
)
prompt_found = 1
yield (
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change2: yield instead of list, flag set before yield

program_criteria_seq[0].program_id,
custom_shape,
input_ids,
extra_kwargs,
sample_key,
)
]
break
if prompt_found:
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change3: see flag instead of length of list

break
if len(valid_prompts) > 0:
break
else:
for program_info in programs:
program_id = program_info.program_id
batch_size_limit = program_info.batch_size_limit
batch_size_limit_type = program_info.batch_size_limit_type
prompt_length_limit = program_info.prompt_length_limit
prompt_length_limit_type = program_info.prompt_length_limit_type

filtered_program_map = program_map
if program_id.isnumeric():
filtered_program_map = {
k: v
for k, v in program_map.items()
if k[0] == program_criteria_list[int(program_id)]
}
used_keys = set()
# for each program, we need to check if we have a shape that satisfies the --programs request
for program_seq_key, valid_prompt_shapes in filtered_program_map.items():
# if ? or numeric => we need to check if we have found at least one valid key to stop
if (program_id == "?" or program_id.isnumeric()) and len(used_keys) > 0:
break
# if * => we need to see if we have found the first key to see if we should skip
elif program_id == "*" and program_seq_key[0] in used_keys:
continue

for valid_prompt_shape in valid_prompt_shapes:
# make sure the criteria for batch limit and prompt limit is satisfied
# eval is safe here because we have limited what type and limit can be before

batch_check = eval(
f"valid_prompt_shape[0] {batch_size_limit_type} {batch_size_limit}"
)
prompt_check = eval(
f"valid_prompt_shape[1] {prompt_length_limit_type} {prompt_length_limit}"
)
if batch_check and prompt_check:
# when we enforce homogeneous prompt programs, we will cycle through all sizes between the min of a program and the valid prompt sequence length
# if there does not exist enough sequence sizes between this range, we will cycle back to the beginning
# in the event we don't have enough sequences that satisfy the enforce_sizes, we will repeat sequences and warn the user
enforce_sizes = [valid_prompt_shape[1]]
if args.enforce_homogeneous_prompt_programs:
# this will get the number of bits for the sequence length and shift to get the power of 2 that is less than or equal to the sequence length
tkv_cutoff = 1 << (valid_prompt_shape[1].bit_length() - 1)
possible_seq_lengths = [
_ for _ in range(tkv_cutoff, valid_prompt_shape[1], 64)
]
# favor sequences that are close to the valid prompt length
possible_seq_lengths.reverse()
enforce_sizes = enforce_sizes + list(
itertools.islice(
itertools.cycle(possible_seq_lengths),
valid_prompt_shape[0] - 1,
else:
for program_info in programs:
program_id = program_info.program_id
batch_size_limit = program_info.batch_size_limit
batch_size_limit_type = program_info.batch_size_limit_type
prompt_length_limit = program_info.prompt_length_limit
prompt_length_limit_type = program_info.prompt_length_limit_type

filtered_program_map = program_map
if program_id.isnumeric():
filtered_program_map = {
k: v
for k, v in program_map.items()
if k[0] == program_criteria_list[int(program_id)]
}
used_keys = set()
# for each program, we need to check if we have a shape that satisfies the --programs request
for program_seq_key, valid_prompt_shapes in filtered_program_map.items():
# if ? or numeric => we need to check if we have found at least one valid key to stop
if (program_id == "?" or program_id.isnumeric()) and len(used_keys) > 0:
break
# if * => we need to see if we have found the first key to see if we should skip
elif program_id == "*" and program_seq_key[0] in used_keys:
continue

for valid_prompt_shape in valid_prompt_shapes:
# make sure the criteria for batch limit and prompt limit is satisfied
# eval is safe here because we have limited what type and limit can be before

batch_check = eval(
f"valid_prompt_shape[0] {batch_size_limit_type} {batch_size_limit}"
)
prompt_check = eval(
f"valid_prompt_shape[1] {prompt_length_limit_type} {prompt_length_limit}"
)
if batch_check and prompt_check:
# when we enforce homogeneous prompt programs, we will cycle through all sizes between the min of a program and the valid prompt sequence length
# if there does not exist enough sequence sizes between this range, we will cycle back to the beginning
# in the event we don't have enough sequences that satisfy the enforce_sizes, we will repeat sequences and warn the user
enforce_sizes = [valid_prompt_shape[1]]
if args.enforce_homogeneous_prompt_programs:
# this will get the number of bits for the sequence length and shift to get the power of 2 that is less than or equal to the sequence length
tkv_cutoff = 1 << (valid_prompt_shape[1].bit_length() - 1)
possible_seq_lengths = [
_ for _ in range(tkv_cutoff, valid_prompt_shape[1], 64)
]
# favor sequences that are close to the valid prompt length
possible_seq_lengths.reverse()
enforce_sizes = enforce_sizes + list(
itertools.islice(
itertools.cycle(possible_seq_lengths),
valid_prompt_shape[0] - 1,
)
)
try:
input_ids, extra_kwargs, sample_key = __prepare_inputs(
valid_prompt_shape[0],
valid_prompt_shape[1],
tokenizer,
enforce_sizes=enforce_sizes,
)
)
try:
input_ids, extra_kwargs, sample_key = __prepare_inputs(
valid_prompt_shape[0],
valid_prompt_shape[1],
tokenizer,
enforce_sizes=enforce_sizes,
)
valid_prompts.append(
(
used_keys.add(program_seq_key[0])
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change 4: used_keys.add(program_seq_key[0]) before yield and then yield

yield (
program_seq_key[0],
valid_prompt_shape,
input_ids,
extra_kwargs,
sample_key,
)
)
used_keys.add(program_seq_key[0])
break
except ValueError:
dprint(
f"No valid sample exists in dataset for this input shape {valid_prompt_shape}"
)

if len(used_keys) == 0 and local_rank == 0:
dprint(
f"no valid prompt shape was found which would result in program {program_id} that satisfied batch{batch_size_limit_type}{batch_size_limit} and prompt_length{prompt_length_limit_type}{prompt_length_limit}"
)
break
except ValueError:
dprint(
f"No valid sample exists in dataset for this input shape {valid_prompt_shape}"
)

if len(used_keys) == 0 and local_rank == 0:
dprint(
f"no valid prompt shape was found which would result in program {program_id} that satisfied batch{batch_size_limit_type}{batch_size_limit} and prompt_length{prompt_length_limit_type}{prompt_length_limit}"
)


# metric calculator based on the cross-entropy and mean diff for each decode step
Expand All @@ -642,7 +641,13 @@ def __metric_calculator(r: torch.Tensor, t: torch.Tensor):

failed_cases = []
# for each program and valid prompt (batch size, sequence length)
for program_id, valid_prompt, input_ids, extra_kwargs, sample_key in valid_prompts:
for (
program_id,
valid_prompt,
input_ids,
extra_kwargs,
sample_key,
) in get_program_prompt_list():
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change 5: call function to yield instead of list

extra_kwargs["attn_name"] = ATTN_NAME
if (
"granite-3.3-8b-instruct" in model_variant
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