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from collections import defaultdict
from src.execution_model import OverlappedOperation, Schedule, ScheduleConfig
def generate_1f1b_schedule(config: ScheduleConfig):
schedule = Schedule(config)
assert config.num_devices == config.num_stages, "num_devices must be equal to num_stages for 1F1B"
for i in range(config.num_devices):
fwd_batch_id = 0
bwd_batch_id = 0
cooldown_batches = warmup_batches = config.num_devices - i - 1
steady_batches = config.num_batches - warmup_batches
for _ in range(warmup_batches):
schedule.device_queues[i].add_operation(
schedule.get_op(fwd_batch_id, i, "forward")
)
fwd_batch_id += 1
for _ in range(steady_batches):
schedule.device_queues[i].add_operation(
schedule.get_op(fwd_batch_id, i, "forward")
)
fwd_batch_id += 1
schedule.device_queues[i].add_operation(
schedule.get_op(bwd_batch_id, i, "backward")
)
bwd_batch_id += 1
for _ in range(cooldown_batches):
schedule.device_queues[i].add_operation(
schedule.get_op(bwd_batch_id, i, "backward")
)
bwd_batch_id += 1
return schedule
def generate_zero_bubble_1p_schedule(config: ScheduleConfig):
# Create a new schedule with split_backward=True to support backward_D and backward_W operations
schedule = Schedule(config)
total_batches = config.num_batches
assert config.num_devices == config.num_stages, "num_devices must be equal to num_stages for ZB-1P"
for i in range(config.num_devices):
fwd_batch_id = 0
bwd_d_batch_id = 0
bwd_w_batch_id = 0
cooldown_batches = warmup_batches = config.num_devices - i - 1
steady_batches = total_batches - warmup_batches
for _ in range(warmup_batches):
schedule.device_queues[i].add_operation(
schedule.get_op(fwd_batch_id, i, "forward")
)
fwd_batch_id += 1
for _ in range(steady_batches):
schedule.device_queues[i].add_operation(
schedule.get_op(fwd_batch_id, i, "forward")
)
schedule.device_queues[i].add_operation(
schedule.get_op(bwd_d_batch_id, i, "backward_D")
)
if fwd_batch_id - bwd_w_batch_id >= config.num_devices - 1:
schedule.device_queues[i].add_operation(
schedule.get_op(bwd_w_batch_id, i, "backward_W")
)
bwd_w_batch_id += 1
bwd_d_batch_id += 1
fwd_batch_id += 1
for _ in range(cooldown_batches):
schedule.device_queues[i].add_operation(
schedule.get_op(bwd_d_batch_id, i, "backward_D")
)
schedule.device_queues[i].add_operation(
schedule.get_op(bwd_w_batch_id, i, "backward_W")
)
bwd_w_batch_id += 1
bwd_d_batch_id += 1
while bwd_w_batch_id < total_batches:
schedule.device_queues[i].add_operation(
schedule.get_op(bwd_w_batch_id, i, "backward_W")
)
bwd_w_batch_id += 1
return schedule
def generate_1f1b_overlap_schedule(config: ScheduleConfig):
schedule = Schedule(config)
assert config.num_devices == config.num_stages, "num_devices must be equal to num_stages for 1F1B"
for i in range(config.num_devices):
fwd_batch_id = 0
bwd_batch_id = 0
cooldown_batches = warmup_batches = 2 * (config.num_devices - i - 1) + 1
steady_batches = config.num_batches - warmup_batches
for _ in range(warmup_batches):
schedule.device_queues[i].add_operation(
schedule.get_op(fwd_batch_id, i, "forward")
)
fwd_batch_id += 1
for _ in range(steady_batches):
fwd_op = schedule.get_op(fwd_batch_id, i, "forward")
bwd_op = schedule.get_op(bwd_batch_id, i, "backward")
overlapped_op = OverlappedOperation([fwd_op, bwd_op])
schedule.register_overlapped_operation(overlapped_op)
schedule.device_queues[i].add_operation(overlapped_op)
fwd_batch_id += 1
bwd_batch_id += 1
for _ in range(cooldown_batches):
schedule.device_queues[i].add_operation(
schedule.get_op(bwd_batch_id, i, "backward")
)
bwd_batch_id += 1
return schedule
def _get_pp_rank_microbatches(
num_microbatches,
num_devices,
device_id,
num_stages_per_device,
microbatch_group_size_per_vp_stage,
):
"""Get the number of total, warmup, and remaining microbatches in PP scheduling."""
total_num_microbatches = num_microbatches * num_stages_per_device
if num_devices > 1:
# Run (num_model_chunks-1)*microbatch_group_size_per_vp_stage on
# all workers, followed by more microbatches after depending on
# stage ID (more forward passes for earlier stages, later stages can
# immediately start with 1F1B).
num_warmup_microbatches = (num_devices - device_id - 1) * 2
num_warmup_microbatches += (num_stages_per_device - 1) * microbatch_group_size_per_vp_stage
else:
# forward_backward_no_pipelining
num_warmup_microbatches = 1
if num_warmup_microbatches >= total_num_microbatches:
num_warmup_microbatches = total_num_microbatches
return num_warmup_microbatches
def _get_schedule_table(num_microbatches, num_model_chunks, microbatch_group_size_per_vp_stage):
"""Get the schedule table for PP scheduling.
Create a tunable schedule lookup table.
The schedule lookup table uses the virtual_microbatch_id to find the corresponding microbatch_id and model_chunk_id.
For example, the tunable schedule table for PP2 N3M5 with VP2 is constructed as below:
virtual_microbatch_id | 0 1 2 3 4 5 6 7 8 9
microbatch_id | 0 1 2 0 1 2 3 4 3 4
model_chunk_id | 0 0 0 1 1 1 0 0 1 1
"""
schedule_table = []
for min_microbatch_id_in_group in range(
0, num_microbatches, microbatch_group_size_per_vp_stage
):
if min_microbatch_id_in_group + microbatch_group_size_per_vp_stage >= num_microbatches:
# Construct schedule for the last microbatch group
schedule_table.extend(
[
(microbatch_id, model_chunk_id)
for model_chunk_id in range(num_model_chunks)
for microbatch_id in range(min_microbatch_id_in_group, num_microbatches)
]
)
else:
# Construct schedule for other microbatch groups
schedule_table.extend(
[
(microbatch_id, model_chunk_id)
for model_chunk_id in range(num_model_chunks)
for microbatch_id in range(
min_microbatch_id_in_group,
min_microbatch_id_in_group + microbatch_group_size_per_vp_stage,
)
]
)
return schedule_table
def _convert_schedule_table_to_order(num_warmup_microbatches, num_model_chunks, schedule_table):
"""Convert a tunable schedule lookup table to the te.make_graphed_callables() accepted
order format. For example, the tunable schedule table for PP2 N3M5 with VP2 is as below:
virtual_microbatch_id | 0 1 2 3 4 5 6 7 8 9
microbatch_id | 0 1 2 0 1 2 3 4 3 4
model_chunk_id | 0 0 0 1 1 1 0 0 1 1
Then the forward backward separated order is:
forward | 1 1 1 2 2 2 1 1 2 2
backward | -2 -2 -2 -1 -1 -1 -2 -2 -1 -1
If num_warmup_microbatches is 5, the output order is:
1 1 1 2 2 2 -2 1 -2 1 -2 2 -1 2 -1 -1 -2 -2 -1 -1
"""
_, model_chunk_id_table = zip(*schedule_table)
forward_order = [chunk_id + 1 for chunk_id in model_chunk_id_table]
backward_order = [chunk_id - num_model_chunks for chunk_id in model_chunk_id_table]
order = forward_order[:num_warmup_microbatches]
for i in range(num_warmup_microbatches, len(forward_order)):
order.append(forward_order[i])
order.append(backward_order[i - num_warmup_microbatches])
if num_warmup_microbatches > 0:
order.extend(backward_order[-num_warmup_microbatches:])
return order
# Some codes are copied from Megatron-LM
def generate_1f1b_interleave_schedule(config: ScheduleConfig):
schedule = Schedule(config)
for device_id in range(config.num_devices):
microbatch_group_size_per_vp_stage = config.num_devices
num_warmup_microbatches = _get_pp_rank_microbatches(
config.num_batches,
config.num_devices,
device_id,
config.num_stages_per_device,
microbatch_group_size_per_vp_stage,
)
schedule_table = _get_schedule_table(
config.num_batches,
config.num_stages_per_device,
microbatch_group_size_per_vp_stage,
)
order = _convert_schedule_table_to_order(
num_warmup_microbatches,
num_model_chunks=config.num_stages_per_device,
schedule_table=schedule_table,
)
cur_stage_microbatch_id = {}
for i in range(1, config.num_stages_per_device+1):
cur_stage_microbatch_id[i] = 0
cur_stage_microbatch_id[-i] = 0
for order_item in order:
stage_id = schedule.device_queues[device_id].stages[abs(order_item)-1]
if order_item > 0:
op_type = "forward"
micro_batch_id = cur_stage_microbatch_id[order_item]
cur_stage_microbatch_id[order_item] = cur_stage_microbatch_id[order_item] + 1
elif order_item < 0:
op_type = "backward"
micro_batch_id = cur_stage_microbatch_id[order_item]
cur_stage_microbatch_id[order_item] = cur_stage_microbatch_id[order_item] + 1
else:
raise ValueError(f"Invalid order item: {order_item}")
schedule.device_queues[device_id].add_operation(
schedule.get_op(micro_batch_id, stage_id, op_type)
)
return schedule
def generate_1f1b_interleave_overlap_schedule(config: ScheduleConfig):
schedule = Schedule(config)
for device_id in range(config.num_devices):
microbatch_group_size_per_vp_stage = config.num_devices
num_warmup_microbatches = _get_pp_rank_microbatches(
config.num_batches,
config.num_devices,
device_id,
config.num_stages_per_device,
microbatch_group_size_per_vp_stage,
)
schedule_table = _get_schedule_table(
config.num_batches,
config.num_stages_per_device,
microbatch_group_size_per_vp_stage,
)
# NOTE: Add one more warmup microbatch for overlapped operations!
num_warmup_microbatches += 1
order = _convert_schedule_table_to_order(
num_warmup_microbatches,
num_model_chunks=config.num_stages_per_device,
schedule_table=schedule_table,
)
cur_stage_microbatch_id = {}
for i in range(1, config.num_stages_per_device+1):
cur_stage_microbatch_id[i] = 0
cur_stage_microbatch_id[-i] = 0
i = 0
num_overlapped_batches = len(order) - num_warmup_microbatches * 2
while i < len(order):
if i < num_warmup_microbatches:
order_item = order[i]
assert order_item > 0
op_type = "forward"
micro_batch_id = cur_stage_microbatch_id[order_item]
cur_stage_microbatch_id[order_item] = cur_stage_microbatch_id[order_item] + 1
stage_id = schedule.device_queues[device_id].stages[abs(order_item)-1]
schedule.device_queues[device_id].add_operation(
schedule.get_op(micro_batch_id, stage_id, op_type)
)
i += 1
elif i >= num_warmup_microbatches and i < num_warmup_microbatches + num_overlapped_batches - 1:
order_item_a = order[i]
order_item_b = order[i+1]
op_type_a = "forward" if order_item_a > 0 else "backward"
micro_batch_id_a = cur_stage_microbatch_id[order_item_a]
cur_stage_microbatch_id[order_item_a] = cur_stage_microbatch_id[order_item_a] + 1
op_type_b = "forward" if order_item_b > 0 else "backward"
micro_batch_id_b = cur_stage_microbatch_id[order_item_b]
cur_stage_microbatch_id[order_item_b] = cur_stage_microbatch_id[order_item_b] + 1
stage_id_a = schedule.device_queues[device_id].stages[abs(order_item_a)-1]
stage_id_b = schedule.device_queues[device_id].stages[abs(order_item_b)-1]
op_a = schedule.get_op(micro_batch_id_a, stage_id_a, op_type_a)
op_b = schedule.get_op(micro_batch_id_b, stage_id_b, op_type_b)
overlapped_op = OverlappedOperation([op_a, op_b])
schedule.register_overlapped_operation(overlapped_op)
schedule.device_queues[device_id].add_operation(overlapped_op)
i += 2
else:
assert i >= num_warmup_microbatches + num_overlapped_batches
order_item = order[i]
assert order_item < 0
op_type = "backward"
micro_batch_id = cur_stage_microbatch_id[order_item]
cur_stage_microbatch_id[order_item] = cur_stage_microbatch_id[order_item] + 1
stage_id = schedule.device_queues[device_id].stages[abs(order_item)-1]
schedule.device_queues[device_id].add_operation(
schedule.get_op(micro_batch_id, stage_id, op_type)
)
i += 1
return schedule
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