Making large AI models cheaper, faster and more accessible
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from .estimate_memory import EstimateMemory
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from .reorder_graph import ReorderGraph
from .trace_index import TraceIndex
from .utils import is_non_compute_node
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class SelectChunk(object):
def __init__(
self,
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trace_index: TraceIndex,
estimate_memory: EstimateMemory,
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reorder_graph: ReorderGraph,
max_memory=None,
):
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self.index_tracer = trace_index
self.memory_estimator = estimate_memory
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self.reorder_graph = reorder_graph
if max_memory is not None:
self.stratge = "fit_memory"
self.max_memory = max_memory # MB
else:
self.stratge = "min_memory"
def _select_best_chunk_region(
self, possible_chunk_regions, chunk_infos, peak_node, max_chunk_region, mem_peak
):
if self.stratge == "min_memory":
best_region = self._select_min_memory_chunk_region(
possible_chunk_regions,
chunk_infos,
peak_node,
max_chunk_region,
mem_peak,
)
elif self.stratge == "fit_memory":
best_region = self._select_fit_memory_chunk_region(
possible_chunk_regions,
chunk_infos,
peak_node,
max_chunk_region,
mem_peak,
)
else:
raise RuntimeError()
return best_region
def _select_fit_memory_chunk_region(
self, possible_chunk_regions, chunk_infos, peak_node, max_chunk_region, mem_peak
):
# stop chunk if max memory satisfy memory limit
if max(mem_peak) < self.max_memory:
return None
# remove illegal regions
illegal_regions = []
for i in possible_chunk_regions:
if not self._is_legal_region(i, chunk_infos):
illegal_regions.append(i)
for i in illegal_regions:
if i in possible_chunk_regions:
possible_chunk_regions.remove(i)
if len(possible_chunk_regions) == 0:
return None
# get mem for chunk region
regions_dict = []
for region in possible_chunk_regions:
cur_region = region.copy()
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cur_node_list, cur_region = self.reorder_graph.tmp_reorder(
self.index_tracer.node_list, cur_region
)
cur_chunk_infos = chunk_infos + [cur_region]
cur_mem_peak = self.memory_estimator.estimate_chunk_inference_mem(
cur_node_list, cur_chunk_infos
)[0]
cur_chunk_region_peak = cur_mem_peak[
max_chunk_region[0] : max_chunk_region[1] + 1
]
cur_chunk_region_max_peak = max(cur_chunk_region_peak)
if cur_chunk_region_max_peak < self.max_memory:
regions_dict.append(
{
"chunk_info": region,
"chunk_max_mem": cur_chunk_region_max_peak,
"chunk_len": self._get_compute_node_num(
region["region"][0], region["region"][1]
),
"reorder_chunk_info": cur_region,
"reorder_node_list": cur_node_list,
}
)
# no region found
if len(regions_dict) == 0:
raise RuntimeError("Search failed. Try a larger memory threshold.")
# select the min chunk len
chunk_len = [i["chunk_len"] for i in regions_dict]
best_region_idx = chunk_len.index(min(chunk_len))
best_region = regions_dict[best_region_idx]
# get max chunk size
best_region = self._get_fit_chunk_size(best_region, chunk_infos)
return best_region
def _get_fit_chunk_size(self, chunk_region_dict, chunk_infos):
chunk_size = 1
reorder_chunk_info = chunk_region_dict["reorder_chunk_info"]
reorder_chunk_info["chunk_size"] = chunk_size
cur_chunk_max_mem = 0
# search a region
while cur_chunk_max_mem < self.max_memory:
chunk_size *= 2
reorder_chunk_info["chunk_size"] = chunk_size
cur_chunk_infos = chunk_infos + [reorder_chunk_info]
cur_mem_peak = self.memory_estimator.estimate_chunk_inference_mem(
chunk_region_dict["reorder_node_list"], cur_chunk_infos
)[0]
cur_chunk_max_mem = max(
cur_mem_peak[
reorder_chunk_info["region"][0] : reorder_chunk_info["region"][1]
+ 1
]
)
# search exact size
chunk_info = chunk_region_dict["chunk_info"]
chunk_info["chunk_size"] = self._chunk_size_binary_search(
chunk_size // 2, chunk_size, chunk_region_dict, chunk_infos
)
return chunk_info
def _chunk_size_binary_search(self, left, right, chunk_region_dict, chunk_infos):
if left >= 16:
gap = 4
else:
gap = 1
chunk_info = chunk_region_dict["reorder_chunk_info"]
while right >= left + gap:
mid = int((left + right) / 2 + 0.5)
chunk_info["chunk_size"] = mid
cur_chunk_infos = chunk_infos + [chunk_info]
cur_mem_peak = self.memory_estimator.estimate_chunk_inference_mem(
chunk_region_dict["reorder_node_list"], cur_chunk_infos
)[0]
cur_chunk_max_mem = max(
cur_mem_peak[chunk_info["region"][0] : chunk_info["region"][1] + 1]
)
if cur_chunk_max_mem >= self.max_memory:
right = mid - gap
else:
left = mid + gap
return left
def _get_compute_node_num(self, start, end):
count = 0
for i in self.index_tracer.node_list[start : end + 1]:
if not is_non_compute_node(i):
count += 1
return count
def _select_min_memory_chunk_region(
self, possible_chunk_regions, chunk_infos, peak_node, max_chunk_region, mem_peak
):
# remove illegal regions
illegal_regions = []
for i in possible_chunk_regions:
if not self._is_legal_region(i, chunk_infos):
illegal_regions.append(i)
for i in illegal_regions:
if i in possible_chunk_regions:
possible_chunk_regions.remove(i)
if len(possible_chunk_regions) == 0:
return None
# get mem for chunk region
regions_dict = []
for region in possible_chunk_regions:
cur_region = region.copy()
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cur_node_list, cur_region = self.reorder_graph.tmp_reorder(
self.index_tracer.node_list, cur_region
)
cur_chunk_infos = chunk_infos + [cur_region]
cur_mem_peak = self.memory_estimator.estimate_chunk_inference_mem(
cur_node_list, cur_chunk_infos
)[0]
cur_chunk_region_peak = cur_mem_peak[
max_chunk_region[0] : max_chunk_region[1] + 1
]
cur_chunk_region_max_peak = max(cur_chunk_region_peak)
regions_dict.append(
{
"chunk_info": region,
"chunk_max_mem": cur_chunk_region_max_peak,
"chunk_len": self._get_compute_node_num(
region["region"][0], region["region"][1]
),
"reorder_chunk_info": cur_region,
"reorder_node_list": cur_node_list,
}
)
# select the min mem
chunk_max_mem = [i["chunk_max_mem"] for i in regions_dict]
best_region_idx = chunk_max_mem.index(min(chunk_max_mem))
best_region = regions_dict[best_region_idx]["chunk_info"]
if best_region is not None:
best_region["chunk_size"] = 1
return best_region
def _is_legal_region(self, cur_chunk_info, chunk_infos):
(chunk_region_start, chunk_region_end) = cur_chunk_info["region"]
if cur_chunk_info in chunk_infos:
return False
if chunk_region_end < chunk_region_start:
return False
for i in chunk_infos:
region = i["region"]
if not (
(chunk_region_start > region[1] and chunk_region_end > region[1])
or (chunk_region_start < region[0] and chunk_region_end < region[0])
):
return False
return True