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import gc
import os
from concurrent.futures import ThreadPoolExecutor
from pathlib import Path
import tensorrt as trt
import torch
from filelock import FileLock
from tensorrt_llm.functional import DimRange, Tensor
from tensorrt_llm.logger import logger
from tensorrt_llm.network import Network, net_guard
from .config import AutoParallelConfig
from .device_mesh import LogicalDeviceMesh, PhysicalDeviceMesh
from .node_graph import NodeGraph
from .parallelization import ParallelConfig, parallelize
from .pipeline_graph import PipelineGraph
from .simplifier import GraphConfig, Simplifier, StageType
from .utils import current_flags
def to_network(graph: PipelineGraph, network: Network):
logger.debug("Converting graph to network")
trt_network = graph.as_trt()
trt_network.name = network.trt_network.name
new_network = Network()
new_network._init(trt_network)
new_network._dtype = network._dtype
new_network._plugin_config = network._plugin_config
new_network._unfilled_weights = graph._unfilled_weights
new_network._auto_parallel_config = graph._auto_parallel_config
with net_guard(network):
for i in range(trt_network.num_inputs):
input = trt_network.get_input(i)
tensor = Tensor(is_network_input=False)
if input.name in network._inputs:
profiles = network._inputs[input.name].profiles
elif len(network._inputs) == 0:
profiles = []
else:
shape = input.shape
num_profiles = len(list(network._inputs.values())[0].profiles)
profile = DimRange(shape, [None] * len(shape))
profiles = [profile] * num_profiles
tensor.profiles = profiles
tensor.trt_tensor = input
new_network._inputs[input.name] = tensor
return new_network
def find_solution(
node_graph: NodeGraph,
graph_config: GraphConfig,
lmesh: LogicalDeviceMesh,
memory_budget: int,
flags: list,
device: int,
dump_path: str,
) -> ParallelConfig:
torch.cuda.set_device(device)
with current_flags(*flags):
cost_graph = node_graph.get_cost_graph(lmesh)
num_stages = graph_config.num_stages
if num_stages == 1:
stage_types = [None]
elif num_stages == 2:
stage_types = [StageType.START, StageType.END]
else:
stage_types = [StageType.START, StageType.BLOCK, StageType.END]
best_config, best_solution = None, None
for stage_type in stage_types:
if stage_type is not None:
node_graph.set_slowest_stage(stage_type, graph_config)
solution = node_graph.find_solution(
cost_graph,
memory_budget,
)
cost = solution.total_cost
if best_config is None or cost < best_config.cost:
best_config = ParallelConfig()
best_config.graph_config = graph_config
best_config.lmesh = lmesh
best_config.cost = cost
best_config.graph_strategy = solution.node_best_strategy
best_config.stage_type = stage_type
best_solution = solution
if dump_path is not None:
lock = FileLock(f"{dump_path}/path.lock", thread_local=False)
vlz_name = f"{dump_path}/solution."
if graph_config.num_micro_batches != 1:
vlz_name += f"mbs{graph_config.num_micro_batches}."
if graph_config.num_stages != 1:
vlz_name += f"stages{graph_config.num_stages}."
vlz_name += lmesh.cluster_key
with lock:
node_graph.visualize_solution(
best_solution,
vlz_name,
ignore_shape_io=True,
)
return best_config
def infer_builder_flags(network):
fp16_enabled = False
bf16_enabled = False
int8_enabled = False
fp8_enabled = False
def check_dtype(tensor):
nonlocal fp16_enabled
nonlocal bf16_enabled
nonlocal int8_enabled
nonlocal fp8_enabled
if tensor.dtype == trt.DataType.HALF:
fp16_enabled = True
elif tensor.dtype == trt.DataType.BF16:
bf16_enabled = True
elif tensor.dtype == trt.DataType.INT8:
int8_enabled = True
elif tensor.dtype == trt.DataType.FP8:
fp8_enabled = True
trt_network = network.trt_network
for i in range(trt_network.num_inputs):
input = trt_network.get_input(i)
check_dtype(input)
for i in range(trt_network.num_layers):
layer = trt_network.get_layer(i)
for j in range(layer.num_outputs):
output = layer.get_output(j)
check_dtype(output)
builder_flags = 0
if fp16_enabled:
builder_flags |= 1 << int(trt.BuilderFlag.FP16)
builder_flags |= 1 << int(trt.BuilderFlag.OBEY_PRECISION_CONSTRAINTS)
if bf16_enabled:
builder_flags |= 1 << int(trt.BuilderFlag.BF16)
builder_flags |= 1 << int(trt.BuilderFlag.OBEY_PRECISION_CONSTRAINTS)
if int8_enabled:
builder_flags |= 1 << int(trt.BuilderFlag.INT8)
if fp8_enabled:
builder_flags |= 1 << int(trt.BuilderFlag.FP8)
builder_flags |= 1 << int(trt.BuilderFlag.OBEY_PRECISION_CONSTRAINTS)
return builder_flags
def auto_parallel(network: Network, config: AutoParallelConfig):
debug_mode = config.debug_mode
memory_budget = config.get_cluster_info(
).memory_budget_per_device * 1024 * 1024 * 1024
enable_pipeline_parallelism = config.enable_pipeline_parallelism
if config.world_size < config.gpus_per_node:
num_hosts = 1
num_devices_per_host = config.world_size
else:
assert config.world_size % config.gpus_per_node == 0
num_hosts = config.world_size // config.gpus_per_node
num_devices_per_host = config.gpus_per_node
parallel_config_cache = config.parallel_config_cache
dump_path = config.dump_path if debug_mode else None
fill_weights = config.fill_weights
if num_hosts == 1 and num_devices_per_host == 1:
return [network]
if dump_path is not None:
if not os.path.exists(dump_path):
os.makedirs(dump_path)
builder_flags = config.builder_flags or infer_builder_flags(network)
flags = [builder_flags, network.strongly_typed]
with current_flags(*flags):
simplifier = Simplifier(network, config)
network_hash = simplifier.get_network_hash()
best_config = None
if parallel_config_cache is not None and Path(
parallel_config_cache).exists():
parallel_config = ParallelConfig.from_file(parallel_config_cache)
if (ParallelConfig.VERSION == parallel_config.version
and network_hash == parallel_config.network_hash
and config == parallel_config.auto_parallel_config):
logger.info(
f"use cache of parallel config from {parallel_config_cache}"
)
best_config = parallel_config
if best_config is None:
num_devices = num_hosts * num_devices_per_host
phy_ids = [[
i + j * num_devices_per_host
for i in range(num_devices_per_host)
] for j in range(num_hosts)]
phy_mesh = PhysicalDeviceMesh(phy_ids, config)
if enable_pipeline_parallelism:
num_micro_batches_list = simplifier.list_all_num_micro_batches()
else:
num_micro_batches_list = [1]
jobs = []
for num_micro_batches in num_micro_batches_list:
simplifier.infer_shapes(num_micro_batches)
if enable_pipeline_parallelism:
pipeline_configs = phy_mesh.list_all_pipeline_configs()
else:
pipeline_configs = [(1, num_devices)]
for num_stages, num_devices_per_stage in pipeline_configs:
# TODO: add fallback path that allows num_micro_batches >= num_stages
# if no solution satisfies memory budget
if num_micro_batches < num_stages:
continue
simplified_graph, graph_config = simplifier.simplify_graph(
phy_mesh,
num_stages,
num_devices_per_stage,
)
if simplified_graph is None:
continue
node_graph = NodeGraph(simplified_graph)
node_graph.assign_cost_weights(graph_config)
lmeshes = graph_config.stage_phy_meshes[
0].get_logical_meshes()
for lmesh in lmeshes:
jobs.append(
(node_graph, graph_config, lmesh, memory_budget *
(num_devices / num_devices_per_stage)))
try:
with ThreadPoolExecutor() as executor:
best_config = sorted(
executor.map(
lambda x: find_solution(
*x,
flags,
torch.cuda.current_device(),
dump_path,
),
jobs,
),
key=lambda x: x.cost,
)[0]
finally:
phy_mesh.close()
if parallel_config_cache is not None:
best_config.network_hash = network_hash
best_config.auto_parallel_config = config
best_config.save(parallel_config_cache)
new_graphs = parallelize(simplifier, best_config)
networks = [to_network(new_graph, network) for new_graph in new_graphs]
if debug_mode and fill_weights:
networks[0]._fill_weights()
gc.collect()
torch.cuda.empty_cache()
return networks
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