forked from jiuyuan/InfiniTensor
kunlun distributed
This commit is contained in:
weijie01
parent
a71cd14963
commit
32a13b7760
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@ -285,8 +285,8 @@ if(USE_KUNLUN)
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message(STATUS "KUNLUN_HOME: ${KUNLUN_HOME}")
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include_directories("${KUNLUN_HOME}/include/")
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find_library(KUNLUN_RT libxpurt.so "${KUNLUN_HOME}/so/")
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find_library(KUNLUN_DNN libxpuapi.so "${KUNLUN_HOME}/so/")
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find_library(KUNLUN_RT libxpurt.so "${KUNLUN_HOME}/lib64/")
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find_library(KUNLUN_DNN libxpuapi.so "${KUNLUN_HOME}/lib64/")
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set(CMAKE_CXX_FLAGS "${CMAKE_CXX_FLAGS} -lstdc++ -Wall -Werror")
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if ((NOT DEFINED TARGET_CPU_ARCH) AND (NOT DEFINED ENV{TARGET_CPU_ARCH}))
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@ -9,7 +9,7 @@ find_path(XCCL_INCLUDE_DIRS # ${XCCL_INCLUDE_DIR}
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HINTS XCCL_INCLUDE_DIR)
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find_library(XCCL_LIBRARIES # ${XCCL_LIB_DIR}
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NAMES so/libbkcl.so
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NAMES lib64/libbkcl.so
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HINTS XCCL_LIB_DIR)
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message(STATUS "XCCL_INCLUDE_DIRS: ${XCCL_INCLUDE_DIRS}")
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@ -0,0 +1,278 @@
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import sys
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sys.path.append('../')
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import argparse
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import os
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import time
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import multiprocessing as mp
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from pyinfinitensor.onnx import OnnxStub, backend
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import onnx
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from onnx.external_data_helper import convert_model_to_external_data
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from onnx.shape_inference import infer_shapes_path
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import numpy as np
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from parallel_opt import parallel_model
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from functools import wraps
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def parse_args():
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parser = argparse.ArgumentParser(description="launch distributed infinitensor")
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parser.add_argument("--num_nodes", type=int, default=1, help="number of nodes")
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parser.add_argument(
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"--nproc_per_node", type=int, default=2, help="number of processes per node"
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)
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parser.add_argument(
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"--name", type=str, default="test", help="name of this instance."
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)
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parser.add_argument(
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"--model", type=str, default="", help="path to the ONNX model file."
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)
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parser.add_argument(
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"--gen_std",
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default=False,
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action="store_true",
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help="whether to generate the standard results.",
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)
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parser.add_argument(
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"--run_single",
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default=False,
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action="store_true",
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help="whether run model with single process with standard inputs"
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)
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parser.add_argument(
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"--input_dir",
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default="./",
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help="path to save model input data"
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)
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parser.add_argument(
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"--result_dir",
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default="./",
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help="path to save model standard output"
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)
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parser.add_argument(
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"--internal_model_dir",
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default="./",
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help="path to save internal onnx model for parallel run"
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)
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args = parser.parse_args()
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# check path, mkdir if not exist
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check_exists(args.input_dir)
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check_exists(args.result_dir)
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check_exists(args.internal_model_dir)
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print("arg setting: ", args)
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return (
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args.num_nodes,
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args.nproc_per_node,
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args.name,
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args.model,
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args.gen_std,
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args.run_single,
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args.input_dir,
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args.result_dir,
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args.internal_model_dir
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)
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"""
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utils function for this scripts
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"""
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def check_exists(path: str):
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if not os.path.exists(path):
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os.makedirs(path)
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def np_assert(base, test, rtol=1e-2, atol=1e-1):
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# np.testing.assert_allclose(test, base, rtol, atol)
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print("max abs diff:", abs(base - test).max())
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"""
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Perf wrapper, run function n times
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then average
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"""
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def perf_it(n):
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def decorator(func):
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@wraps(func)
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def wrapper(*args, **kwargs):
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# warmup
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for _ in range(n):
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func(*args, **kwargs)
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t_total = 0
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for _ in range(n):
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t0 = time.time()
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func(*args, **kwargs)
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t1 = time.time()
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t_total += t1 - t0
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avg_time = (t_total) / n
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print(f"Avg runtime of {n} time is {avg_time:.6f} seconds")
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return avg_time
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return wrapper
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return decorator
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"""
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Run InfiniTensor model with Standard input
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check=True: check with standard output gen by pytorch
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perf=True: run n times to get avg time
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"""
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def run_model(task_name,
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model,
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runtime,
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world_size=1,
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rank=0,
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n=10,
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check=True,
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perf=True):
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stub = OnnxStub(model, runtime)
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# load in Onnx model inputs
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def load_inputs(stub: OnnxStub):
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# check exists
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inputs = []
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for i, (name, tensor) in enumerate(stub.inputs.items()):
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input_path = os.path.join(input_dir, \
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f"{task_name}_input_{i}.npy")
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print(input_path)
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if os.path.exists(input_path):
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input = np.load(input_path)
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else :
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raise KeyError(f"{i} th input of model not exists")
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# check shape
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if all(x == y for x,y in zip(input.shape, tensor.shape())):
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tensor.copyin_numpy(input)
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else:
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tensor.copyin_numpy(np.hsplit(input, world_size)[rank])
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load_inputs(stub)
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# stub.tune()
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stub.run()
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time.sleep(0.01)
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output = next(stub.outputs.values().__iter__()).copyout_numpy()
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# check output results with standard output
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if check:
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st_output_path = os.path.join(result_dir, \
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f"{task_name}_output.npy")
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assert os.path.exists(st_output_path) , \
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"standard output not exists"
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st_output = np.load(st_output_path)
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if np.isnan(output).any():
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print("Nan in output")
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exit()
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np_assert(st_output, output)
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# perf
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if perf:
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@perf_it(n)
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def perf_infinitensor(stub: OnnxStub):
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stub.run()
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perf_infinitensor(stub)
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return output
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"""
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Start a worker in Parallel
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"""
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def start_worker(name: str,
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world_size: int,
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rank: int,
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local_rank: int,
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model: onnx.ModelProto):
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dist_name = name + "_dist"
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# partial a onnx model to world_size part
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model = parallel_model(model, world_size, rank)
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onnx.save(model, os.path.join(internal_model_dir, \
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f"{dist_name}_rank{rank}.onnx"))
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runtime = backend.KUNLUNRuntime(local_rank)
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# print("init comm")
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runtime.init_comm(
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dist_name,
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world_size,
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rank,
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)
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run_model(name, model, runtime, world_size, rank)
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"""
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generate standard input/output with
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sigle card run
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"""
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def gen_stardard(task_name: str, model: onnx.ModelProto):
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runtime = backend.KUNLUNRuntime(0)
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stub = OnnxStub(model, runtime)
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position_id = 0
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# generate random input for model
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for i, (name, tensor) in enumerate(stub.inputs.items()):
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input = tensor.copyout_numpy()
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if np.issubdtype(input.dtype, np.integer):
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if input.size == 1:
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input = np.random.randint(0,2,size=input.shape, dtype=input.dtype)
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else:
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input = np.random.randint(0,2,size=input.shape, dtype=input.dtype)
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elif input.dtype == np.bool_:
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input = np.random.randint(0,2,size=input.shape) > 0
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else:
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if i == 0:
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input = np.ones(input.shape).astype(input.dtype)
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position_id = input.shape[-1] - 1
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else:
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input = np.random.rand(*input.shape).astype(input.dtype)
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tensor.copyin_numpy(input)
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np.save(os.path.join(input_dir, \
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f"{task_name}_input_{i}.npy"), input)
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stub.run()
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# print(stub.outputs)
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output = next(stub.outputs.values().__iter__()).copyout_numpy()
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if np.isnan(output).any():
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print("Nan in output")
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exit()
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np.save(os.path.join(result_dir, f"{task_name}_output.npy"), output)
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def main():
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global input_dir, result_dir, internal_model_dir
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nnodes, nproc_per_node, task_name, \
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model_path, gen_std, run_single, \
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input_dir, result_dir, internal_model_dir = parse_args()
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# load input onnx model
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model = onnx.load(model_path)
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# generate standart output
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if gen_std:
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print("Generate inputs and outputs.")
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gen_stardard(task_name, model)
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return
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if run_single:
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print("Run model by one GPU card.")
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runtime = backend.KUNLUNRuntime(0)
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run_model(task_name, model, runtime)
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return
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# run distributed parallel.
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world_size = nnodes * nproc_per_node
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print(f"Run model by {world_size} GPU in parallel.")
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workers = [
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mp.Process(
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target=start_worker,
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args=(task_name, world_size, rank, rank % nproc_per_node, model),
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)
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for rank in range(world_size)
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]
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for w in workers:
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w.start()
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for w in workers:
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w.join()
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if __name__ == "__main__":
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main()
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@ -0,0 +1,215 @@
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import sys
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sys.path.append("../")
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import argparse
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import os
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import time
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import multiprocessing as mp
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from pyinfinitensor.onnx import OnnxStub, backend
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import onnx
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from onnx.external_data_helper import convert_model_to_external_data
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from onnx.shape_inference import infer_shapes_path
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import numpy as np
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from parallel_opt import parallel_model
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st_input_dir = ".cache/input/"
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st_output_dir = ".cache/output/"
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def parse_args():
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parser = argparse.ArgumentParser(description="launch distributed infinitensor")
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parser.add_argument("--num_nodes", type=int, default=1, help="number of nodes")
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parser.add_argument(
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"--nproc_per_node", type=int, default=2, help="number of processes per node"
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)
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parser.add_argument(
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"--name", type=str, default="test", help="name of this instance."
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)
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parser.add_argument(
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"--model", type=str, default="/data1/shared/panzezhong/llama/fp32/my_llama_fp32.sim.onnx", help="path to the ONNX model file."
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)
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parser.add_argument("--batch_size", type=int, default=1, help="batch size.")
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parser.add_argument("--length", type=int, default=1, help="sequence length.")
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parser.add_argument(
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"--gen_std",
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default=False,
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action="store_true",
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help="whether to generate the standard results.",
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)
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parser.add_argument(
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"--run_single",
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default=False,
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action="store_true",
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help="whether run model with single process with standard inputs"
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)
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args = parser.parse_args()
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print("arg setting: ", args)
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return (
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args.num_nodes,
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args.nproc_per_node,
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args.name,
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args.model,
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args.batch_size,
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args.length,
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args.gen_std,
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args.run_single
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)
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def run_model(model, runtime, world_size=1, rank=0, n=10):
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stub = OnnxStub(model, runtime)
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load_inputs(stub, world_size, rank)
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# stub.tune()
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stub.run()
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# get outputs
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time.sleep(0.01)
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outputs = next(stub.outputs.values().__iter__()).copyout_numpy()
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# bench
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begin = time.time()
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for _ in range(n):
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stub.run()
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end = time.time()
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avg_time = (end - begin) / n
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print(f"average time: {avg_time}")
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return outputs
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def run_and_compare(name, model, runtime, world_size=1, rank = 0):
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results = np.load(os.path.join(st_output_dir, "test_output.npy"))
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outputs = run_model(model, runtime, world_size, rank)
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print(outputs[:100])
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if np.isnan(outputs).any():
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print("Nan in output")
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print("answer argmax:", np.argmax(results))
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print("output argmax:", np.argmax(outputs))
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#np.testing.assert_allclose(outputs, results, rtol=1e-3, atol=1e-3)
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getDiff(results, outputs)
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def start_worker(
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name: str, world_size: int, rank: int, local_rank: int, model: onnx.ModelProto
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):
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dist_name = name + "_dist"
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model = parallel_model(model, world_size, rank)
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extern_path = f"./{dist_name}_rank{rank}.pb"
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if os.path.exists(extern_path):
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os.remove(extern_path)
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onnx.save_model(
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model,
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f"./{dist_name}_rank{rank}.onnx",
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save_as_external_data=True,
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location=extern_path,
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)
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infer_shapes_path(f"./{dist_name}_rank{rank}.onnx")
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runtime = backend.KUNLUNRuntime(local_rank)
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# print("init comm")
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runtime.init_comm(
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dist_name,
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world_size,
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rank,
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)
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run_and_compare(name, model, runtime, world_size, rank)
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def start_single(name, model):
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runtime = backend.KUNLUNRuntime(0)
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run_and_compare(name, model, runtime)
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def generate_input_output(model):
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runtime = backend.KUNLUNRuntime(0)
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stub = OnnxStub(model, runtime)
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position_id = 0
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for i, (name, tensor) in enumerate(stub.inputs.items()):
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input = tensor.copyout_numpy()
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if np.issubdtype(input.dtype, np.integer):
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if input.size == 1:
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# input = np.array([position_id])
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input = np.random.randint(0,2,size=input.shape, dtype=input.dtype)
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else:
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input = np.random.randint(0,2,size=input.shape, dtype=input.dtype)
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elif input.dtype == np.bool_:
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input = np.random.randint(0,2,size=input.shape) > 0
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else:
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if i == 0:
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input = np.ones(input.shape).astype(input.dtype)
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position_id = input.shape[-1] - 1
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else:
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input = np.random.rand(*input.shape).astype(input.dtype)
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tensor.copyin_numpy(input)
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np.save(os.path.join(st_input_dir, f"input_{i}"), input)
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stub.run()
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# print(stub.outputs)
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time.sleep(0.01)
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output = next(stub.outputs.values().__iter__()).copyout_numpy()
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print(output[:100])
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if np.isnan(output).any():
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print("Nan in output")
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np.save(os.path.join(st_output_dir, f"output"), output)
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def load_inputs(stub, world_size=1, rank=0):
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for i, (name, tensor) in enumerate(stub.inputs.items()):
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input = np.load(os.path.join(st_input_dir, f"test_input_{name}.npy"))
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if all(x == y for x,y in zip(input.shape,tensor.shape())):
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tensor.copyin_numpy(input)
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else:
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tensor.copyin_numpy(np.hsplit(input, world_size)[rank])
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def getDiff(base, test):
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absolute_diff = np.abs(np.subtract(base, test))
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max_absolute_diff = np.max(absolute_diff)
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baseCopy = base.astype(np.float64).ravel()
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testCopy = test.astype(np.float64).ravel()
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upValue = np.sum(np.abs(baseCopy - testCopy))
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downValue = np.sum(np.abs(baseCopy)) + np.float64(1e-9)
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max_relative_diff = upValue / downValue
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print(f"Max absolute difference: {max_absolute_diff}\nMax relative difference: {max_relative_diff}")
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return max_absolute_diff, max_relative_diff
|
||||
|
||||
|
||||
def main():
|
||||
nnodes, nproc_per_node, name, model_path, bs, length, gen_std, run_single = parse_args()
|
||||
|
||||
model = onnx.load(model_path)
|
||||
|
||||
# generate standart output
|
||||
if gen_std:
|
||||
print("Generate inputs and outputs.")
|
||||
p = mp.Process(target=generate_input_output, args=[model])
|
||||
p.start()
|
||||
p.join()
|
||||
return
|
||||
|
||||
# # run single process.
|
||||
# # use standalone process to isolate cuda.
|
||||
if run_single:
|
||||
print("run model by single GPU.")
|
||||
p = mp.Process(target=start_single, args=(name, model))
|
||||
p.start()
|
||||
p.join()
|
||||
return
|
||||
|
||||
# run distributed parallel.
|
||||
world_size = nnodes * nproc_per_node
|
||||
print(f"run model by {world_size} GPU in parallel.")
|
||||
workers = [
|
||||
mp.Process(
|
||||
target=start_worker,
|
||||
args=(name, world_size, rank, rank % nproc_per_node, model),
|
||||
)
|
||||
for rank in range(world_size)
|
||||
]
|
||||
|
||||
for w in workers:
|
||||
w.start()
|
||||
|
||||
for w in workers:
|
||||
w.join()
|
||||
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
|
|
@ -0,0 +1,208 @@
|
|||
import argparse
|
||||
import torch
|
||||
from transformers import BertModel, BertConfig
|
||||
from transformers import GPT2Model, GPT2Config
|
||||
from transformers import OPTModel, OPTConfig
|
||||
from transformers import LlamaModel, LlamaConfig
|
||||
import time
|
||||
import numpy as np
|
||||
import onnx
|
||||
import os
|
||||
from onnx.external_data_helper import convert_model_to_external_data
|
||||
from onnxsim import simplify
|
||||
|
||||
torch.backends.cuda.matmul.allow_tf32 = False
|
||||
torch.backends.cudnn.allow_tf32 = False
|
||||
def parse_args():
|
||||
parser = argparse.ArgumentParser(description="Run pytorch gpt2/bert/opt and optionally export onnx.")
|
||||
parser.add_argument(
|
||||
"--model", type=str, choices=["gpt2", "bert", "opt", "llama"], required=True, help="model type"
|
||||
)
|
||||
parser.add_argument("--batch_size", type=int, default=1, help="batch size.")
|
||||
parser.add_argument("--length", type=int, default=1, help="sequence length.")
|
||||
parser.add_argument(
|
||||
"--export_onnx",
|
||||
type=str,
|
||||
nargs="?",
|
||||
default=None,
|
||||
const="./",
|
||||
help="whether and where to export onnx file",
|
||||
)
|
||||
parser.add_argument(
|
||||
"--input_dir",
|
||||
type=str,
|
||||
default="./",
|
||||
help="path to save pytorch model input data"
|
||||
)
|
||||
parser.add_argument(
|
||||
"--result_dir",
|
||||
type=str,
|
||||
default="./",
|
||||
help="path to save pytorch model output data"
|
||||
)
|
||||
args = parser.parse_args()
|
||||
print("arg setting: ", args)
|
||||
return (
|
||||
args.model,
|
||||
args.batch_size,
|
||||
args.length,
|
||||
args.export_onnx,
|
||||
args.input_dir,
|
||||
args.result_dir
|
||||
)
|
||||
|
||||
|
||||
def get_model(modelname):
|
||||
if modelname == "bert":
|
||||
model = BertModel.from_pretrained("bert-base-uncased", add_pooling_layer=False, hidden_act="gelu_new") # erf is not impl by infini
|
||||
voc_size = BertConfig().vocab_size
|
||||
elif modelname == "gpt2":
|
||||
model = GPT2Model.from_pretrained("gpt2")
|
||||
voc_size = GPT2Config().vocab_size
|
||||
elif modelname == "opt":
|
||||
model = OPTModel.from_pretrained("./opt-125m")
|
||||
voc_size = OPTConfig().vocab_size
|
||||
elif modelname == "llama":
|
||||
model = LlamaModel.from_pretrained("meta-llama/Llama-2-7b-hf")
|
||||
voc_size = LlamaConfig().vocab_size
|
||||
else :
|
||||
raise KeyError(modelname)
|
||||
|
||||
model = model.eval()
|
||||
return model, voc_size
|
||||
|
||||
def run_pytorch(torch_model, voc_size, batchsize, len, model_name):
|
||||
data = np.random.randint(0, voc_size, (batchsize, len), dtype=np.int32)
|
||||
np.save(os.path.join(input_dir, f"{model_name}_input_0.npy"), data)
|
||||
inputs = torch.from_numpy(data).to("cuda")
|
||||
torch_model = torch_model.to("cuda")
|
||||
|
||||
n_iter = 20
|
||||
with torch.no_grad():
|
||||
for _ in range(10):
|
||||
outputs = torch_model(inputs)
|
||||
torch.cuda.synchronize()
|
||||
begin = time.time()
|
||||
with torch.no_grad():
|
||||
for _ in range(n_iter):
|
||||
torch.cuda.synchronize()
|
||||
outputs = torch_model(inputs)
|
||||
#
|
||||
torch.cuda.synchronize()
|
||||
torch.cuda.synchronize()
|
||||
end = time.time()
|
||||
|
||||
avg_time = (end - begin) / n_iter
|
||||
outputs = outputs.last_hidden_state.to("cpu")
|
||||
print("outputs abs mean:", abs(np.array(outputs)).mean())
|
||||
print(f"average time: {avg_time}")
|
||||
torch.cuda.memory.empty_cache()
|
||||
np.save(os.path.join(result_dir, f"{model_name}_output.npy"), \
|
||||
np.array(outputs))
|
||||
print(f"Save input & output as {model_name}_input_0.npy and {model_name}_output.npy")
|
||||
|
||||
|
||||
def export_onnx(model_name, model, data, path, extern=False):
|
||||
torch.onnx.export(model, data, path, verbose=False, do_constant_folding=True)
|
||||
onnx_model = onnx.load(path)
|
||||
# onnx_model, check = simplify(onnx_model,
|
||||
# skip_shape_inference=True,
|
||||
# skipped_optimizers=['eliminate_duplicate_initializer'])
|
||||
if model_name == "gpt2":
|
||||
onnx_model, check = simplify(onnx_model,
|
||||
skip_shape_inference=True,
|
||||
skipped_optimizers=['fuse_qkv', 'eliminate_duplicate_initializer'])
|
||||
else :
|
||||
onnx_model, check = simplify(onnx_model,
|
||||
skipped_optimizers=['fuse_qkv', 'eliminate_duplicate_initializer'])
|
||||
assert check
|
||||
add_value_info_for_constants(onnx_model)
|
||||
onnx_model = onnx.shape_inference.infer_shapes(onnx_model)
|
||||
if extern:
|
||||
extern_path = path.replace('.onnx', '.pb')
|
||||
if os.path.exists(extern_path):
|
||||
os.remove(extern_path)
|
||||
convert_model_to_external_data(
|
||||
onnx_model,
|
||||
all_tensors_to_one_file=True,
|
||||
location=extern_path.split("/")[-1],
|
||||
size_threshold=1024,
|
||||
convert_attribute=False,
|
||||
)
|
||||
onnx.save(onnx_model, path)
|
||||
|
||||
def add_value_info_for_constants(model : onnx.ModelProto):
|
||||
"""
|
||||
Currently onnx.shape_inference doesn't use the shape of initializers, so add
|
||||
that info explicitly as ValueInfoProtos.
|
||||
Mutates the model.
|
||||
Args:
|
||||
model: The ModelProto to update.
|
||||
"""
|
||||
# All (top-level) constants will have ValueInfos before IRv4 as they are all inputs
|
||||
if model.ir_version < 4:
|
||||
return
|
||||
|
||||
def add_const_value_infos_to_graph(graph : onnx.GraphProto):
|
||||
inputs = {i.name for i in graph.input}
|
||||
existing_info = {vi.name: vi for vi in graph.value_info}
|
||||
for init in graph.initializer:
|
||||
# Check it really is a constant, not an input
|
||||
if init.name in inputs:
|
||||
continue
|
||||
|
||||
# The details we want to add
|
||||
elem_type = init.data_type
|
||||
shape = init.dims
|
||||
|
||||
# Get existing or create new value info for this constant
|
||||
vi = existing_info.get(init.name)
|
||||
if vi is None:
|
||||
vi = graph.value_info.add()
|
||||
vi.name = init.name
|
||||
|
||||
# Even though it would be weird, we will not overwrite info even if it doesn't match
|
||||
tt = vi.type.tensor_type
|
||||
if tt.elem_type == onnx.TensorProto.UNDEFINED:
|
||||
tt.elem_type = elem_type
|
||||
if not tt.HasField("shape"):
|
||||
# Ensure we set an empty list if the const is scalar (zero dims)
|
||||
tt.shape.dim.extend([])
|
||||
for dim in shape:
|
||||
tt.shape.dim.add().dim_value = dim
|
||||
|
||||
# Handle subgraphs
|
||||
for node in graph.node:
|
||||
for attr in node.attribute:
|
||||
# Ref attrs refer to other attrs, so we don't need to do anything
|
||||
if attr.ref_attr_name != "":
|
||||
continue
|
||||
|
||||
if attr.type == onnx.AttributeProto.GRAPH:
|
||||
add_const_value_infos_to_graph(attr.g)
|
||||
if attr.type == onnx.AttributeProto.GRAPHS:
|
||||
for g in attr.graphs:
|
||||
add_const_value_infos_to_graph(g)
|
||||
|
||||
|
||||
return add_const_value_infos_to_graph(model.graph)
|
||||
|
||||
|
||||
def main():
|
||||
global input_dir, result_dir
|
||||
|
||||
modelname, batchsize, seqlen, \
|
||||
export_path, input_dir, result_dir = parse_args()
|
||||
|
||||
model, voc_size = get_model(modelname) # pytorch model
|
||||
|
||||
if export_path is not None:
|
||||
filename = "{}_{}_{}.onnx".format(modelname, batchsize, seqlen)
|
||||
path = os.path.join(export_path, filename)
|
||||
param = torch.zeros((batchsize, seqlen), dtype=torch.int)
|
||||
export_onnx(modelname, model, param, path, True) # export pytorch model to onnx model
|
||||
|
||||
run_pytorch(model, voc_size, batchsize, seqlen, modelname)
|
||||
|
||||
if __name__ == "__main__":
|
||||
main()
|
||||
|
|
@ -0,0 +1,17 @@
|
|||
export HF_ENDPOINT=https://hf-mirror.com
|
||||
|
||||
models=("bert" "gpt2")
|
||||
batch_size=(1 32)
|
||||
seq_len=(100 500)
|
||||
nproc=(1 2 4)
|
||||
|
||||
for model in "${models[@]}"; do
|
||||
for bs in "${batch_size[@]}"; do
|
||||
for len in "${seq_len[@]}"; do
|
||||
python -m xacc run_pytorch.py --model "$model" --batch_size "$bs" --length "$len" --export_onnx ../models/"$model" > results/"$model"_"$bs"_"$len"
|
||||
for n in "${nproc[@]}"; do
|
||||
python kunlun_launch.py --name "$model" --model ../models/"$model"/"$model"_"$bs"_"$len".onnx --nproc_per_node=$n >> results/"$model"_"$bs"_"$len"
|
||||
done
|
||||
done
|
||||
done
|
||||
done
|
||||
|
|
@ -5,8 +5,8 @@
|
|||
#include "utils/operator_utils.h"
|
||||
#include <functional>
|
||||
#include <nlohmann/json.hpp>
|
||||
using json = nlohmann::json;
|
||||
namespace infini {
|
||||
using json = nlohmann::json;
|
||||
|
||||
class RuntimeObj; // Forward declaration for Kernel::compute
|
||||
|
||||
|
|
|
|||
|
|
@ -2,8 +2,8 @@
|
|||
#include "core/graph.h"
|
||||
#include "core/kernel.h"
|
||||
#include <nlohmann/json_fwd.hpp>
|
||||
using json = nlohmann::json;
|
||||
namespace infini {
|
||||
using json = nlohmann::json;
|
||||
|
||||
class PerfEngine {
|
||||
public:
|
||||
|
|
|
|||
|
|
@ -20,14 +20,15 @@ class AllReduceXCCL : public KUNLUNKernelWithoutConfig {
|
|||
BKCLContext_t comm =
|
||||
dynamic_cast<XcclCommunicatorObj &>(context->getCommunicator())
|
||||
.getXcclComm();
|
||||
double t = timeit(
|
||||
[&]() {
|
||||
checkXcclError(bkcl_all_reduce(comm, input, output, count,
|
||||
BKCLDataType::BKCL_FLOAT,
|
||||
getRedOp(), 0));
|
||||
},
|
||||
[&]() { context->sync(); });
|
||||
std::cout << "Time consuming for " << op->getInputs(0)->size() << " size is " << t << std::endl;
|
||||
// double t = timeit(
|
||||
// [&]() {
|
||||
checkXcclError(bkcl_all_reduce(comm, input, output, count,
|
||||
BKCLDataType::BKCL_FLOAT, getRedOp(),
|
||||
0));
|
||||
// },
|
||||
// [&]() { context->sync(); });
|
||||
// std::cout << "Time consuming for " << op->getInputs(0)->size() << "
|
||||
// size is " << t << std::endl;
|
||||
}
|
||||
virtual BKCLOp getRedOp() const = 0;
|
||||
};
|
||||
|
|
|
|||
Loading…
Reference in New Issue