diff --git a/Makefile b/Makefile index c0e0c8b7..ecc75da8 100644 --- a/Makefile +++ b/Makefile @@ -40,7 +40,7 @@ endif build: mkdir -p build/$(TYPE) - cd build/$(TYPE) && cmake $(CMAKE_OPT) ../.. && make -j8 + cd build/$(TYPE) && cmake $(CMAKE_OPT) ../.. && make -j64 clean: rm -rf build diff --git a/examples/distributed/__init__.py b/examples/distributed/__init__.py new file mode 100644 index 00000000..e69de29b diff --git a/examples/distributed/bang_launch.py b/examples/distributed/bang/bang_launch.py similarity index 100% rename from examples/distributed/bang_launch.py rename to examples/distributed/bang/bang_launch.py diff --git a/examples/distributed/cuda_launch.py b/examples/distributed/cuda/cuda_launch.py similarity index 100% rename from examples/distributed/cuda_launch.py rename to examples/distributed/cuda/cuda_launch.py diff --git a/examples/distributed/launch_kvcache.py b/examples/distributed/cuda/launch_kvcache.py similarity index 100% rename from examples/distributed/launch_kvcache.py rename to examples/distributed/cuda/launch_kvcache.py diff --git a/examples/distributed/launch_kunlun.py b/examples/distributed/launch_kunlun.py deleted file mode 100644 index e8c1a0ab..00000000 --- a/examples/distributed/launch_kunlun.py +++ /dev/null @@ -1,213 +0,0 @@ -import argparse -import os -import time -import multiprocessing as mp -from pyinfinitensor.onnx import OnnxStub, backend -import onnx -from onnx.external_data_helper import convert_model_to_external_data -from onnx.shape_inference import infer_shapes_path -import numpy as np -from parallel_opt import parallel_model - -st_input_dir = "standard/inputs/" -st_output_dir = "standard/outputs/" - -def parse_args(): - parser = argparse.ArgumentParser(description="launch distributed infinitensor") - parser.add_argument("--num_nodes", type=int, default=1, help="number of nodes") - parser.add_argument( - "--nproc_per_node", type=int, default=2, help="number of processes per node" - ) - parser.add_argument( - "--name", type=str, default="test", help="name of this instance." - ) - parser.add_argument( - "--model", type=str, default="/data1/shared/panzezhong/llama/fp32/my_llama_fp32.sim.onnx", help="path to the ONNX model file." - ) - 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( - "--gen_std", - default=False, - action="store_true", - help="whether to generate the standard results.", - ) - parser.add_argument( - "--run_single", - default=False, - action="store_true", - help="whether run model with single process with standard inputs" - ) - args = parser.parse_args() - print("arg setting: ", args) - return ( - args.num_nodes, - args.nproc_per_node, - args.name, - args.model, - args.batch_size, - args.length, - args.gen_std, - args.run_single - ) - - -def run_model(model, runtime, world_size=1, rank=0, n=10): - stub = OnnxStub(model, runtime) - load_inputs(stub, world_size, rank) - # stub.tune() - stub.run() - # get outputs - time.sleep(0.01) - outputs = next(stub.outputs.values().__iter__()).copyout_numpy() - - # bench - begin = time.time() - for _ in range(n): - stub.run() - end = time.time() - avg_time = (end - begin) / n - print(f"average time: {avg_time}") - return outputs - - - -def run_and_compare(name, model, runtime, world_size=1, rank = 0): - results = np.load(os.path.join(st_output_dir,f"output.npy")) - outputs = run_model(model, runtime, world_size, rank) - print(outputs[:100]) - if np.isnan(outputs).any(): - print("Nan in output") - print("answer argmax:", np.argmax(results)) - print("output argmax:", np.argmax(outputs)) - #np.testing.assert_allclose(outputs, results, rtol=1e-3, atol=1e-3) - getDiff(results, outputs) - - -def start_worker( - name: str, world_size: int, rank: int, local_rank: int, model: onnx.ModelProto -): - dist_name = name + "_dist" - model = parallel_model(model, world_size, rank) - extern_path = f"./{dist_name}_rank{rank}.pb" - if os.path.exists(extern_path): - os.remove(extern_path) - onnx.save_model( - model, - f"./{dist_name}_rank{rank}.onnx", - save_as_external_data=True, - location=extern_path, - ) - infer_shapes_path(f"./{dist_name}_rank{rank}.onnx") - runtime = backend.KUNLUNRuntime(local_rank) - # print("init comm") - runtime.init_comm( - dist_name, - world_size, - rank, - ) - run_and_compare(name, model, runtime, world_size, rank) - - -def start_single(name, model): - runtime = backend.KUNLUNRuntime(0) - run_and_compare(name, model, runtime) - - -def generate_input_output(model): - runtime = backend.KUNLUNRuntime(0) - stub = OnnxStub(model, runtime) - position_id = 0 - for i, (name, tensor) in enumerate(stub.inputs.items()): - input = tensor.copyout_numpy() - if np.issubdtype(input.dtype, np.integer): - if input.size == 1: - # input = np.array([position_id]) - input = np.random.randint(0,2,size=input.shape, dtype=input.dtype) - else: - input = np.random.randint(0,2,size=input.shape, dtype=input.dtype) - elif input.dtype == np.bool_: - input = np.random.randint(0,2,size=input.shape) > 0 - else: - if i == 0: - input = np.ones(input.shape).astype(input.dtype) - position_id = input.shape[-1] - 1 - else: - input = np.random.rand(*input.shape).astype(input.dtype) - tensor.copyin_numpy(input) - np.save(os.path.join(st_input_dir, f"input_{i}"), input) - stub.run() - # print(stub.outputs) - time.sleep(0.01) - output = next(stub.outputs.values().__iter__()).copyout_numpy() - print(output[:100]) - if np.isnan(output).any(): - print("Nan in output") - np.save(os.path.join(st_output_dir, f"output"), output) - - -def load_inputs(stub, world_size=1, rank=0): - for i, (name, tensor) in enumerate(stub.inputs.items()): - input = np.load(os.path.join(st_input_dir, f"input_{i}.npy")) - if all(x == y for x,y in zip(input.shape,tensor.shape())): - tensor.copyin_numpy(input) - else: - tensor.copyin_numpy(np.hsplit(input, world_size)[rank]) - - -def getDiff(base, test): - absolute_diff = np.abs(np.subtract(base, test)) - max_absolute_diff = np.max(absolute_diff) - - baseCopy = base.astype(np.float64).ravel() - testCopy = test.astype(np.float64).ravel() - upValue = np.sum(np.abs(baseCopy - testCopy)) - downValue = np.sum(np.abs(baseCopy)) + np.float64(1e-9) - max_relative_diff = upValue / downValue - print(f"Max absolute difference: {max_absolute_diff}\nMax relative difference: {max_relative_diff}") - - 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() diff --git a/examples/distributed/parallel_opt.py b/examples/distributed/parallel_opt.py index bbb0ac65..25a9a418 100644 --- a/examples/distributed/parallel_opt.py +++ b/examples/distributed/parallel_opt.py @@ -80,6 +80,7 @@ def parallel_model(model: ModelProto, tp_world_size: int = 1, tp_rank: int = 0): def shard_reshape(node: NodeProto): # print("reshape", node.name, node.input[0], place[node.input[0]]) + # import pdb; pdb.set_trace() if not is_sharded(node.input[0]): return in_plc = place[node.input[0]] @@ -110,7 +111,7 @@ def parallel_model(model: ModelProto, tp_world_size: int = 1, tp_rank: int = 0): s_dim = 0 elif in_plc.dim == 2: s_dim = 1 - + # import pdb; pdb.set_trace() assert s_dim != -1 assert out_dims[s_dim] % tp_world_size == 0, out_dims out_dims[s_dim] //= tp_world_size @@ -246,3 +247,7 @@ def parallel_model(model: ModelProto, tp_world_size: int = 1, tp_rank: int = 0): model = helper.make_model(graph) model = onnx.shape_inference.infer_shapes(model) return model + +if __name__ == "__main__": + model = onnx.load("./models/gpt2/gpt2_1_100.onnx") + models = parallel_model(model, 2, 0) \ No newline at end of file diff --git a/pyinfinitensor/src/pyinfinitensor/onnx.py b/pyinfinitensor/src/pyinfinitensor/onnx.py index 522a4813..d959ce60 100644 --- a/pyinfinitensor/src/pyinfinitensor/onnx.py +++ b/pyinfinitensor/src/pyinfinitensor/onnx.py @@ -947,7 +947,7 @@ class OnnxStub: tensors[node.input[0]], tensors.get(node.output[0]), ) - elif node.op_type == "Constant": + elif node.op_type in ["Constant", "ConstantOfShape"]: output_name = node.output[0] attributes = _parse_attribute(node) tensor = attributes["value"] diff --git a/src/kernels/kunlun/all_reduce.cc b/src/kernels/kunlun/all_reduce.cc index ab01d60d..bbbe13a5 100644 --- a/src/kernels/kunlun/all_reduce.cc +++ b/src/kernels/kunlun/all_reduce.cc @@ -20,9 +20,14 @@ class AllReduceXCCL : public KUNLUNKernelWithoutConfig { BKCLContext_t comm = dynamic_cast(context->getCommunicator()) .getXcclComm(); - checkXcclError(bkcl_all_reduce(comm, input, output, count, - BKCLDataType::BKCL_FLOAT, getRedOp(), - 0)); + 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; };