add test scripts for llama2 and 9G models

.gitmodules

@@ -13,6 +13,3 @@
 [submodule "example"]
 	path = examples/NNmodel
 	url = git@github.com:wanghailu0717/NNmodel.git
-[submodule "examples/distributed/onnxsim_large_model"]
-	path = examples/distributed/onnxsim_large_model
-	url = git@github.com:luchangli03/onnxsim_large_model.git

examples/NNmodel

@@ -1 +1 @@
-Subproject commit 51d3105277f3774ed31c02ed4cd11fa92925af77
+Subproject commit b896cec2dba5b8522b141ac4f89eb43074ee1b98

examples/distributed/README.md

@@ -1,7 +1,5 @@
 # 分布式脚本
 
-## 英伟达平台运行方式
-
 #### 1. 运行pytorch模型并生成输入和标准输出，可选择导出onnx
 
 使用 `--export_onnx` 设置导出onnx的目录，默认为当前路径 `./`，不使用这个flag则只进行计算和生成输入输出。
@@ -17,23 +15,3 @@ python run_pytorch.py --model gpt2  --batch_size 1  --length 1 --export_onnx ./
 ```bash
 python cuda_launch.py --model "/XXX/XXX.onnx" --nproc_per_node 4 
 ```
-
-## 寒武纪平台运行方式
-
-**将上述运行脚本 `run_pytorch.py` 以及 `cuda_launch.py` 针对寒武纪平台做了相应的适配，具体见 `run_pytorch_mlu.py` 以及 `bang_launch.py`。**
-
-#### 1. 运行pytorch模型并生成输入和标准输出，可选择导出onnx
-
-使用 `--export_onnx` 设置导出onnx的目录，默认为当前路径 `./`，不使用这个flag则只进行计算和生成输入输出。
-
-```bash
-python run_pytorch_mlu.py --model gpt2  --batch_size 1  --length 1 --export_onnx ./
-```
-
-会在当前目录下生成输入输出文件`test_inputs.npy` 和 `test_results.npy`，目前只支持单一输入输出。
-
-#### 2. 运行InfiniTensor分布式脚本
-
-```bash
-python bang_launch.py --model "/XXX/XXX.onnx" --nproc_per_node 4 
-```

examples/distributed/bang/run_pytorch_mlu.py

@@ -1,249 +0,0 @@
-import argparse
-import torch
-import torch_mlu
-from transformers import BertModel, BertConfig
-from transformers import GPT2Model, GPT2Config
-from transformers import OPTModel, OPTConfig
-from transformers import AlbertModel, AlbertConfig
-from transformers import LlamaModel, LlamaConfig
-import time
-import numpy as np
-import onnx
-import sys
-import os
-from onnx.external_data_helper import convert_model_to_external_data
-from onnxsim import simplify
-
-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", "albert"], 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(
-        "--type", type=str, choices=["fp32", "fp16", "tf32"], required=True, help="model data type"
-    )
-    args = parser.parse_args()
-    print("arg setting: ", args)
-    return (
-        args.model,
-        args.batch_size,
-        args.length,
-        args.export_onnx,
-        args.type
-    )
-
-
-def get_model(modelname):
-    match modelname:
-        case "albert":
-            model = AlbertModel.from_pretrained("albert/albert-base-v2")
-            voc_size = AlbertConfig().vocab_size
-        case "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
-        case "gpt2":
-            model = GPT2Model.from_pretrained("GPT2")
-            voc_size = GPT2Config().vocab_size
-        case "opt":
-            model = OPTModel.from_pretrained("facebook/opt-125m")
-            voc_size = OPTConfig().vocab_size
-        case "llama":
-            model = LlamaModel.from_pretrained("meta-llama/Llama-2-7b-hf")
-            voc_size = LlamaConfig().vocab_size
-        case _:
-            raise KeyError(modelname)
-
-    model = model.eval()
-    return model, voc_size
-
-def run_pytorch(torch_model, voc_size, batchsize, len, dtype="fp32"):
-    data = np.random.randint(0, voc_size, (batchsize, len), dtype=np.int32)
-    os.makedirs(os.path.dirname("./data/"), exist_ok=True)
-    np.save("./data/input_0", data)
-    inputs = torch.from_numpy(data).to("mlu")
-    torch_model = torch_model.to("mlu")
-    if dtype == "fp16":
-        torch_model = torch_model.half()
-
-    n_iter = 20
-    with torch.no_grad():
-        for _ in range(10):
-            outputs = torch_model(inputs)
-    torch.mlu.synchronize()
-    begin = time.time()
-    with torch.no_grad():
-        for _ in range(n_iter):
-            torch.mlu.synchronize()
-            outputs = torch_model(inputs)
-            torch.mlu.synchronize()
-    torch.mlu.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.mlu.memory.empty_cache()
-    np.save("./data/output", np.array(outputs))
-    print("Save input & output into ./data.")
-
-
-def export_onnx(modelname, model, data, path, extern=False, dtype="fp32"):
-    data = data.to("mlu")
-    model = model.to("mlu")
-    if dtype == "fp16":
-        model = model.half()
-    torch.onnx.export(model, data, path, verbose=False, do_constant_folding=True)
-    if modelname != "llama":
-        # use onnxsim to simplify
-        onnx_model = onnx.load(path)
-        onnx_model, check = simplify(onnx_model, skipped_optimizers=['eliminate_duplicate_initializer'])
-        # 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)
-            extern_path = extern_path.split("/")[-1]
-            convert_model_to_external_data(
-                onnx_model,
-                all_tensors_to_one_file=True,
-                location=extern_path,
-                size_threshold=1024,
-                convert_attribute=False,
-            )
-        onnx.save(onnx_model, path)
-    else:
-        # use third party tool to simplify llama
-        # reference: https://github.com/luchangli03/onnxsim_large_model/
-        sys.path.append("onnxsim_large_model")
-        from onnx_utils import set_onnx_input_shape
-        from compress_model import SIZE_1MB, compress_onnx_model, uncompress_onnx_model
-
-        in_model_path = path
-        out_model_path = path
-        if not out_model_path:
-            out_model_path = in_model_path[:-5] + ".sim.onnx"
-        if os.path.isdir(out_model_path):
-            out_model_path = os.path.join(out_model_path, os.path.basename(in_model_path))
-
-        onnx_model = onnx.load(in_model_path)
-        print(f"load model from {in_model_path} success")
-
-        size_th_bytes = 1024 * 1024
-
-        onnx_model, removed_inits = compress_onnx_model(onnx_model, size_th_bytes=size_th_bytes)
-        print(f"compress model success")
-
-        onnx_model = set_onnx_input_shape(onnx_model, "")
-
-        tensor_size_threshold = f"1024KB"
-        skipped_optimizers = []
-        skipped_optimizers.append("eliminate_duplicate_initializer")
-        onnx_model, check = simplify(onnx_model, skipped_optimizers=skipped_optimizers,
-                                    tensor_size_threshold=tensor_size_threshold)
-        if not check:
-            raise ValueError(f"simplify compressed model {in_model_path} failed")
-
-        print(f"simplify model success")
-
-        onnx_model = uncompress_onnx_model(onnx_model, removed_inits)
-        print(f"uncompress model success")
-
-        add_value_info_for_constants(onnx_model)
-
-        onnx.save(onnx_model, out_model_path, save_as_external_data=True)
-
-
-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():
-    torch.backends.mlu.matmul.allow_tf32 = False
-    torch.backends.cnnl.allow_tf32 = False
-    modelname, batchsize, seqlen, export_path, dtype = parse_args()
-    if dtype == "tf32":
-        torch.backends.mlu.matmul.allow_tf32 = True
-    else:
-        os.environ["CAMBRICON_TF32_OVERRIDE"] = "0"
-
-    model, voc_size = get_model(modelname)
-    if export_path is not None:
-        filename = "{}_{}_{}_{}.onnx".format(modelname, batchsize, seqlen, dtype)
-        path = os.path.join(export_path, filename)
-        if not os.path.exists(path):
-            param = torch.zeros((batchsize, seqlen), dtype=torch.int)
-            export_onnx(modelname, model, param, path, True, dtype)
-        else:
-            print("Onnx path exists, skipping export.")
-
-    run_pytorch(model, voc_size, batchsize, seqlen, dtype)
-
-if __name__ == "__main__":
-    main()

examples/distributed/bang_launch.py

@@ -1,39 +1,35 @@
-import sys
-sys.path.append('../')
-
 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
 
+
 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=1, help="number of processes per node"
+        "--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, required=True, help="path to the ONNX model file."
+        "--model", type=str, default="/data/onnx_models/llama2/llama_bs1_seq1024.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(
-        "--type", type=str, choices=["fp32", "fp16", "tf32"], default="fp32", help="data type"
-    )
     args = parser.parse_args()
     print("arg setting: ", args)
     return (
@@ -44,46 +40,39 @@ def parse_args():
         args.batch_size,
         args.length,
         args.gen_std,
-        args.type,
     )
 
 
-def run_model(model, runtime, world_size=1, rank=0, n=10, data_type="default"):
-    stub = OnnxStub(model, runtime, matmul_compute_type=data_type)
+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()
-    begin = time.time()
-    for _ in range(n * 2):
-        stub.run()
     end = time.time()
-    avg_time = (end - begin) / (n * 2)
+    avg_time = (end - begin) / n
     print(f"average time: {avg_time}")
     return outputs
 
-def load_inputs(stub, world_size=1, rank=0):
-    for i, (name, tensor) in enumerate(stub.inputs.items()):
-        input = np.load(f"./data/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 run_and_compare(name, model, runtime, world_size=1, rank=0, data_type="default"):
+def run_and_compare(name, model, runtime, world_size=1, rank = 0):
     results = np.load(f"./data/output.npy")
-    outputs = run_model(model, runtime, world_size, rank, data_type=data_type)
-    print("outputs abs mean:", abs(outputs).mean())
-    print("max abs diff:", abs(outputs - results).max())
+    outputs = run_model(model, runtime, world_size, rank)
+    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, data_type: str
+    name: str, world_size: int, rank: int, local_rank: int, model: onnx.ModelProto
 ):
     dist_name = name + "_dist"
     model = parallel_model(model, world_size, rank)
@@ -96,7 +85,7 @@ def start_worker(
         save_as_external_data=True,
         location=extern_path,
     )
-    #infer_shapes_path(f"./{dist_name}_rank{rank}.onnx")
+    infer_shapes_path(f"./{dist_name}_rank{rank}.onnx")
     runtime = backend.BangRuntime(local_rank)
     # print("init comm")
     runtime.init_comm(
@@ -104,12 +93,13 @@ def start_worker(
         world_size,
         rank,
     )
-    run_and_compare(name, model, runtime, world_size, rank, data_type)
+    run_and_compare(name, model, runtime, world_size, rank)
 
 
-def start_single(name, model, data_type):
+def start_single(name, model):
     runtime = backend.BangRuntime(0)
-    run_and_compare(name, model, runtime, data_type=data_type)
+    run_and_compare(name, model, runtime)
+
 
 def generate_input_output(model):
     os.makedirs(os.path.dirname("./data/"), exist_ok=True)
@@ -142,36 +132,55 @@ def generate_input_output(model):
     np.save(f"./data/output", output)
 
 
+def load_inputs(stub, world_size=1, rank=0):
+    for i, (name, tensor) in enumerate(stub.inputs.items()):
+        input = np.load(f"./data/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}\n"
+          f"Max 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, data_type = parse_args()
-    data_type = "default" if data_type == "fp32" else data_type
-    
+    nnodes, nproc_per_node, name, model_path, bs, length, gen_std = parse_args()
+
     model = onnx.load(model_path)
 
     # generate standart output
     if gen_std:
-        print(f"generate standard data for {name}.")
-        # a small vocabulary size to fit all LLM.
-        generate_input_output(model)
+        print("Generate inputs and outputs.")
+        p = mp.Process(target=generate_input_output, args=[model])
+        p.start()
+        p.join()
         return
 
-    if nproc_per_node == 1:
-        # run single process.
-        # use standalone process to isolate bang.
-        print("run model by single MLU.")
-        # p = mp.Process(target=start_single, args=(name, model, data_type))
-        # p.start()
-        # p.join()
-        start_single(name, model, data_type)
-        return
+    # run single process.
+    # use standalone process to isolate cuda.
+    print("run model by single MLU.")
+    p = mp.Process(target=start_single, args=(name, model))
+    p.start()
+    p.join()
 
     # run distributed parallel.
     world_size = nnodes * nproc_per_node
-    print(f"run model by {world_size} MLU in parallel.")
+    print(f"run model by {world_size} MLUs in parallel.")
     workers = [
         mp.Process(
             target=start_worker,
-            args=(name, world_size, rank, rank % nproc_per_node, model, data_type),
+            args=(name, world_size, rank, rank % nproc_per_node, model),
         )
         for rank in range(world_size)
     ]

examples/distributed/kunlun/export_onnx.sh

@@ -1,14 +0,0 @@
- export HF_ENDPOINT=https://hf-mirror.com
-
-models=("bert" "gpt2" "llama")
-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 "${seq_len[@]}"; do
-            python run_pytorch.py --model "$model" --batch_size "$bs" --length "$len" --export_onnx ../models/"$model" --export_only 
-        done
-    done
-done 

examples/distributed/kunlun/kunlun_launch.py

@@ -1,280 +0,0 @@
-import sys
-sys.path.append('../')
-
-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
-from functools import wraps
-
-
-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, choices=["gpt2", "bert", "llama"], help="name of model."
-    )
-    parser.add_argument(
-        "--model", type=str, default="", help="path to the ONNX model file."
-    )
-    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"
-    )
-    parser.add_argument(
-        "--input_dir",
-        default="./",
-        help="path to save model input data"
-    )
-    parser.add_argument(
-        "--result_dir",
-        default="./",
-        help="path to save model standard output"
-    )
-    parser.add_argument(
-        "--internal_model_dir",
-        default="./",
-        help="path to save internal onnx model for parallel run"
-    )
-    args = parser.parse_args()
-
-    # check path, mkdir if not exist
-    check_exists(args.input_dir)
-    check_exists(args.result_dir)
-    check_exists(args.internal_model_dir)
-
-    print("arg setting: ", args)
-    return (
-        args.num_nodes,
-        args.nproc_per_node,
-        args.name,
-        args.model,
-        args.gen_std,
-        args.run_single,
-        args.input_dir,
-        args.result_dir,
-        args.internal_model_dir
-    )
-
-
-"""
-utils function for this scripts
-"""
-def check_exists(path: str):
-    if not os.path.exists(path):
-        os.makedirs(path)
-
-def np_assert(base, test, rtol=1e-2, atol=1e-1):
-    # np.testing.assert_allclose(test, base, rtol, atol)
-    print("max abs diff:", abs(base - test).max())
-
-
-"""
-Perf wrapper, run function n times
-then average
-"""
-def perf_it(n):
-    def decorator(func):
-        @wraps(func)
-        def wrapper(*args, **kwargs):
-            # warmup
-            for _ in range(n):
-                func(*args, **kwargs)
-
-            t_total = 0
-            for _ in range(n):
-                t0 = time.time()
-                func(*args, **kwargs)
-                t1 = time.time()
-                t_total += t1 - t0
-            avg_time = (t_total) / n
-            print(f"Avg runtime of {n} time is {avg_time:.6f} seconds")
-            return avg_time
-        return wrapper
-    return decorator
-
-
-"""
-Run InfiniTensor model with Standard input
-check=True: check with standard output gen by pytorch
-perf=True: run n times to get avg time
-"""
-def run_model(task_name,
-              model,
-              runtime,
-              world_size=1,
-              rank=0,
-              n=10,
-              check=True,
-              perf=True):
-
-    stub = OnnxStub(model, runtime,
-                    use_naive_allocator=True \
-                    if task_name == "llama" else False)
-
-    # load in Onnx model inputs
-    def load_inputs(stub: OnnxStub):
-        # check exists
-        inputs = []
-        for i, (name, tensor) in enumerate(stub.inputs.items()):
-            input_path = os.path.join(input_dir, \
-                                f"{task_name}_input_{i}.npy")
-            print(input_path)
-            if os.path.exists(input_path):
-                input = np.load(input_path)
-            else :
-                raise KeyError(f"{i} th input of model not exists")
-            # check shape
-            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])
-
-    load_inputs(stub)
-    # stub.tune()
-    stub.run()
-    time.sleep(0.01)
-    output = next(stub.outputs.values().__iter__()).copyout_numpy()
-
-    # check output results with standard output
-    if check:
-        st_output_path = os.path.join(result_dir, \
-                                f"{task_name}_output.npy")
-        assert os.path.exists(st_output_path) , \
-                    "standard output not exists"
-        st_output = np.load(st_output_path)
-        if np.isnan(output).any():
-            print("Nan in output")
-            exit()
-        np_assert(st_output, output)
-
-    # perf
-    if perf:
-        @perf_it(n)
-        def perf_infinitensor(stub: OnnxStub):
-            stub.run()
-        perf_infinitensor(stub)
-
-    return output
-
-
-"""
-Start a worker in Parallel
-"""
-def start_worker(name: str,
-           world_size: int,
-           rank: int,
-           local_rank: int,
-           model: onnx.ModelProto):
-
-    dist_name = name + "_dist"
-    # partial a onnx model to world_size part
-    model = parallel_model(model, world_size, rank)
-    onnx.save(model, os.path.join(internal_model_dir, \
-                                    f"{dist_name}_rank{rank}.onnx"), save_as_external_data=True)
-    runtime = backend.KUNLUNRuntime(local_rank)
-    # print("init comm")
-    runtime.init_comm(
-        dist_name,
-        world_size,
-        rank,
-    )
-    run_model(name, model, runtime, world_size, rank)
-
-
-"""
-generate standard input/output with
-sigle card run
-"""
-def gen_standard(task_name: str, model: onnx.ModelProto):
-    runtime = backend.KUNLUNRuntime(0)
-    stub = OnnxStub(model, runtime)
-    position_id = 0
-    # generate random input for model
-    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.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(input_dir, \
-                    f"{task_name}_input_{i}.npy"), input)
-    stub.run()
-    # print(stub.outputs)
-    output = next(stub.outputs.values().__iter__()).copyout_numpy()
-    if np.isnan(output).any():
-        print("Nan in output")
-        exit()
-    np.save(os.path.join(result_dir, f"{task_name}_output.npy"), output)
-
-
-def main():
-
-    global input_dir, result_dir, internal_model_dir
-
-    nnodes, nproc_per_node, task_name, \
-        model_path, gen_std, run_single, \
-            input_dir, result_dir, internal_model_dir = parse_args()
-
-    # load input onnx model
-    model = onnx.load(model_path)
-
-    # generate standart output
-    if gen_std:
-        print("Generate inputs and outputs.")
-        gen_standard(task_name, model)
-        return
-
-    if run_single:
-        print("Run model by one GPU card.")
-        runtime = backend.KUNLUNRuntime(0)
-        run_model(task_name, model, runtime)
-        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=(task_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()

examples/distributed/kunlun/launch.sh

@@ -1,36 +0,0 @@
-export HF_ENDPOINT=https://hf-mirror.com
-
-# models=("bert" "gpt2" "llama")
-models=("bert" "gpt2")
-batch_size=(1 32)
-seq_len=(100 500)
-nproc=(1 2 4)
-
-results_dir="results"
-
-if [ -d "$results_dir" ]; then
-    echo "directory ./$results_dir exists"
-else
-    mkdir -p "$results_dir"
-    echo "mkdir $results_dir, logs saved there"
-fi
-
-
-for model in "${models[@]}"; do
-    for bs in "${batch_size[@]}"; do
-        for len in "${seq_len[@]}"; do
-            # run pytorch model
-            echo "Run pytorch $model with batch_size=$bs length=$len ."
-            python run_pytorch.py --model "$model" --batch_size "$bs" --length "$len" #> results/"$model"_"$bs"_"$len"_pytorch
-            for n in "${nproc[@]}"; do
-                # run infinitensor 
-                echo "Run $n parallel infinitensor "$model" with batch_size=$bs and length=$len ."
-                python kunlun_launch.py --name "$model" --model ../models/"$model"/"$model"_"$bs"_"$len".onnx --nproc_per_node=$n # >> results/"$model"_"$bs"_"$len"_infini 
-                # delete internal files
-                find ./ -type f -name "*.onnx" -delete
-                find ./ -type f -name "*.pb" -delete
-            done
-            find ./ -type f -name "*.npy" -delete
-        done
-    done
-done

examples/distributed/kunlun/llama_launch.sh

@@ -1,35 +0,0 @@
-export HF_ENDPOINT=https://hf-mirror.com
-
-# models=("bert" "gpt2" "llama")
-models=("llama")
-batch_size=(1 )
-seq_len=(100 500)
-nproc=(1 2 4)
-
-results_dir="results"
-
-if [ -d "$results_dir" ]; then
-    echo "directory ./$results_dir exists"
-else
-    mkdir -p "$results_dir"
-    echo "mkdir $results_dir, logs saved there"
-fi
-
-
-for model in "${models[@]}"; do
-    for bs in "${batch_size[@]}"; do
-        for len in "${seq_len[@]}"; do
-            echo "Run pytorch llama with batch_size="$bs" and length="$len""
-            python run_pytorch.py --model "$model" --batch_size "$bs" --length "$len"
-            for n in "${nproc[@]}"; do
-                    # run pytorch model
-                    echo "Run infinitensor llama with batch_size="$bs" and length="$len" and nproc="$n"."
-                    python kunlun_launch.py --name llama --model ../models/llama/llama_"$bs"_"$len"_fp32.onnx --nproc_per_node=$n
-                    # delete internal files
-                    find ./ -type f -name "*.onnx" -delete
-                    find ./ -type f -name "*0c" -delete
-            done
-            find ./ -type f -name "*.npy" -delete
-        done
-    done
-done

examples/distributed/kunlun/run_pytorch.py

@@ -1,245 +0,0 @@
-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
-import sys
-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"
-    )
-    parser.add_argument(
-        "--export_only",
-        action="store_true"
-    )
-    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,
-        args.export_only
-    )
-
-
-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 = 10
-    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)
-
-    if model_name != "llama":
-        onnx_model = onnx.load(path)
-        onnx_model, check = simplify(onnx_model,
-                                 skipped_optimizers=['fuse_qkv', 'eliminate_duplicate_initializer'])
-                                 # skipped_optimizers=['fuse_qkv'])
-        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)
-    else:
-        sys.path.append("onnxsim_large_model")
-        from onnx_utils import set_onnx_input_shape
-        from compress_model import SIZE_1MB, compress_onnx_model, uncompress_onnx_model
-
-        in_model_path = path
-        out_model_path = in_model_path[:-5] + ".sim.onnx"
-
-        onnx_model = onnx.load(in_model_path)
-        print(f"load model from {in_model_path} success")
-
-        size_th_bytes = 1024 * 1024
-        onnx_model, removed_inits = compress_onnx_model(onnx_model, size_th_bytes=size_th_bytes)
-        print("compress model success")
-
-        onnx_model = set_onnx_input_shape(onnx_model, "")
-        tensor_size_threshold = f"1024KB"
-        skipped_optimizers = []
-        skipped_optimizers.append("eliminate_duplicate_initializer")
-        onnx_model, check = simplify(onnx_model, skipped_optimizers=skipped_optimizers,
-                                    tensor_size_threshold=tensor_size_threshold)
-        if not check:
-            raise ValueError(f"simplify compressed model {in_model_path} failed")
-
-        print(f"simplify model success")
-
-        onnx_model = uncompress_onnx_model(onnx_model, removed_inits)
-        print(f"uncompress model success")
-
-        add_value_info_for_constants(onnx_model)
-
-        onnx.save(onnx_model, out_model_path, save_as_external_data=True)
-
-
-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, export_only = parse_args()
-
-    model, voc_size = get_model(modelname) # pytorch model
-
-    if export_path is not None:
-        os.makedirs(export_path, exist_ok=True)
-        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
-        if export_only:
-            return
-
-    run_pytorch(model, voc_size, batchsize, seqlen, modelname)
-
-if __name__ == "__main__":
-    main()

examples/distributed/launch_kunlun.py

@@ -0,0 +1,213 @@
+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()

examples/distributed/onnxsim_large_model

@@ -1 +0,0 @@
-Subproject commit cbcf3fbf985a00494b0f136c92eaccd42031bf65

examples/distributed/parallel_opt.py

@@ -110,6 +110,7 @@ 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
+
         assert s_dim != -1
         assert out_dims[s_dim] % tp_world_size == 0, out_dims
         out_dims[s_dim] //= tp_world_size

examples/python/test_9G.py

@@ -0,0 +1,512 @@
+import os
+from pyinfinitensor.onnx import OnnxStub, backend
+import numpy as np
+import onnx
+import torch
+from tqdm import tqdm
+import onnx_graphsurgeon as gs
+import time
+import nvtx
+import argparse
+from mpi4py import MPI
+from pytrie import StringTrie
+import io
+import json
+import re
+from typing import (
+    Dict,
+    List,
+    IO,
+)
+
+parser = argparse.ArgumentParser(description='')
+parser.add_argument('--batchsize', dest='batchsize', type=int, default=1)
+parser.add_argument('--layer', dest='n_layers', type=int, default=48)
+parser.add_argument("--num_nodes", dest='num_nodes',
+                    type=int, default=1, help="number of nodes")
+parser.add_argument("--world_size", dest="world_size",
+                    type=int, default=1, help="")
+parser.add_argument("--nproc_per_node", dest="nproc_per_node",
+                    type=int, default=1, help="number of processes per node")
+parser.add_argument("--n_max_length", dest="n_max_length",
+                    type=int, default=1024, help="number of processes per node")
+parser.add_argument("--vocab_size", dest="vocab_size",
+                    type=int, default=119696, help="vocabulary size")
+parser.add_argument("--hidden_size", dest="hidden_size",
+                    type=int, default=4096, help="vocabulary size")
+parser.add_argument('--rank', dest='rank', type=int, default=0)
+parser.add_argument('--speedup', action='store_true')
+parser.add_argument('--no_cudagraph', action='store_true')
+parser.add_argument('--fp16', action='store_true')
+args = parser.parse_args()
+comm = MPI.COMM_WORLD
+args.rank = comm.Get_rank()
+args.nproc_per_node = comm.Get_size()
+args.world_size = args.num_nodes * args.nproc_per_node
+
+ONNX_MODEL_PATH = "/data3/shared/xnsong/9G/dist/9g_dist_bs{}_layer{}_fp{}_worldsize{}_rank{}.onnx".format(
+    args.batchsize, args.n_layers, 16 if args.fp16 else 32, args.world_size, args.rank)
+
+weight_path = "9g_dist_bs{}_layer{}_fp{}_worldsize{}_rank{}.pb".format(
+    args.batchsize, args.n_layers, 16 if args.fp16 else 32, args.world_size, args.rank)
+
+model_dir = "/data1/shared/9G-Infer/models/11B-Chat-QY-epoch-8/cpm9g-11b-sft.pt"
+
+@gs.Graph.register()
+def RMSNorm(self, a, b):
+    return self.layer(op="RMSNorm", inputs=a, outputs=b)
+
+@gs.Graph.register()
+def RoPE(self, a, b):
+    return self.layer(op="RoPE", inputs=a, outputs=b)
+
+@gs.Graph.register()
+def AttentionKVCache(self, a, b):
+    return self.layer(op="AttentionKVCache", inputs=a, outputs=b)
+
+def to_numpy(dict):
+    ret = dict
+    if args.fp16:
+        ret = np.float16(ret)
+    else:
+        ret = np.float32(ret)
+    return ret
+
+def parallel(array, split='replicate'):
+    if args.world_size > 1 and split == 'partial_column':
+        return np.hsplit(array, args.world_size)[args.rank]
+    elif args.world_size > 1 and split == 'partial_row':
+        return np.vsplit(array, args.world_size)[args.rank]
+    return array
+
+
+def generate_onnx(ONNX_MODEL_PATH):
+    state_dict = torch.load(f'{model_dir}', map_location='cpu')
+    new_state_dict = {name: param.cpu().numpy()
+        for name, param in state_dict.items()
+    }
+
+    operators = []
+    graph = gs.Graph(nodes=operators)
+    gather_input = gs.Variable(name="gather_input.0", dtype=np.int64, shape=(1,1))
+    pos_input = gs.Variable(name="pos_input.0", dtype=np.int64, shape=(1,1))
+
+    embedding_weight = gs.Constant(name="embedding.weight", values=to_numpy(new_state_dict["input_embedding.weight"]))
+    gather_output = gs.Variable(name="gather_output.0") 
+    gather = gs.Node(op="Gather", inputs=[embedding_weight, gather_input], outputs=[gather_output])
+    operators.append(gather)
+    input = gather_output
+    
+    graph.inputs=[gather_input, pos_input]
+    graph.outputs=[]
+
+    for i in tqdm(range(args.n_layers)):
+        # global input
+        attn_kcache_input = gs.Variable(name="/layers." + str(i) + "/attn/kcache_input", dtype=np.float32, shape=(1,32,1023,128))
+        attn_vcache_input = gs.Variable(name="/layers." + str(i) + "/attn/vcache_input", dtype=np.float32, shape=(1,32,1023,128))
+        graph.inputs.append(attn_kcache_input)
+        graph.inputs.append(attn_vcache_input)
+
+        # weight
+        layernorm_0_mul_weight = gs.Constant(name="/layers." + str(i) + "/layernorm.0/mul_weight", 
+                                             values=to_numpy(new_state_dict["encoder.layers." + str(i) + ".self_att.layernorm_before_attention.weight"]))
+        attn_qproj_weight = gs.Constant(name="/layers." + str(i) + "/attn/qproj_weight", 
+                                        values=parallel(
+                                            np.transpose(
+                                                to_numpy(
+                                                    new_state_dict["encoder.layers." + str(i) + ".self_att.self_attention.project_q.weight"]))
+                                            , 'partial_column'))
+        attn_kproj_weight = gs.Constant(name="/layers." + str(i) + "/attn/kproj_weight", 
+                                        values=parallel(
+                                            np.transpose(
+                                                to_numpy(
+                                                    new_state_dict["encoder.layers." + str(i) + ".self_att.self_attention.project_k.weight"]))
+                                            , 'partial_column'))
+        attn_vproj_weight = gs.Constant(name="/layers." + str(i) + "/attn/vproj_weight", 
+                                        values=parallel(
+                                            np.transpose(
+                                                to_numpy(
+                                                    new_state_dict["encoder.layers." + str(i) + ".self_att.self_attention.project_v.weight"]))
+                                            , 'partial_column'))
+        attn_outmatmul_input = gs.Constant(name="/layers." + str(i) + "/attn/outmatmul_weight", 
+                                           values=parallel(
+                                               np.transpose(
+                                                   to_numpy(
+                                                       new_state_dict["encoder.layers." + str(i) + ".self_att.self_attention.attention_out.weight"]))
+                                                , 'partial_row'))
+        
+        layernorm_1_mul_weight = gs.Constant(name="/layers." + str(i) + "/layernorm.1/mul_weight", 
+                                             values=to_numpy(new_state_dict["encoder.layers." + str(i) + ".ffn.layernorm_before_ffn.weight"]))
+        ffn_matmul_0_input = gs.Constant(name="/layers." + str(i) + "/ffn/matmul_0_weight", 
+                                         values=parallel(
+                                             np.transpose(
+                                                 to_numpy(
+                                                     new_state_dict["encoder.layers." + str(i) + ".ffn.ffn.w_in.w_0.weight"]))
+                                             , 'partial_column'))
+        ffn_matmul_1_input = gs.Constant(name="/layers." + str(i) + "/ffn/matmul_1_weight", 
+                                         values=parallel(
+                                             np.transpose(
+                                                 to_numpy(
+                                                     new_state_dict["encoder.layers." + str(i) + ".ffn.ffn.w_in.w_1.weight"]))
+                                             , 'partial_column'))
+        ffn_matmul_out_input = gs.Constant(name="/layers." + str(i) + "/ffn/matmul_out_weight", 
+                                           values=parallel(
+                                               np.transpose(
+                                                   to_numpy(
+                                                       new_state_dict["encoder.layers." + str(i) + ".ffn.ffn.w_out.weight"]))
+                                                , 'partial_row'))
+        
+        attn_qrope_output = gs.Variable(name="/layers." + str(i) + "/attn/qrope_output")
+        attn_krope_output = gs.Variable(name="/layers." + str(i) + "/attn/krope_output")
+        attn_kvcache_output = gs.Variable(name="/layers." + str(i) + "/attn/kvcache_output")
+        layernorm_0_mul_output_1 = gs.Variable(name="/layers." + str(i) + "/layernorm.0/mul_output_1")
+        layernorm_1_mul_output_1 = gs.Variable(name="/layers." + str(i) + "/layernorm.1/mul_output_1")
+        attn_qproj_output = gs.Variable(name="/layers." + str(i) + "/attn/qproj_output")
+        attn_kproj_output = gs.Variable(name="/layers." + str(i) + "/attn/kproj_output")
+        attn_vproj_output = gs.Variable(name="/layers." + str(i) + "/attn/vproj_output")
+        attn_outmatmul_output = gs.Variable(name="/layers." + str(i) + "/attn/outmatmul_output")
+        attn_outadd_output = gs.Variable(name="/layers." + str(i) + "/attn/outadd_output")
+        ffn_matmul_0_output = gs.Variable(name="/layers." + str(i) + "/ffn/matmul_0_output")
+        ffn_silu_output = gs.Variable(name="/layers." + str(i) + "/ffn/silu_output")
+        ffn_matmul_1_output = gs.Variable(name="/layers." + str(i) + "/ffn/matmul_1_output")
+        ffn_mul_output = gs.Variable(name="/layers." + str(i) + "/ffn/mul_output")
+        ffn_matmul_out_output = gs.Variable(name="/layers." + str(i) + "/ffn/matmul_out_output")
+        ffn_add_output = gs.Variable(name="/layers." + str(i) + "/ffn/add_output")
+
+        graph.RMSNorm([input, layernorm_0_mul_weight], [layernorm_0_mul_output_1])
+        attn_qproj = gs.Node(op="MatMul", inputs=[layernorm_0_mul_output_1, attn_qproj_weight], outputs=[attn_qproj_output])
+        operators.append(attn_qproj)
+        attn_kproj = gs.Node(op="MatMul", inputs=[layernorm_0_mul_output_1, attn_kproj_weight], outputs=[attn_kproj_output])
+        operators.append(attn_kproj)
+        attn_vproj = gs.Node(op="MatMul", inputs=[layernorm_0_mul_output_1, attn_vproj_weight], outputs=[attn_vproj_output])
+        operators.append(attn_vproj)
+        graph.RoPE([pos_input, attn_qproj_output], [attn_qrope_output])
+        graph.RoPE([pos_input, attn_kproj_output], [attn_krope_output])
+        graph.AttentionKVCache([attn_kcache_input, attn_vcache_input, attn_qrope_output, attn_krope_output, attn_vproj_output, pos_input],[attn_kvcache_output])
+        attn_outproj = gs.Node(op="MatMul", inputs=[attn_kvcache_output, attn_outmatmul_input], outputs=[attn_outmatmul_output])
+        operators.append(attn_outproj)
+
+        attn_reduce_sum_output = gs.Variable(name="/layers." + str(i) + "/attn/reducesum_output")
+        if args.world_size > 1:
+            reduce_sum = gs.Node(op="ReduceSum", name="/layers." + str(i) + "/attn/reducesum",
+                                    inputs=[attn_outmatmul_output], outputs=[attn_reduce_sum_output], 
+                                    attrs={"noop_with_empty_axes":1, "communicator":0})
+            graph.nodes.append(reduce_sum)
+        
+        attn_outadd = gs.Node(op="Add", inputs=[input, attn_outmatmul_output if args.world_size == 1 else attn_reduce_sum_output], outputs=[attn_outadd_output])
+        operators.append(attn_outadd)
+
+        graph.RMSNorm([attn_outadd_output, layernorm_1_mul_weight], [layernorm_1_mul_output_1])
+
+        ffn_matmul_0 = gs.Node(op="MatMul", inputs=[layernorm_1_mul_output_1, ffn_matmul_0_input], outputs=[ffn_matmul_0_output])
+        operators.append(ffn_matmul_0)
+        ffn_silu = gs.Node(op="Silu", inputs=[ffn_matmul_0_output], outputs=[ffn_silu_output])
+        operators.append(ffn_silu)
+        ffn_matmul_1 = gs.Node(op="MatMul", inputs=[layernorm_1_mul_output_1, ffn_matmul_1_input], outputs=[ffn_matmul_1_output])
+        operators.append(ffn_matmul_1)
+        ffn_mul = gs.Node(op="Mul", inputs=[ffn_silu_output, ffn_matmul_1_output], outputs=[ffn_mul_output])
+        operators.append(ffn_mul)
+        ffn_matmul_out = gs.Node(op="MatMul", inputs=[ffn_mul_output, ffn_matmul_out_input], outputs=[ffn_matmul_out_output])
+        operators.append(ffn_matmul_out)
+
+        ffn_reduce_sum_output = gs.Variable(name="/layers." + str(i) + "/ffn/reducesum_output")
+        if args.world_size > 1:
+            reduce_sum = gs.Node(op="ReduceSum", name="/layers." + str(i) + "/ffn/reducesum",
+                                    inputs=[ffn_matmul_out_output], outputs=[ffn_reduce_sum_output], 
+                                    attrs={"noop_with_empty_axes":1, "communicator":0})
+            graph.nodes.append(reduce_sum)
+
+        ffn_add = gs.Node(op="Add", inputs=[attn_outadd_output, ffn_matmul_out_output if args.world_size == 1 else ffn_reduce_sum_output], outputs=[ffn_add_output])
+        operators.append(ffn_add)
+        input = ffn_add_output
+
+    layernorm_mul_weight = gs.Constant(name="/output/layernorm/mul_weight", values=to_numpy(new_state_dict["encoder.output_layernorm.weight"]))
+    layernorm_mul_output_1 = gs.Variable(name="/output/layernorm/mul_output_1")
+
+    graph.RMSNorm([input, layernorm_mul_weight], [layernorm_mul_output_1])
+
+    lm_head_weight = gs.Constant(name="/output/lm_head/weight", values=np.transpose(to_numpy(new_state_dict["lm_head.weight"])))
+    lm_head_output = gs.Variable(name="/output/lm_head/output") 
+    lm_head = gs.Node(op="MatMul", inputs=[layernorm_mul_output_1, lm_head_weight], outputs=[lm_head_output])
+    operators.append(lm_head)
+
+    if args.fp16:
+        final_cast_output = gs.Variable(name="/output/cast/output", dtype=np.float32, shape=(1,1,args.vocab_size))
+        final_cast = gs.Node(op="Cast", inputs=[lm_head_output], outputs=[final_cast_output])
+        final_cast.attrs["to"] = np.float32
+        operators.append(final_cast)
+        graph.outputs.append(final_cast_output)
+    else:
+        lm_head_output.dtype=np.float32
+        lm_head_output.shape=(1,1,args.vocab_size)
+        graph.outputs.append(lm_head_output)
+
+    onnx.save(gs.export_onnx(graph), ONNX_MODEL_PATH, save_as_external_data=True, location=weight_path) 
+    return
+
+
+def load_vocab(fp: IO[bytes]) -> Dict[str, int]:
+    """Loads a vocabulary file into a dictionary."""
+    vocab: Dict[str, int] = {}
+
+    reader = io.TextIOWrapper(fp, encoding="utf-8")
+    for token in reader.readlines():
+        token = token.strip()
+        if len(token) == 0:
+            continue
+        token = json.loads(token)
+        vocab[token] = len(vocab)
+    return vocab
+
+
+class CPM9GTokenizer(object):
+    def __init__(self, path):
+        self.unk_token = "<unk>"
+        self.bos_token = "<s>"
+        self.eos_token = "</s>"
+        self.byte_list = ["<0x0{}>".format(hex(i).upper()[2:]) for i in range(0x10)] + [
+            "<0x{}>".format(hex(i).upper()[2:]) for i in range(0x10, 0x100)
+        ]
+
+        self._special_token_set = set([self.unk_token, self.bos_token, self.eos_token] + self.byte_list)
+
+        all_tokens = load_vocab(io.FileIO(path, "rb"))
+
+        self.encoder: Dict[str, int] = {}
+        self._special_encoder: Dict[str, int] = {}
+        for token, token_id in all_tokens.items():
+            if token in self._special_token_set:
+                self._special_encoder[token] = token_id
+            else:
+                self.encoder[token] = token_id
+
+        self.decoder = {v: k for k, v in self.encoder.items()}
+        self._byte_decoder = {self._special_encoder[token]: i for i, token in enumerate(self.byte_list)}
+
+        self._max_word_len = max([len(x) for x in self.encoder.keys()])
+
+        self._len_word_first = {}
+        for x in self.encoder.keys():
+            if not x[0] in self._len_word_first:
+                self._len_word_first[x[0]] = 1
+            if len(x) > self._len_word_first[x[0]]:
+                self._len_word_first[x[0]] = len(x)
+        self.tencoder = StringTrie(self.encoder)
+
+    def get_piece(self, text: str) -> str:
+        if text[0] in self._len_word_first:
+            text = text[: self._len_word_first[text[0]]]
+            len_text = len(text)
+            for i in range(len(text)):
+                sub = text[: len_text - i]
+                if sub in self.encoder:
+                    return sub
+        return text[0]
+
+    @property
+    def vocab_size(self):
+        return len(self)
+
+    @property
+    def eos_id(self):
+        return self._special_encoder[self.eos_token]
+
+    @property
+    def bos_id(self):
+        return self._special_encoder[self.bos_token]
+
+    @property
+    def unk_id(self):
+        return self._special_encoder[self.unk_token]
+
+    def __len__(self):
+        return len(self.encoder) + len(self._special_encoder)
+
+    def tokenize(self, text: str) -> List[str]:
+        output_tokens: List[str] = []
+        st = 0
+        while st < len(text):
+            piece = self.get_piece(text[st:])
+            output_tokens.append(piece)
+            st += len(piece)
+        return output_tokens
+
+    @staticmethod
+    def escape(text: str) -> str:
+        return text
+
+    @staticmethod
+    def unescape(text: str) -> str:
+        return text
+
+    def encode(self, text: str, with_bos = True) -> List[int]:
+        ret = []
+        if with_bos:
+            ret.append(self.bos_id)
+        for x in self.tokenize(text):
+            if x in self.encoder:
+                ret.append(self.encoder[x])
+            else:
+                ret.extend(self._encode_unicode(x))
+        return ret
+
+    def decode(self, tokens: List[int]):
+        """Decode ids into a string."""
+        ret = []
+        st = 0
+        while st < len(tokens):
+            if tokens[st] in self.decoder:
+                ret.append(self.decoder[tokens[st]])
+                st += 1
+            elif tokens[st] in self._byte_decoder:
+                first = self._byte_decoder[tokens[st]]
+                length = 1 if first < 128 else len(re.search('^1+0', bin(first)[2:])[0])-1
+                code = 0
+                try:
+                    for j in range(length):
+                        code = code << 8 | self._byte_decoder[tokens[st + j]]
+                    code = int.to_bytes(code, length, "big").decode("utf-8")
+                    ret.append(code)
+                except:
+                    pass
+                st = st + length
+            elif tokens[st] == self.eos_id:
+                ret.append(self.eos_token)
+                st += 1
+            elif tokens[st] == self.bos_id:
+                ret.append(self.bos_token)
+                st += 1
+            else:
+                ret.append(self.unk_token)
+                st += 1
+        return "".join(ret)
+
+    def _encode_unicode(self, token):
+        # wrap unicode encoding into a helper function
+        ids = []
+        utf8_id = token.encode("utf-8")
+        for _id in utf8_id:
+            ids.append(self._special_encoder[self.byte_list[_id]])
+        return ids
+
+    def next_token(self, text):
+        # fast next token matching
+        token, token_id = self.tencoder.longest_prefix_item(text, (None, None))
+        if token is None:
+            token = text[0]
+            token_ids = self._encode_unicode(token)
+        else:
+            token_ids = [token_id]
+        return token, token_ids
+
+
+def start_worker(
+    world_size: int, rank: int, local_rank: int, model: onnx.ModelProto, query
+):
+    model = onnx.load(ONNX_MODEL_PATH)
+    runtime = backend.CudaRuntime(local_rank)
+    if args.nproc_per_node > 1:
+        runtime.init_comm(
+            "9g",
+            world_size,
+            rank,
+        )
+        print("[{}] comm init.".format(rank))
+
+    stub = OnnxStub(model, runtime)
+    print("[{}] stub init.".format(rank))
+
+    for i in range(10):
+        if args.no_cudagraph:
+            stub.run()
+        else:
+            stub.run_with_cudagraph()
+    print("[{}] stub warmup.".format(rank))
+
+    tokenizer = CPM9GTokenizer("/data1/shared/9G-Infer/models/11B-Chat-QY-epoch-8/vocabs.txt")
+    query = tokenizer.encode(query)
+
+    output_tokens = []
+    for i in range(len(query)):
+        q = np.array(query[i])
+        (list(stub.inputs.items()))[0][1].copyin_int64(q.reshape(-1).tolist())
+        pos = i * np.ones((args.batchsize, 1), dtype=np.int64)
+        (list(stub.inputs.items()))[1][1].copyin_int64(pos.reshape(-1).tolist())
+
+        if args.no_cudagraph:
+            stub.run()
+        else:
+            stub.run_with_cudagraph()
+
+        if i == len(query) - 1:
+            output = np.array((list(stub.outputs.items()))[-1][1].copyout_float16()) if False \
+                else np.array((list(stub.outputs.items()))[-1][1].copyout_float())
+            q = np.argmax(output)
+            output_tokens.append(q)
+        
+    avg_time = 0
+    count = 0
+    while i < 1000:
+        count = count + 1
+        torch.cuda.synchronize()
+        with nvtx.annotate("gen {}-th token".format(i), color="red"):
+            i = i + 1
+            (list(stub.inputs.items()))[0][1].copyin_int64(q.reshape(-1).tolist())
+            pos = i * np.ones((args.batchsize, 1), dtype=np.int64)
+            (list(stub.inputs.items()))[1][1].copyin_int64(pos.reshape(-1).tolist())
+
+            t0 = time.time()
+            if args.no_cudagraph:
+                stub.run()
+            else:
+                stub.run_with_cudagraph()
+            t1 = time.time()
+            avg_time += t1 - t0
+
+            output = np.array((list(stub.outputs.items()))[-1][1].copyout_float16()) if False \
+                else np.array((list(stub.outputs.items()))[-1][1].copyout_float())
+
+            # print(output)
+
+            with nvtx.annotate("argmax".format(i), color="green"):
+                q = np.argmax(output)
+            if q == 2:
+                break
+
+            output_tokens.append(q)
+    avg_time = avg_time / count
+    print("avg_time_cost =", avg_time*1000, "ms")
+    text = tokenizer.decode(output_tokens)
+    return text
+
+
+if __name__ == "__main__":
+    comm = MPI.COMM_WORLD
+    args.rank = comm.Get_rank()
+    args.nproc_per_node = comm.Get_size()
+    world_size = args.num_nodes * args.nproc_per_node
+
+    if not os.path.exists(ONNX_MODEL_PATH):
+        print("exporting onnx graph")
+        generate_onnx(ONNX_MODEL_PATH)
+    else:
+        print("will use exsiting onnx graph")
+    onnx_model = onnx.load(ONNX_MODEL_PATH)
+    print("data loaded")
+    
+
+    #query = '''Beijing is the captial'''
+    #query = '''什么是PTX？'''
+    #query = '''生病了怎么办？'''
+    #query = '''Happy'''
+    query = '''def gcd(a, b):'''
+
+    ####################################
+    # infinitensor dist
+    ####################################
+    # run distributed parallel.
+    pred = start_worker(world_size, args.rank, args.rank %
+                  args.nproc_per_node, onnx_model, query)
+    if args.rank == 0:
+        print("输入：\n\n", query, "\n")
+        print("输出：", pred)

examples/python/test_llama2_7b.py

@@ -0,0 +1,491 @@
+from transformers import AutoModelForCausalLM, AutoTokenizer, LlamaForCausalLM
+from tqdm import tqdm
+import argparse
+import torch
+import onnx
+import onnx_graphsurgeon as gs
+import os
+import numpy as np
+from pyinfinitensor.onnx import OnnxStub, backend
+import time
+import nvtx
+from mpi4py import MPI
+
+parser = argparse.ArgumentParser(description='')
+parser.add_argument('--batchsize', dest='batchsize', type=int, default=1)
+parser.add_argument('--layer', dest='n_layers', type=int, default=32)
+parser.add_argument("--num_nodes", dest='num_nodes',
+                    type=int, default=1, help="number of nodes")
+parser.add_argument("--nproc_per_node", dest="nproc_per_node",
+                    type=int, default=1, help="number of processes per node")
+parser.add_argument("--world_size", dest="world_size",
+                    type=int, default=1, help="")
+parser.add_argument("--n_max_length", dest="n_max_length",
+                    type=int, default=1024, help="")
+parser.add_argument("--vocab_size", dest="vocab_size",
+                    type=int, default=32000, help="vocabulary size")
+parser.add_argument("--hidden_size", dest="hidden_size",
+                    type=int, default=4096)
+parser.add_argument("--head_size", dest="head_size",
+                    type=int, default=32)
+parser.add_argument("--head_dim", dest="head_dim",
+                    type=int, default=128)
+parser.add_argument('--rank', dest='rank', type=int, default=0)
+parser.add_argument('--no_cudagraph', action='store_true')
+parser.add_argument('--fp16', action='store_true')
+parser.add_argument('--is_1st_graph', action='store_true')
+parser.add_argument('--speedup', action='store_true')
+args = parser.parse_args()
+
+comm = MPI.COMM_WORLD
+args.rank = comm.Get_rank()
+args.nproc_per_node = comm.Get_size()
+args.world_size = args.num_nodes * args.nproc_per_node
+
+PRETRAINED_LLAMA_PATH = "/data0/shared/data/public/opensource_models/meta-llama/Llama-2-7b-hf/"
+ONNX_MODEL_PATH = "/data3/shared/xnsong/llama2/" + ("1st" if args.is_1st_graph else "2nd")
+ONNX_MODEL_ORIGIN_PATH = ONNX_MODEL_PATH + "/origin/llama2_origin_bs{}_layer{}.onnx".format(
+    args.batchsize, args.n_layers)
+ONNX_MODEL_SIM_PATH = ONNX_MODEL_PATH + "/sim/llama2_sim_bs{}_layer{}.onnx".format(
+    args.batchsize, args.n_layers)
+ONNX_MODEL_FUSION_PATH = ONNX_MODEL_PATH + "/fusion/llama2_fusion_bs{}_layer{}.onnx".format(
+    args.batchsize, args.n_layers)
+ONNX_MODEL_SPECIAL_PATH = ONNX_MODEL_PATH + "/special/llama2_special_bs{}_layer{}.onnx".format(
+    args.batchsize, args.n_layers)
+ONNX_MODEL_FP16_PATH = ONNX_MODEL_PATH + "/fp16/llama2_fp16_bs{}_layer{}.onnx".format(
+    args.batchsize, args.n_layers)
+ONNX_MODEL_DIST_PATH = ONNX_MODEL_PATH + "/dist/llama2_dist_bs{}_layer{}_fp{}_worldsize{}_rank{}.onnx".format(
+    args.batchsize, args.n_layers, 16 if args.fp16 else 32, args.world_size, args.rank)
+
+def parallel_model(onnx_model, world_size, rank):
+    graph = gs.import_onnx(onnx_model)
+    tmap = graph.tensors()
+    
+    for i in range(args.n_layers):
+        tmap[graph.inputs[2+i*2].name].shape[1] = tmap[graph.inputs[2+i*2].name].shape[1]//world_size
+        tmap[graph.inputs[3+i*2].name].shape[1] = tmap[graph.inputs[3+i*2].name].shape[1]//world_size
+        for node in graph.nodes:
+            if node.name == "/model/layers." + str(i) + "/self_attn/q_proj/MatMul":
+                node.inputs[1].values = np.hsplit(node.inputs[1].values, world_size)[rank]
+            elif node.name == "/model/layers." + str(i) + "/self_attn/k_proj/MatMul":
+                node.inputs[1].values = np.hsplit(node.inputs[1].values, world_size)[rank]
+            elif node.name == "/model/layers." + str(i) + "/self_attn/v_proj/MatMul":
+                node.inputs[1].values = np.hsplit(node.inputs[1].values, world_size)[rank]
+            elif node.name == "/model/layers." + str(i) + "/self_attn/o_proj/MatMul":
+                node.inputs[1].values = np.vsplit(node.inputs[1].values, world_size)[rank]
+                reduce_sum_output = gs.Variable("reduce_sum_output_" + str(i) + "_0", 
+                                                dtype=np.float32)
+                reduce_sum = gs.Node(op="ReduceSum", name="reduce_sum_"+str(i)+"_0",
+                                     inputs=node.outputs, outputs=[reduce_sum_output], 
+                                     attrs={"noop_with_empty_axes":1, "communicator":0})
+                graph.nodes.append(reduce_sum)
+                next_node = node.outputs[0].outputs[0]
+                next_node.inputs[1] = reduce_sum_output
+            elif node.name == "/model/layers." + str(i) + "/self_attn/Reshape_0" or \
+                 node.name == "/model/layers." + str(i) + "/self_attn/Reshape_1":
+                node.inputs[1].values = np.array(
+                    [1, 1,
+                     args.head_size//world_size,
+                     args.hidden_size//args.head_size])
+            elif node.name == "/model/layers." + str(i) + "/self_attn/Reshape_2":
+                 node.inputs[1] = gs.Constant(name="/model/layers."+str(i)+"/self_attn/vreshape_input",
+                                             values=np.array(
+                                                 [1, 1,
+                                                  args.head_size//world_size,
+                                                  args.hidden_size//args.head_size]))
+            elif node.name == "/model/layers." + str(i) + "/self_attn/Reshape_3":
+                node.inputs[1] = gs.Constant(name="/model/layers." + str(i) + "/self_attn/Reshape_3_shape",
+                                             values=np.array(
+                                                 [1, 1, args.hidden_size//world_size]))
+
+            elif node.name == "/model/layers." + str(i) + "/mlp/up_proj/MatMul":
+                node.inputs[1].values = np.hsplit(node.inputs[1].values, world_size)[rank]
+            elif node.name == "/model/layers." + str(i) + "/mlp/gate_proj/MatMul":
+                node.inputs[1].values = np.hsplit(node.inputs[1].values, world_size)[rank]
+            elif node.name == "/model/layers." + str(i) + "/mlp/down_proj/MatMul":
+                node.inputs[1].values = np.vsplit(node.inputs[1].values, world_size)[rank]
+                reduce_sum_output_1 = gs.Variable("reduce_sum_output_" + str(i) + "_1", 
+                                                  dtype=np.float32)
+                reduce_sum_1 = gs.Node(op="ReduceSum", inputs=node.outputs, outputs=[reduce_sum_output_1],
+                                     attrs={"noop_with_empty_axes":1, "communicator":0})
+                graph.nodes.append(reduce_sum_1)
+                next_node = node.outputs[0].outputs[0]
+                next_node.inputs[1] = reduce_sum_output_1
+
+    # new_out_1 = tmap["/model/layers.0/mlp/down_proj/MatMul_output_0"] #reduce_sum_output
+    # new_out_1.dtype = np.float32
+    # new_out_1.shape = [1,1,4096]
+    # graph.outputs.append(new_out_1)
+    graph.cleanup(True).toposort()
+    return gs.export_onnx(graph)
+
+def simplify(onnx_model):
+    graph = gs.import_onnx(onnx_model)
+    for node in graph.nodes:
+        if node.op == "Cast":
+            inp_node = node.i()
+            inp_node.outputs = node.outputs
+            node.outputs.clear()
+    
+    for i in range(args.n_layers):
+        nodename = "/model/layers." + str(i) + "/self_attn/Add_2"
+        node = [node for node in graph.nodes if node.name == nodename][0]
+        inp_node = node.i()
+        inp_node.outputs = node.outputs
+        node.outputs.clear()
+
+    graph.cleanup().toposort()
+    return gs.export_onnx(graph)
+
+@gs.Graph.register()
+def replace_with_RMSNorm(self, inputs, outputs):
+    inputs[0].outputs.pop(0)
+    inputs[0].outputs.pop(0)
+
+    for out in outputs:
+        out.inputs.clear()
+    return self.layer(op="RMSNorm", inputs=inputs, outputs=outputs, name="rmsnorm")
+
+@gs.Graph.register()
+def replace_with_silu(self, inputs, outputs):
+    for inp in inputs:
+        inp.outputs.clear()
+    for out in outputs:
+        out.inputs.clear()
+    return self.layer(op="Silu", inputs=inputs, outputs=outputs, name="silu")
+
+@gs.Graph.register()
+def replace_with_RoPE(self, a, b):
+    return self.layer(op="RoPE", inputs=a, outputs=b, name="rope")
+
+@gs.Graph.register()
+def replace_with_attention(self, inputs, outputs, inputs_added, outputs_removed):
+    for inp in inputs:
+        inp.outputs.clear()
+    for out in outputs:
+        out.inputs.clear()
+    for inp in inputs_added:
+        inputs.append(inp)
+    for out in outputs_removed:
+        out.inputs.clear()
+    return self.layer(op="AttentionKVCache", inputs=inputs, outputs=outputs, name="attention")
+
+def fusion(model):
+    graph = gs.import_onnx(model)
+    tmap = graph.tensors()
+
+    tmap["onnx::Reshape_1"].outputs.clear()
+
+    inputs = [tmap["/model/layers.0/input_layernorm/Cast_output_0"], tmap["model.layers.0.input_layernorm.weight"]]
+    rmsnorm_outputs = [tmap["/model/layers.0/input_layernorm/Mul_1_output_0"]]
+    graph.replace_with_RMSNorm(inputs, rmsnorm_outputs) 
+
+    for i in range(args.n_layers):
+        # rotary embedding op
+        tmap["/model/layers." + str(i) + "/self_attn/Add_output_0"].inputs.clear()
+        tmap["/model/layers." + str(i) + "/self_attn/Add_1_output_0"].inputs.clear()
+        attn_qreshape_input = gs.Constant(name="/model/layers." + str(i) + "/self_attn/qreshape_input", 
+                                          values=np.array([1,1,args.head_size,args.hidden_size//args.head_size]))
+        attn_kreshape_input = gs.Constant(name="/model/layers." + str(i) + "/self_attn/kreshape_input", 
+                                          values=np.array([1,1,args.head_size,args.hidden_size//args.head_size]))
+        attn_qrope_output   = gs.Variable(name="/model/layers." + str(i) + "/self_attn/qrope_output")
+        attn_krope_output    = gs.Variable(name="/model/layers." + str(i) + "/self_attn/krope_output")
+        attn_qreshape_output = gs.Variable(name="/model/layers." + str(i) + "/self_attn/qreshape_output") 
+        attn_kreshape_output = gs.Variable(name="/model/layers." + str(i) + "/self_attn/kreshape_output") 
+
+        attn_qreshape = gs.Node(op="Reshape", name = "/model/layers." + str(i) + "/self_attn/Reshape_0", inputs=[attn_qrope_output, attn_qreshape_input], outputs=[attn_qreshape_output])
+        attn_kreshape = gs.Node(op="Reshape", name = "/model/layers." + str(i) + "/self_attn/Reshape_1", inputs=[attn_krope_output, attn_kreshape_input], outputs=[attn_kreshape_output])
+        attn_qtrans = gs.Node(op="Transpose", attrs={"perm":np.array([0,2,1,3])}, inputs=[attn_qreshape_output], 
+                              outputs=[tmap["/model/layers." + str(i) + "/self_attn/Add_output_0"]])
+        attn_ktrans = gs.Node(op="Transpose", attrs={"perm":np.array([0,2,1,3])}, inputs=[attn_kreshape_output], 
+                              outputs=[tmap["/model/layers." + str(i) + "/self_attn/Add_1_output_0"]])
+
+        graph.nodes.append(attn_qreshape)
+        graph.nodes.append(attn_kreshape)
+        graph.nodes.append(attn_qtrans)
+        graph.nodes.append(attn_ktrans)
+        inputs = [tmap["onnx::Reshape_1"], tmap["/model/layers." + str(i) + "/self_attn/q_proj/MatMul_output_0"]]
+        graph.replace_with_RoPE(inputs, [attn_qrope_output])
+        inputs = [tmap["onnx::Reshape_1"], tmap["/model/layers." + str(i) + "/self_attn/k_proj/MatMul_output_0"]]
+        graph.replace_with_RoPE(inputs, [attn_krope_output])
+
+        # rms-norm op
+        inputs = [tmap["/model/layers." + str(i) + "/post_attention_layernorm/Cast_output_0"], \
+                  tmap["model.layers." + str(i) + ".post_attention_layernorm.weight"]]
+        outputs = [tmap["/model/layers." + str(i) + "/post_attention_layernorm/Mul_1_output_0"]]
+        graph.replace_with_RMSNorm(inputs, outputs)
+        inputs = [tmap["/model/layers." + str(i+1) + "/input_layernorm/Cast_output_0"] if i != args.n_layers-1 else \
+                  tmap["/model/norm/Cast_output_0"], \
+                  tmap["model.layers." + str(i+1) + ".input_layernorm.weight"] if i != args.n_layers-1 else \
+                  tmap["model.norm.weight"]]
+        outputs = [tmap["/model/layers."+ str(i+1) + "/input_layernorm/Mul_1_output_0"]] if i != args.n_layers-1 else \
+                  [tmap["/model/norm/Mul_1_output_0"]]
+        graph.replace_with_RMSNorm(inputs, outputs)
+
+        # silu op
+        inputs = [tmap["/model/layers." + str(i) + "/mlp/gate_proj/MatMul_output_0"]]
+        outputs = [tmap["/model/layers." + str(i) + "/mlp/act_fn/Mul_output_0"]]
+        graph.replace_with_silu(inputs, outputs)
+
+        inputs = [
+            tmap["onnx::Concat_" + str((i+1)*2)],
+            tmap["onnx::Concat_" + str((i+1)*2+1)],
+            tmap["/model/layers." + str(i) + "/self_attn/Add_output_0"],
+            tmap["/model/layers." + str(i) + "/self_attn/Add_1_output_0"],
+            tmap["/model/layers." + str(i) + "/self_attn/Transpose_2_output_0"]]
+        outputs = [
+            tmap["/model/layers." + str(i) + "/self_attn/MatMul_1_output_0"],]
+
+        inputs_added = [graph.inputs[1]]
+        outputs_removed = []
+        graph.replace_with_attention(
+            inputs, outputs, inputs_added, outputs_removed)
+    
+    graph.outputs = [tmap[graph.outputs[0].name]]
+    graph.cleanup(True).toposort()
+
+    return gs.export_onnx(graph)
+
+def special_pass(model):
+    graph = gs.import_onnx(model)
+    tmap = graph.tensors()
+    for node in graph.nodes:
+        if node.op == "Transpose" or node.op == "Reshape":
+            inp_node = node.i()
+            inp_node.outputs = node.outputs
+            node.outputs.clear()
+    graph.cleanup(True).toposort()
+    return gs.export_onnx(graph)
+
+def convert_to_fp16(model):
+    graph = gs.import_onnx(model)
+    
+    for node in graph.nodes:
+        if node.op == "Gather" and node.name == "/model/embed_tokens/Gather":
+            node.inputs[0].values = np.float16(node.inputs[0].values)
+        
+        if node.op == "RMSNorm":
+            node.inputs[1].values = np.float16(node.inputs[1].values)
+
+        if node.op == "MatMul":
+            node.inputs[1].values = np.float16(node.inputs[1].values)
+            if node.name == "/lm_head/MatMul":
+                cast_1_out = gs.Variable(node.name+"_cast_out_output_0", dtype=np.float32, shape=node.outputs[0].shape)
+                cast_1 = gs.Node(op="Cast", inputs=[node.outputs[0]], outputs=[cast_1_out])
+                cast_1.attrs["to"] = np.float32
+                cast_1.name = node.name+"_cast_out_0"
+                graph.nodes.append(cast_1)
+                graph.outputs[0] = cast_1_out
+                node.outputs[0].dtype = np.float16
+
+    graph.cleanup(True).toposort()
+    return gs.export_onnx(graph)
+
+def export_onnx(model: AutoModelForCausalLM):
+    if not os.path.exists(ONNX_MODEL_ORIGIN_PATH):
+        print("exporting origin onnx model...")
+        with torch.no_grad():
+            param = torch.zeros(
+                (args.batchsize, model.config.max_position_embeddings-1), dtype=torch.long)
+            logits = model(param, past_key_values=None)
+            
+            if not args.is_1st_graph:
+                param_kvcache = torch.zeros((args.batchsize, 1), dtype=torch.long)
+                torch.onnx.export(model, (param_kvcache, {"past_key_values": logits.past_key_values,
+                                                          "position_ids": param_kvcache}), \
+                                ONNX_MODEL_ORIGIN_PATH, verbose=False,
+                                do_constant_folding=True,)
+            else:
+                position_ids = torch.tile(torch.arange(0, model.config.max_position_embeddings-1), (args.batchsize, 1))
+                attention_mask = torch.ones((args.batchsize, model.config.max_position_embeddings-1), dtype=torch.bool)
+                torch.onnx.export(model, (param, {"attention_mask": attention_mask,
+                                                  "position_ids": position_ids}),\
+                                ONNX_MODEL_ORIGIN_PATH, verbose=False,
+                                do_constant_folding=True,)
+        print("export origin onnx finished.")
+
+    if not args.is_1st_graph and not os.path.exists(ONNX_MODEL_SIM_PATH):
+        print("exporting sim onnx model...")
+        onnx_model = onnx.load(ONNX_MODEL_ORIGIN_PATH)
+        onnx_model = simplify(onnx_model)
+        onnx.save(onnx_model, ONNX_MODEL_SIM_PATH, save_as_external_data=True, \
+                    location="llama2_sim_bs{}_layer{}.pb".format(args.batchsize, args.n_layers))
+        print("exporting sim onnx model finished.")
+
+    if not args.is_1st_graph and not os.path.exists(ONNX_MODEL_FUSION_PATH):
+        print("exporting fusion onnx model...")
+        onnx_model = onnx.load(ONNX_MODEL_SIM_PATH)
+        onnx_model = fusion(onnx_model)
+        onnx.save(onnx_model, ONNX_MODEL_FUSION_PATH, save_as_external_data=True, \
+                    location="llama2_fusion_bs{}_layer{}.pb".format(args.batchsize, args.n_layers))
+        print("exporting fusion onnx model finished.")
+    
+    if not args.is_1st_graph and not os.path.exists(ONNX_MODEL_SPECIAL_PATH):
+        print("exporting special onnx model...")
+        onnx_model = onnx.load(ONNX_MODEL_FUSION_PATH)
+        onnx_model = special_pass(onnx_model)
+        onnx.save(onnx_model, ONNX_MODEL_SPECIAL_PATH, save_as_external_data=True, \
+                    location="llama2_special_bs{}_layer{}.pb".format(args.batchsize, args.n_layers))
+        print("exporting special onnx model finished.")
+
+    if not args.is_1st_graph and args.fp16 and not os.path.exists(ONNX_MODEL_FP16_PATH):
+        print("exporting fp16 onnx model...")
+        onnx_model = onnx.load(ONNX_MODEL_SPECIAL_PATH)
+        onnx_model = convert_to_fp16(onnx_model)
+        onnx.save(onnx_model, ONNX_MODEL_FP16_PATH, save_as_external_data=True, \
+                    location="llama2_fp16_bs{}_layer{}.pb".format(args.batchsize, args.n_layers))
+        print("exporting fp16 onnx model finished.")
+
+    print("world_size =", args.world_size)
+    if not args.is_1st_graph and args.world_size > 1 and not os.path.exists(ONNX_MODEL_DIST_PATH):
+        print("exporting dist onnx model...")    
+        onnx_model = onnx.load(ONNX_MODEL_FP16_PATH) if args.fp16 else onnx.load(ONNX_MODEL_SPECIAL_PATH)
+        onnx_model = parallel_model(onnx_model, args.world_size, args.rank)
+        onnx.save(onnx_model, ONNX_MODEL_DIST_PATH, save_as_external_data=True, \
+            location="llama2_dist_bs{}_layer{}_fp{}_worldsize{}_rank{}.pb".format(
+                args.batchsize, args.n_layers, 
+                16 if args.fp16 else 32, args.world_size, args.rank))
+        print("exporting dist onnx model finished.")
+
+def get_it_logit(onnx_model, input_ids):
+    # initialization
+    runtime = backend.CudaRuntime(args.rank)
+    runtime.init_comm(
+        "dist",
+        args.world_size,
+        args.rank,
+    )
+    print("[{}] comm init.".format(args.rank))
+    stub = OnnxStub(onnx_model, runtime)
+    print("[{}] stub init.".format(args.rank))
+
+    # warm up
+    for i in range(10):
+        if args.no_cudagraph:
+            stub.run()
+        else:
+            stub.run_with_cudagraph()
+    print("[{}] stub warmup.".format(args.rank))
+
+    logits = np.zeros((args.batchsize, args.n_max_length, args.vocab_size), dtype=np.float32)
+    output_ids = np.zeros((args.batchsize, args.n_max_length), dtype=np.int64)
+    avg_inference_time = 0
+    t0 = time.time()
+    for i in tqdm(range(0, args.n_max_length)):
+        with nvtx.annotate("seq_length = {}".format(i), color="red"):
+            assert input_ids.shape[0] == args.batchsize
+            input_id = input_ids[:, i] if i < input_ids.shape[1] else output_ids[:, i-1]
+            position_id = i*np.ones((args.batchsize, 1), dtype=np.int32)
+
+            # copyin input
+            with nvtx.annotate("[it] copyin", color="blue"):
+                (list(stub.inputs.items()))[0][1].copyin_int64(
+                    input_id.reshape(-1).tolist())
+                (list(stub.inputs.items()))[1][1].copyin_int64(
+                    position_id.reshape(-1).tolist())
+                
+            # run
+            t10 = time.time()
+            with nvtx.annotate("[it] run", color="green"):
+                if args.no_cudagraph:
+                    stub.run()
+                else:
+                    stub.run_with_cudagraph()
+            t11 = time.time()
+            avg_inference_time += (t11 - t10)
+                    
+            # copyout output
+            if not args.speedup:
+                with nvtx.annotate("[it] copyout", color="blue"):
+                    logits[:,i, :] = np.array((list(stub.outputs.items()))[0][1].copyout_float()).reshape(args.batchsize, -1)
+                    output_ids[:, i] = np.argmax(logits[:, i, :], -1).astype(np.int64)
+                    
+
+    t1 = time.time()
+    if args.rank == 0:
+        result = "[it] e2e: {} gpus, {} layers, e2e time: {:.2f}s, average inference time: {:.2f}ms"\
+                .format(args.num_nodes * args.nproc_per_node, args.n_layers, t1-t0, \
+                        avg_inference_time*1000/args.n_max_length)
+        print(result)
+    del stub
+    return output_ids
+
+if __name__ == "__main__":
+    torch_model = LlamaForCausalLM.from_pretrained(
+        PRETRAINED_LLAMA_PATH, num_hidden_layers=int(args.n_layers)).eval()
+    tokenizer = AutoTokenizer.from_pretrained(PRETRAINED_LLAMA_PATH)
+    #prompt = "Hey, are you conscious? Can you talk to me?"
+    #prompt = "What is PTX?"
+    #prompt = "Tell me a joke."
+    #prompt = "What are the key principles of smart investing?"
+    prompt = "What is DeepSpeed?"
+    prompts=[prompt]*args.batchsize
+    inputs = tokenizer(prompts, return_tensors="pt")
+    
+    input_ids = inputs.input_ids
+    print("prompt ids =", input_ids)
+
+    ##########################################################
+    # inference with InfiniTensor
+    ##########################################################
+    print("exporting onnx...")
+    export_onnx(torch_model)
+    print("exporting onnx finished.")
+    
+    onnx_to_run_path = ONNX_MODEL_DIST_PATH if args.world_size > 1 else \
+                       (ONNX_MODEL_FP16_PATH if args.fp16 else ONNX_MODEL_SPECIAL_PATH)
+    print("loading onnx", onnx_to_run_path, "...")
+    onnx_model = onnx.load(onnx_to_run_path)
+    print("loading onnx finished.")
+    output_ids_it = get_it_logit(onnx_model, input_ids)
+    it_output_text = tokenizer.batch_decode(output_ids_it[:, input_ids.shape[-1]:output_ids_it.shape[-1]])
+    if args.rank == 0:
+        for i in range(args.batchsize):
+            print("prompt: ", prompts[i])
+            print("answer: [it]", it_output_text[i])
+
+    ##########################################################
+    # validation with pytorch
+    ##########################################################
+    """
+    generate_ids = torch_model.generate(inputs.input_ids, max_length=args.n_max_length)#, num_beams=4, do_sample=True)
+    outputs = tokenizer.batch_decode(generate_ids, skip_special_tokens=True, clean_up_tokenization_spaces=False)[0]
+    """
+    if not args.speedup and not args.is_1st_graph:
+        kvcache_torch = None
+        output_ids_pt = torch.zeros(args.batchsize, args.n_max_length).int() # + input_ids.shape[-1] - 1).int()
+        if args.fp16:
+            torch_model = torch_model.half()
+        
+        torch_model = torch_model.cuda()
+        # print(torch.cuda.memory_summary())
+
+        avg_inference_time = 0
+        with torch.no_grad():
+            t0 = time.time()
+            for i in range(args.n_max_length):
+                input_id = input_ids[:,i] if i < input_ids.shape[1] else out_token
+                input_id = input_id.view(args.batchsize,1).cuda()
+                t00 = time.time()
+                outputs = torch_model(input_id, past_key_values=kvcache_torch)
+                t01 = time.time()
+                avg_inference_time += (t01-t00)
+
+                logits = outputs['logits']
+                kvcache_torch = outputs['past_key_values']
+                out_token = torch.argmax(logits, dim=-1)
+                output_ids_pt[:, i:i+1] = out_token
+            t1 = time.time()
+        avg_inference_time /= args.n_max_length
+        result = "[pt] e2e time: {:.2f}s, average inference time: {:.2f}ms"\
+                .format(t1-t0, avg_inference_time*1000)
+
+        if args.rank == 0:
+            print(result)
+            pt_output_text = tokenizer.batch_decode(output_ids_pt[:,input_ids.shape[-1]:args.n_max_length])
+            for i in range(args.batchsize):
+                print("[pt]", args.rank, pt_output_text[i])
+            
+            if not args.is_1st_graph:
+                assert(output_ids_it.shape[-1] == args.n_max_length)
+                np.testing.assert_equal(output_ids_pt[:, input_ids.shape[-1]:args.n_max_length], output_ids_it[:,input_ids.shape[-1]:args.n_max_length])

include/cuda/cuda_attention_kvcache.h

@@ -3,14 +3,17 @@
 #include <cstdio>
 
 struct AttentionKVCacheMetadata {
-    int dimSize[4];
-    int stride[4];
+    int head_dim;
+    int num_heads;
+    int num_seqs;
+    int max_kv_seqlen;
 };
 
 namespace infini {
-void attention_kvcache_kernel(float *input_k_cache, float *input_v_cache,
-                              float *input_q, float *input_k, float *input_v,
-                              int *position_id, float *output_matmul,
+void attention_kvcache_kernel(int dType, void *input_k_cache,
+                              void *input_v_cache, void *input_q, void *input_k,
+                              void *input_v, int64_t *position_id,
+                              void *output_matmul,
                               const AttentionKVCacheMetadata &compMeta,
                               float *output_O_temp, float *output_sum_temp);
 

include/cuda/cuda_rope.h

@@ -5,8 +5,7 @@
 
 namespace infini {
 
-void rope_kernel(int dType, int *pos, void *input, void *output, int size,
-                 int dim_model, int dim_head, int hidden_stride,
-                 int pos_stride);
+void rope_kernel(int dType, int64_t *pos, void *input, void *output,
+                 int dim_model, int dim_head, int batchsize, int pos_stride);
 
 }; // namespace infini

include/kunlun/kunlun_runtime.h

@@ -21,7 +21,7 @@ class KUNLUNRuntimeObj : public RuntimeObj {
         ctx = xdnn::create_context();
         // 10GB for Longformer
         // size_t longformerNum = 3lu * (1 << 30);
-        size_t workspaceSize = 2llu << 30; // 2 GB
+        size_t workspaceSize = 3llu << 30; // 3 GB
         KUNLUNPtr wkspacePtr = alloc(workspaceSize);
         workspace =
             make_ref<WorkspaceObj<KUNLUNPtr>>(wkspacePtr, workspaceSize);
@@ -42,7 +42,7 @@ class KUNLUNRuntimeObj : public RuntimeObj {
     KUNLUNPtr alloc(size_t size) override {
         void *ptr;
         checkKUNLUNError(
-            xpu_malloc((void **)&ptr, size, XPUMemoryKind::XPU_MEM_HBM));
+            xpu_malloc_ex((void **)&ptr, size, XPUMemoryKind::XPU_MEM_MAIN));
         return ptr;
     }
     void dealloc(void *ptr) override { xpu_free(ptr); }

include/kunlun/xccl_communicator.h

@@ -34,8 +34,8 @@ class XcclCommunicatorObj final : public CommunicatorObj {
             auto begin = std::chrono::steady_clock::now();
             while (!std::filesystem::exists(filePath)) {
                 auto now = std::chrono::steady_clock::now();
-                _IT_ASSERT_2(now < begin + std::chrono::seconds(100),
-                             "time limit (100s) exceeded.");
+                _IT_ASSERT_2(now < begin + std::chrono::seconds(10),
+                             "time limit (10s) exceeded.");
                 std::this_thread::sleep_for(std::chrono::milliseconds(100));
             }
             std::ifstream ifs(filePath, std::ios::binary);

include/operators/attention_kvcache.h

@@ -30,6 +30,10 @@ class AttentionKVCacheObj : public OperatorObj {
     OP_CLONE(AttentionKVCacheObj);
 
     optional<vector<Shape>> inferShape(const TensorVec &inputs) override;
+    vector<DataType> inferDataType(const TensorVec &inputs) const override {
+        return {inputs[2]->getDType()};
+    };
+    DataType getDType() const { return getInputs(2)->getDType(); }
 
     std::string toString() const override;
     int numInputs() const override { return 6; }

include/operators/rope.h

@@ -21,6 +21,10 @@ class RoPEObj : public OperatorObj {
     int numOutputs() const override { return 1; }
     DataType getDType() const { return getInputs(1)->getDType(); }
 
+    vector<DataType> inferDataType(const TensorVec &inputs) const override {
+        return {inputs[1]->getDType()};
+    };
+
   private:
     vector<int> getWorkloadVector() const override;
     vector<int> getOpAttrVector() const override;

pyinfinitensor/src/pyinfinitensor/onnx.py

@@ -208,16 +208,39 @@ class OnnxStub:
                     op[1],
                 )
             elif node.op_type == "MatMul":
-                tensors[node.output[0]] = self.handler.matmul(
-                    tensors[node.input[0]], # input
-                    tensors[node.input[1]], # weight
-                    tensors.get(node.output[0]),
-                    False,
-                    False,
-                    None,
-                    backend.ActType.Linear,
-                    matmul_compute_type,
-                )
+                if node.input[1] in data.keys() \
+                   and to_array(data[node.input[1]]).dtype == np.float32 \
+                   and 'cuda_runtime' in dir(backend) \
+                   and tensors[node.input[0]].shape()[0] == 1 \
+                   and tensors[node.input[0]].shape()[1] == 1 \
+                   and len(tensors[node.input[1]].shape()) == 2 \
+                   and node.input[1] in data.keys():
+                    data[node.input[1]] = from_array(
+                        np.transpose(to_array(data[node.input[1]])))
+                    tensors[node.input[1]] = self.handler.tensor(
+                        [tensors[node.input[1]].shape()[1], tensors[node.input[1]].shape()[0]],
+                        tensors[node.input[1]].dtype())
+                    tensors[node.output[0]] = self.handler.matmul(
+                        tensors[node.input[0]],
+                        tensors[node.input[1]],
+                        tensors.get(node.output[0]),
+                        False,
+                        True,
+                        None,
+                        backend.ActType.Linear,
+                        matmul_compute_type,
+                    )
+                else:
+                    tensors[node.output[0]] = self.handler.matmul(
+                        tensors[node.input[0]],
+                        tensors[node.input[1]],
+                        tensors.get(node.output[0]),
+                        False,
+                        False,
+                        None,
+                        backend.ActType.Linear,
+                        matmul_compute_type,
+                    )
             elif node.op_type == "Gemm":
                 attributes = _parse_attribute(
                     node, {"alpha": 1.0, "beta": 1.0, "transA": 0, "transB": 0}
@@ -967,7 +990,7 @@ class OnnxStub:
                     tensors[node.input[0]],
                     tensors.get(node.output[0]),
                 )
-            elif node.op_type in ["Constant", "ConstantOfShape"]:
+            elif node.op_type == "Constant":
                 output_name = node.output[0]
                 attributes = _parse_attribute(node)
                 tensor = attributes["value"]

src/kernels/bang/cast.cc

@@ -199,24 +199,6 @@ class CastCnnl : public BangKernelWithoutConfig {
                                                    dim.data()));
             NlCastType = CNNL_CAST_UINT32_TO_INT64;
             break;
-        case CastType::Float162Float:
-            checkCnnlError(cnnlSetTensorDescriptor(aDesc, CNNL_LAYOUT_NCHW,
-                                                   CNNL_DTYPE_HALF, dim.size(),
-                                                   dim.data()));
-            checkCnnlError(cnnlSetTensorDescriptor(cDesc, CNNL_LAYOUT_NCHW,
-                                                   CNNL_DTYPE_FLOAT, dim.size(),
-                                                   dim.data()));
-            NlCastType = CNNL_CAST_HALF_TO_FLOAT;
-            break;
-        case CastType::Float2Float16:
-            checkCnnlError(cnnlSetTensorDescriptor(aDesc, CNNL_LAYOUT_NCHW,
-                                                   CNNL_DTYPE_FLOAT, dim.size(),
-                                                   dim.data()));
-            checkCnnlError(cnnlSetTensorDescriptor(cDesc, CNNL_LAYOUT_NCHW,
-                                                   CNNL_DTYPE_HALF, dim.size(),
-                                                   dim.data()));
-            NlCastType = CNNL_CAST_FLOAT_TO_HALF;
-            break;
         default:
             IT_TODO_HALT();
         }

src/kernels/bang/layer_norm.cc

@@ -19,16 +19,14 @@ class LayerNormCnnl : public BangKernelWithoutConfig {
         void *const outputData = (op->getOutput()->getRawDataPtr<void *>());
 
         auto inDims = op->getInputs(0)->getDims();
-        auto fiterDims = op->getInputs(1)->getDims();
         auto outDims = op->getOutput()->getDims();
+        auto fiterDims = op->getOutput(1)->getDims();
 
         float eps = op->getEps();
         const int axis = op->getAxis();
 
-        Shape outMeanDims(outDims);
-        outMeanDims.erase(outMeanDims.begin() + axis);
+        cnnlTensorDescriptor_t inDesc, fiterDesc, outDesc;
 
-        cnnlTensorDescriptor_t inDesc, fiterDesc, outDesc, outMeanDesc;
         checkCnnlError(cnnlCreateTensorDescriptor(&inDesc));
         checkCnnlError(cnnlSetTensorDescriptor(
             inDesc, CNNL_LAYOUT_ARRAY, cnnlDataTypeConvert(op->getDType()),
@@ -41,23 +39,15 @@ class LayerNormCnnl : public BangKernelWithoutConfig {
         checkCnnlError(cnnlSetTensorDescriptor(
             outDesc, CNNL_LAYOUT_ARRAY, cnnlDataTypeConvert(op->getDType()),
             outDims.size(), outDims.data()));
-        checkCnnlError(cnnlCreateTensorDescriptor(&outMeanDesc));
-        checkCnnlError(cnnlSetTensorDescriptor(
-            outMeanDesc, CNNL_LAYOUT_ARRAY, cnnlDataTypeConvert(op->getDType()),
-            outMeanDims.size(), outMeanDims.data()));
         size_t wsSize;
         cnnlGetLayerNormOpWorkspaceSize(context->cnnlHandle(), axis, inDesc,
                                         &wsSize);
         BangPtr wsData = context->getWorkspace(wsSize);
-        size_t meanSize =
-            cnnlGetTensorElementNum(outMeanDesc) * op->getDType().getSize();
-        BangPtr meanData = context->getWorkspace(meanSize);
-        BangPtr rstdData = context->getWorkspace(meanSize);
 
         cnnlStatus_t stat = cnnlLayerNormForward(
             context->cnnlHandle(), inDesc, inputData, axis, fiterDesc,
             scaleData, biasData, eps, wsData, wsSize, outDesc, outputData,
-            outMeanDesc, meanData, rstdData);
+            inDesc, NULL, NULL);
 
         if (stat != CNNL_STATUS_SUCCESS)
             return;

src/kernels/bang/matmul.cc

@@ -66,13 +66,6 @@ class MatmulCnnl : public BangKernelWithoutConfig {
         cnnlSetMatMulDescAttr(bmm_desc, CNNL_MATMUL_DESC_TRANSB, &transB,
                               sizeof(int32_t));
 
-        std::string computeTypeStr = op->getComputeType();
-        if (computeTypeStr == "tf32") {
-            int32_t tf32 = 1;
-            cnnlSetMatMulDescAttr(bmm_desc, CNNL_MATMUL_ALLOW_TF32, &tf32,
-                                  sizeof(int32_t));
-        }
-
         cnnlMatMulAlgo_t bmm_algo;
         cnnlMatMulAlgoCreate(&bmm_algo);
 

src/kernels/cuda/attention_kvcache.cc

@@ -7,33 +7,37 @@ namespace infini {
 
 class AttentionKVCacheCompute {
     void initAttentionKVCacheMetadata(AttentionKVCacheMetadata &metadata,
-                                      Tensor tensor) const {
-        int nDims = tensor->getRank();
-        auto strides = tensor->getStride();
+                                      Tensor input_v_cache,
+                                      Tensor position_id) const {
+        int nDims = input_v_cache->getRank();
+        auto strides = input_v_cache->getStride();
         IT_ASSERT(nDims == 4);
-        IT_ASSERT(strides.size() == (size_t)nDims);
-        for (int i = 0; i < nDims; ++i) {
-            metadata.dimSize[i] = tensor->getDims().at(i);
-            metadata.stride[i] = strides.at(i);
+        int dim_position_id = position_id->getRank();
+        metadata.num_seqs = 1;
+        for (int i = 0; i < dim_position_id; i++) {
+            metadata.num_seqs *= position_id->getDims().at(i);
         }
+        metadata.head_dim = input_v_cache->getDims().at(3);
+        metadata.num_heads = input_v_cache->getDims().at(1);
+        metadata.max_kv_seqlen = input_v_cache->getDims().at(2);
     }
 
   public:
-    void do_compute(Tensor input_k_cache, Tensor input_v_cache, Tensor input_q,
-                    Tensor input_k, Tensor input_v, Tensor position_id,
-                    Tensor output_matmul, CudaPtr p_workspace) const {
+    void do_compute(int dType, Tensor input_k_cache, Tensor input_v_cache,
+                    Tensor input_q, Tensor input_k, Tensor input_v,
+                    Tensor position_id, Tensor output_matmul,
+                    CudaPtr p_workspace) const {
         AttentionKVCacheMetadata metadata;
-        initAttentionKVCacheMetadata(metadata, input_v_cache);
+        initAttentionKVCacheMetadata(metadata, input_v_cache, position_id);
 
-        attention_kvcache_kernel(input_k_cache->getRawDataPtr<float *>(),
-                                 input_v_cache->getRawDataPtr<float *>(),
-                                 input_q->getRawDataPtr<float *>(),
-                                 input_k->getRawDataPtr<float *>(),
-                                 input_v->getRawDataPtr<float *>(),
-                                 position_id->getRawDataPtr<int *>(),
-                                 output_matmul->getRawDataPtr<float *>(),
-                                 metadata, (float *)p_workspace,
-                                 (float *)(p_workspace + (1ll << 30)));
+        attention_kvcache_kernel(
+            dType, input_k_cache->getRawDataPtr<void *>(),
+            input_v_cache->getRawDataPtr<void *>(),
+            input_q->getRawDataPtr<void *>(), input_k->getRawDataPtr<void *>(),
+            input_v->getRawDataPtr<void *>(),
+            position_id->getRawDataPtr<int64_t *>(),
+            output_matmul->getRawDataPtr<void *>(), metadata,
+            (float *)p_workspace, (float *)(p_workspace + (1ll << 30)));
     }
 };
 
@@ -41,15 +45,17 @@ class AttentionKVCacheCuda : private AttentionKVCacheCompute,
                              public CudaKernelWithoutConfig {
     void compute(const Operator &_op,
                  const RuntimeObj *_context) const override {
-        IT_ASSERT(_op->getDType() == DataType::Float32);
+        auto op = as<AttentionKVCacheObj>(_op);
+        int dType = op->getDType().getIndex();
+        int position_idx_dtype = op->getInputs()[5]->getDTypeIndex();
+        IT_ASSERT(dType == 1 || dType == 10 || position_idx_dtype == 7);
 
         size_t workspaceSize = 2ll << 30;
         auto context = dynamic_cast<const CudaRuntimeObj *>(_context);
         CudaPtr idxWsData = context->getWorkspace(workspaceSize);
-        do_compute(_op->getInputs()[0], _op->getInputs()[1],
-                   _op->getInputs()[2], _op->getInputs()[3],
-                   _op->getInputs()[4], _op->getInputs()[5],
-                   _op->getOutputs()[0], idxWsData);
+        do_compute(dType, op->getInputs()[0], op->getInputs()[1],
+                   op->getInputs()[2], op->getInputs()[3], op->getInputs()[4],
+                   op->getInputs()[5], op->getOutputs()[0], idxWsData);
     }
 };
 

src/kernels/cuda/attention_kvcache.cu

@@ -1,171 +1,237 @@
 #include "cuda/cuda_common.h"
 #include "cuda/cuda_attention_kvcache.h"
 #define WARP_SIZE 32
-#define BLOCKSIZE WARP_SIZE
 #define SEQ_UNIT 16
+#define BLOCKSIZE_2 WARP_SIZE*4
+#define MAX_PARTITION 1024
 
 // ASSUME SEQ_LEN OF Q IS 1
-__global__ void _attention_kvcache_kernel_128_1(float* input_k_cache,
-                                              float* input_v_cache, 
-                                              float* input_q, 
-                                              float* input_k, 
-                                              float* input_v, 
-                                              int* position_id,
+template <class T>
+__global__ void _attention_kvcache_kernel_128_1(T* input_k_cache,
+                                              T* input_v_cache, 
+                                              T* input_q, 
+                                              T* input_k, 
+                                              T* input_v, 
+                                              int64_t* position_id,
                                               AttentionKVCacheMetadata compMeta,
-                                              float* output_O_temp,
+                                              half* output_O_temp,
                                               float* output_sum_temp) {
-    int seq_length = position_id[0] + 1;
+    int seq_length = position_id[blockIdx.y] + 1;
     int stride = (seq_length + SEQ_UNIT - 1) / SEQ_UNIT;
-    if(blockIdx.y >= stride)
+    if(blockIdx.z >= stride)
         return;
 
-    int lane_id = threadIdx.x % WARP_SIZE;
-    int group_id = threadIdx.x / WARP_SIZE;
-    int parallel_idx = blockIdx.x * (blockDim.x / WARP_SIZE) + group_id;
-    int idx_seq = blockIdx.y * SEQ_UNIT; 
+    int lane_id_x2 = threadIdx.x % WARP_SIZE * 2;
+    int parallel_idx = blockIdx.x + blockIdx.y * gridDim.x;
 
-    if(parallel_idx >= compMeta.dimSize[0] * compMeta.dimSize[1])
-        return;
+    int idx_seq = blockIdx.z * SEQ_UNIT;
 
-    float ptr_V[SEQ_UNIT*4]; // V
-    float ptr_K[SEQ_UNIT*4]; // K
-    float ptr_Q[4];          // Q
-    float ptr_P[SEQ_UNIT] = {0};
+    half reg_V[4];
+    half reg_K[4];
+    half reg_Q[4];
+    float reg_P;
 
-    float ptr_O[4] = {0};
-    float ptr_sum[1] = {0};
+    float reg_O[4] = {0};
+    float reg_sum = 0;
+    float temp[4];
+    bool is_fp16 = sizeof(T) == 2 ? true : false;
+
+    int idx_qkv = lane_id_x2 + parallel_idx * compMeta.head_dim;
 
     // readin Q
-    (float4 &)ptr_Q[0] = (float4 &)input_q[(lane_id * 4) + (parallel_idx * 128)];
-    int common_idx = (lane_id * 4) + (parallel_idx * compMeta.stride[1]);
-
-    // Q*K
-    #pragma unroll
-    for (int idx_SEQ_UNIT = 0; idx_SEQ_UNIT < SEQ_UNIT && idx_SEQ_UNIT + idx_seq < seq_length; idx_SEQ_UNIT ++) { 
-        if(idx_SEQ_UNIT + idx_seq < seq_length - 1){                  
-            (float4 &)ptr_K[idx_SEQ_UNIT * 4] 
-                = (float4 &) input_k_cache[common_idx + ((idx_SEQ_UNIT + idx_seq) * compMeta.stride[2])];
-        }
-        else{
-            (float4 &)ptr_K[idx_SEQ_UNIT * 4] 
-                = (float4 &) input_k[((lane_id * 4) + parallel_idx * compMeta.stride[2])];
-            (float4 &)input_k_cache[common_idx + ((idx_SEQ_UNIT + idx_seq) * compMeta.stride[2])] =
-                (float4 &)ptr_K[idx_SEQ_UNIT * 4];
-        }
-
-        
+    if(!is_fp16){
         #pragma unroll
-        for (int i = 0; i < 4; i ++){
-            ptr_K[idx_SEQ_UNIT * 4 + i] = ptr_Q[i] * ptr_K[idx_SEQ_UNIT * 4 + i];
-            #pragma unroll
-            for (int offset = 16; offset > 0; offset /= 2) {
-                ptr_K[idx_SEQ_UNIT * 4 + i] += __shfl_down_sync(0xffffffff, ptr_K[idx_SEQ_UNIT * 4 + i], offset);
-            }
-            ptr_P[idx_SEQ_UNIT] += ptr_K[idx_SEQ_UNIT * 4 + i];
+        for(int i = 0; i < 4; i += 2){
+            (float2 &)temp[i] = (float2 &)input_q[idx_qkv + i*WARP_SIZE];
+            *((half2*)(&reg_Q[i])) = __float22half2_rn(*((float2*)(&temp[i])));
         }
     }
-
-    // div sqrt(d)
-    #pragma unroll
-    for (int idx_SEQ_UNIT = 0; idx_SEQ_UNIT < SEQ_UNIT && idx_SEQ_UNIT + idx_seq < seq_length; idx_SEQ_UNIT ++) { 
-        ptr_P[idx_SEQ_UNIT] = __shfl_sync(0xffffffff, ptr_P[idx_SEQ_UNIT], 0); 
-        ptr_P[idx_SEQ_UNIT] /= sqrt(128.0);
+    else{
+        #pragma unroll
+        for(int i = 0; i < 4; i += 2){
+            (half2 &)reg_Q[i] = (half2 &)input_q[idx_qkv + i*WARP_SIZE];
+        }
     }
-
-    // softmax
+    int common_idx = lane_id_x2 + (parallel_idx * compMeta.max_kv_seqlen * compMeta.head_dim);
+    
     #pragma unroll
     for (int idx_SEQ_UNIT = 0; idx_SEQ_UNIT < SEQ_UNIT && idx_SEQ_UNIT + idx_seq < seq_length; idx_SEQ_UNIT ++) { 
-        ptr_P[idx_SEQ_UNIT] = expf(ptr_P[idx_SEQ_UNIT]);
-        ptr_sum[0] += ptr_P[idx_SEQ_UNIT];
-    }
-
-    // * V
-    #pragma unroll
-    for (int idx_SEQ_UNIT = 0; idx_SEQ_UNIT < SEQ_UNIT && idx_SEQ_UNIT + idx_seq < seq_length; idx_SEQ_UNIT ++) { 
-        if(idx_SEQ_UNIT + idx_seq < seq_length - 1){                  
-            (float4 &)ptr_V[idx_SEQ_UNIT * 4] 
-                = (float4 &) input_v_cache[common_idx + ((idx_SEQ_UNIT + idx_seq) * compMeta.stride[2])];
+        reg_P = 0;
+        int idx_kvcache = common_idx + ((idx_SEQ_UNIT + idx_seq) * compMeta.head_dim);
+        // readin K & V
+        if(idx_SEQ_UNIT + idx_seq < seq_length - 1){
+            #pragma unroll
+            for(int i = 0; i < 4; i += 2){
+                *((half2*)(&reg_K[i])) = *((half2*)(&((half*)input_k_cache)[idx_kvcache + i*WARP_SIZE]));
+                *((half2*)(&reg_V[i])) = *((half2*)(&((half*)input_v_cache)[idx_kvcache + i*WARP_SIZE]));
+            }
         }
         else{
-            (float4 &)ptr_V[idx_SEQ_UNIT * 4] 
-                = (float4 &) input_v[((lane_id * 4) + parallel_idx * compMeta.stride[2])];
-            (float4 &)input_v_cache[common_idx + ((idx_SEQ_UNIT + idx_seq) * compMeta.stride[2])]
-                = (float4 &)ptr_V[idx_SEQ_UNIT * 4];
+            if(!is_fp16){
+                #pragma unroll
+                for(int i = 0; i < 4; i += 2){
+                    (float2 &)temp[i] = (float2 &) input_k[idx_qkv + i*WARP_SIZE];
+                    *((half2*)(&reg_K[i])) = __float22half2_rn(*((float2*)(&temp[i])));
+                    *((half2*)(&((half*)input_k_cache)[idx_kvcache + i*WARP_SIZE])) = *((half2*)(&reg_K[i]));
+                    (float2 &)temp[i] = (float2 &) input_v[idx_qkv + i*WARP_SIZE];
+                    *((half2*)(&reg_V[i])) = __float22half2_rn(*((float2*)(&temp[i])));
+                    *((half2*)(&((half*)input_v_cache)[idx_kvcache + i*WARP_SIZE])) = *((half2*)(&reg_V[i]));
+                }
+            }
+            else{
+                #pragma unroll
+                for(int i = 0; i < 4; i += 2){
+                    (half2 &)reg_K[i] = (half2 &)input_k[idx_qkv + i*WARP_SIZE];
+                    *((half2*)(&((half*)input_k_cache)[idx_kvcache + i*WARP_SIZE])) = *((half2*)(&reg_K[i]));
+                    (half2 &)reg_V[i] = (half2 &)input_v[idx_qkv + i*WARP_SIZE];
+                    *((half2*)(&((half*)input_v_cache)[idx_kvcache + i*WARP_SIZE])) = *((half2*)(&reg_V[i]));
+                }
+            }
         }
 
+        // Q*K
+        #pragma unroll
+        for (int i = 0; i < 4; i += 2){
+            (half2 &)reg_K[i] = (half2 &)reg_Q[i] * (half2 &)reg_K[i];
+            #pragma unroll
+            for (int offset = WARP_SIZE/2; offset > 0; offset /= 2) {
+                (half2 &)reg_K[i] += __shfl_xor_sync(0xffffffff, (half2 &)reg_K[i], offset);
+            }
+            (float2 &) temp[i] = __half22float2((half2 &)reg_K[i]);
+            reg_P += (temp[i] + temp[i+1]);
+            (float2 &) temp[i] = __half22float2((half2 &)reg_V[i]);
+        }
+
+        // div sqrt(d)
+        reg_P /= sqrt(128.0);
+
+        // softmax
+        reg_P = expf(reg_P);
+        reg_sum += reg_P;
+
         #pragma unroll
         for (int i = 0; i < 4; i ++)
-            ptr_O[i] = fmaf(ptr_P[idx_SEQ_UNIT], ptr_V[(idx_SEQ_UNIT * 4 + i)], ptr_O[i]);
+            reg_O[i] = fmaf(reg_P, temp[i], reg_O[i]);
     }
 
     #pragma unroll
     for (int i = 0; i < 4; i ++)
-        ptr_O[i] /= ptr_sum[0];
+        reg_O[i] /= reg_sum;
 
-    (float4 &)output_O_temp[(lane_id * 4) + (blockIdx.y * compMeta.dimSize[3]) + (parallel_idx * compMeta.dimSize[3] * stride)] = (float4 &)ptr_O[0];
-    if(lane_id == 0){
-        output_sum_temp[blockIdx.y + parallel_idx * stride] = ptr_sum[0];
+    #pragma unroll
+    for(int i = 0; i < 4; i += 2)
+        (half2 &)output_O_temp[(lane_id_x2 + i*WARP_SIZE) + (blockIdx.z * compMeta.head_dim) + (parallel_idx * compMeta.head_dim * stride)] = __float22half2_rn((float2 &)reg_O[i]);
+    if(lane_id_x2 == 0){
+        output_sum_temp[blockIdx.z + parallel_idx * stride] = reg_sum;
     }
-
 }
 
-__global__ void _attention_kvcache_kernel_128_2(int* position_id,
-                                              float* output_matmul,
+
+template <class T>
+__global__ void _attention_kvcache_kernel_128_2(int64_t* position_id,
+                                              T* output_matmul,
                                               AttentionKVCacheMetadata compMeta,
-                                              float* output_O_temp,
+                                              half* output_O_temp,
                                               float* output_sum_temp) {
     int lane_id = threadIdx.x % WARP_SIZE;
-    int group_id = threadIdx.x / WARP_SIZE;
-    int parallel_idx = blockIdx.x * (blockDim.x / WARP_SIZE) + group_id;
+    int parallel_idx = blockIdx.x;
+    int offset = parallel_idx * compMeta.head_dim;
+
 
-    float ptr_O[4] = {0};
-    float ptr_O_sum[4] = {0};
-    float ptr_sum = 0;
-    float ptr_sum_temp;
     int size = (position_id[0] + SEQ_UNIT) / SEQ_UNIT;
+    bool is_fp16 = sizeof(T) == 2 ? true : false;
+
+    if(size == 1){
+        if(!is_fp16){
+            #pragma unroll
+            for(int i = threadIdx.x; i < compMeta.head_dim; i += blockDim.x)
+                output_matmul[i + offset]
+                    = __half2float(output_O_temp[i + offset]);
+        }
+        else{
+            #pragma unroll
+            for(int i = threadIdx.x; i < compMeta.head_dim; i += blockDim.x)
+                output_matmul[i + offset]
+                    = output_O_temp[i + offset];
+        }
+        return;
+    }
+
+    __shared__ float shm_sum_temp[MAX_PARTITION];
+    __shared__ float shm_sum[WARP_SIZE];
+    float temp_sum = 0;
 
     #pragma unroll
-    for(int i = 0; i < size; i ++){
-        (float4 &)ptr_O[0] 
-            = (float4 &)output_O_temp[(lane_id * 4) + (i * compMeta.dimSize[3]) + parallel_idx * compMeta.dimSize[3] * size];
-        ptr_sum_temp = output_sum_temp[i + parallel_idx * size];
-
-        #pragma unroll
-        for(int k = 0; k < 4; k ++)
-            ptr_O_sum[k] += ptr_O[k] * ptr_sum_temp;
-        ptr_sum += ptr_sum_temp;
+    for(int i = threadIdx.x; i < size; i += blockDim.x){
+        shm_sum_temp[i] = output_sum_temp[i + parallel_idx * size];
+        temp_sum += shm_sum_temp[i];
     }
 
     #pragma unroll
-    for(int k = 0; k < 4; k ++)
-        ptr_O_sum[k] = ptr_O_sum[k] / ptr_sum; 
+    for(int offset = WARP_SIZE/2; offset > 0; offset /= 2)
+        temp_sum += __shfl_down_sync(0xffffffff, temp_sum, offset);
+    if(lane_id == 0)
+        shm_sum[threadIdx.x/WARP_SIZE] = temp_sum;
+    __syncthreads();
+    temp_sum = lane_id < (size + WARP_SIZE - 1) / WARP_SIZE ? shm_sum[lane_id] : 0;
 
-    (float4 &)output_matmul[(lane_id * 4) + (parallel_idx * compMeta.dimSize[3])] = (float4 &)ptr_O_sum[0];
+    #pragma unroll
+    for(int offset = WARP_SIZE/2; offset > 0; offset /= 2)
+        temp_sum += __shfl_xor_sync(0xffffffff, temp_sum, offset);
+    temp_sum = __fdividef(1.0f, temp_sum + 1e-6f);
 
+    #pragma unroll
+    for(int i = threadIdx.x; i < compMeta.head_dim; i += blockDim.x){
+        float acc = 0.0f;
+        for(int j = 0; j < size; j ++){
+            acc = fma(__half2float(output_O_temp[i + (j * compMeta.head_dim) + offset * size]) * shm_sum_temp[j], temp_sum, acc);
+        }
+
+        if(!is_fp16){
+            output_matmul[i + offset] = acc;
+        }
+        else{
+            output_matmul[i + offset] = __float2half(acc);
+        }
+    }
 }
 
 
 namespace infini {
-void attention_kvcache_kernel(float *input_k_cache, float *input_v_cache, 
-                          float *input_q, float *input_k,
-                          float *input_v, int *position_id, float *output_matmul, 
+void attention_kvcache_kernel(int dType, void *input_k_cache, void *input_v_cache, 
+                          void *input_q, void *input_k,
+                          void *input_v, int64_t *position_id, void *output_matmul,
                           const AttentionKVCacheMetadata &compMeta,
                           float *output_O_temp, float *output_sum_temp) {
-    IT_ASSERT(compMeta.dimSize[3] == 128);
+    IT_ASSERT(dType == 1 || dType == 10);
 
-    int gridsize_y = (compMeta.dimSize[2] - 1 + SEQ_UNIT) / SEQ_UNIT;
-    dim3 gridDim(compMeta.dimSize[0]*compMeta.dimSize[1]/(BLOCKSIZE/WARP_SIZE), gridsize_y);
-    dim3 blockDim(BLOCKSIZE, 1);
+    int gridsize_y = (compMeta.max_kv_seqlen - 1 + SEQ_UNIT) / SEQ_UNIT;
+    dim3 gridDim(compMeta.num_heads, compMeta.num_seqs, gridsize_y);
+    dim3 blockDim(WARP_SIZE, 1);
 
-    _attention_kvcache_kernel_128_1
-        <<<gridDim, blockDim, 0, CUDAStream::getCurrentStream()>>>
-        (input_k_cache, input_v_cache, input_q, input_k, input_v, position_id,
-        compMeta, output_O_temp, output_sum_temp);
+    if(dType == 1){
+        _attention_kvcache_kernel_128_1<float>
+            <<<gridDim, blockDim, 0, CUDAStream::getCurrentStream()>>>
+            ((float*)input_k_cache, (float*)input_v_cache, (float*)input_q, (float*)input_k, (float*)input_v, 
+            position_id, compMeta, (half*)output_O_temp, output_sum_temp);
+
+        _attention_kvcache_kernel_128_2<float>
+            <<<compMeta.num_seqs*compMeta.num_heads, BLOCKSIZE_2,
+            0, CUDAStream::getCurrentStream()>>>
+            (position_id, (float*)output_matmul, compMeta, (half*)output_O_temp, output_sum_temp);
+    }
+    else{
+        _attention_kvcache_kernel_128_1<half>
+            <<<gridDim, blockDim, 0, CUDAStream::getCurrentStream()>>>
+            ((half*)input_k_cache, (half*)input_v_cache, (half*)input_q, (half*)input_k, (half*)input_v, 
+            position_id, compMeta, (half*)output_O_temp, output_sum_temp);
+
+        _attention_kvcache_kernel_128_2<half>
+            <<<compMeta.num_seqs*compMeta.num_heads, BLOCKSIZE_2,
+            0, CUDAStream::getCurrentStream()>>>
+            (position_id, (half*)output_matmul, compMeta, (half*)output_O_temp, output_sum_temp);
+    }
 
-    _attention_kvcache_kernel_128_2
-        <<<compMeta.dimSize[0]*compMeta.dimSize[1]/(BLOCKSIZE/WARP_SIZE), WARP_SIZE,
-        0, CUDAStream::getCurrentStream()>>>
-        (position_id, output_matmul, compMeta, output_O_temp, output_sum_temp);
 }
 
 } // namespace infini

src/kernels/cuda/matmul.cc

@@ -36,7 +36,7 @@ constexpr cublasGemmAlgo_t ALGOS[N_ALGO] = {
 
 cublasComputeType_t cuDataType2ComputeType(cudaDataType_t cuDataType) {
     if (cuDataType == CUDA_R_16F) {
-        return CUBLAS_COMPUTE_32F_FAST_16F;
+        return CUBLAS_COMPUTE_16F;
     } else if (cuDataType == CUDA_R_16BF) {
         return CUBLAS_COMPUTE_32F_FAST_16BF;
     } else if (cuDataType == CUDA_R_32F) {

src/kernels/cuda/rope.cc

@@ -18,17 +18,19 @@ class RoPECuda : public CudaKernelWithoutConfig {
         const auto &inputShape = input->getDims();
         int nDims = input->getDims().size();
 
-        int size = input->size();
         IT_ASSERT(nDims == 3 && pos->getDims().size() == 2);
-        IT_ASSERT(inputShape[1] == pos->getDims()[1]);
+        IT_ASSERT(inputShape[0] == pos->getDims()[0] &&
+                  inputShape[1] == pos->getDims()[1]);
+        int position_idx_dtype = op->getInputs()[0]->getDTypeIndex();
+        IT_ASSERT(position_idx_dtype == 7);
         int dim_model = inputShape[2];
-        int dim_head = 128;
-        int hidden_stride = dim_model * inputShape[1];
+        int dim_head = 128; // TODO: get dim_head from the framework
         int pos_stride = inputShape[1];
+        int batchsize = inputShape[0];
 
         const int dType = op->getDType().getIndex();
-        rope_kernel(dType, pos->getRawDataPtr<int *>(), inputData, outputData,
-                    size, dim_model, dim_head, hidden_stride, pos_stride);
+        rope_kernel(dType, pos->getRawDataPtr<int64_t *>(), inputData,
+                    outputData, dim_model, dim_head, batchsize, pos_stride);
     }
 };
 

src/kernels/cuda/rope.cu

@@ -4,13 +4,15 @@
 #include "utils/small_array.h"
 
 template <class T>
-__global__ void _rope_kernel(int* pos, void *in, void *out, int size, int dim_model,
-                             int dim_head, int hidden_stride, int pos_stride) {
+__global__ void _rope_kernel(int64_t* pos, void *in, void *out, int dim_model,
+                             int dim_head, int batchsize, int pos_stride) {
     int batch_id = blockIdx.x;
     int target_pos = pos[batch_id * pos_stride + blockIdx.y];
+
     int ith = blockIdx.z * blockDim.x + threadIdx.x;
     int col = ith % dim_head;
-    int offset = batch_id * hidden_stride + blockIdx.y * dim_model;
+    int batch_stride = pos_stride * dim_model;
+    int offset = batch_id * batch_stride + blockIdx.y * dim_model;
 
     if (ith >= dim_model)
         return;
@@ -34,7 +36,7 @@ __global__ void _rope_kernel(int* pos, void *in, void *out, int size, int dim_mo
 #define CASE(T)                                                                \
     _rope_kernel<DT_CUDA<T>::t>                                                \
         <<<gridsize, blocksize, 0, CUDAStream::getCurrentStream()>>>           \
-        (pos, input, output, size, dim_model, dim_head, hidden_stride, pos_stride);
+        (pos, input, output, dim_model, dim_head, batchsize, pos_stride);
 
 #define SWITCH_DTYPE(DTYPE)                                                    \
     switch (DTYPE) {                                                           \
@@ -79,10 +81,10 @@ __global__ void _rope_kernel(int* pos, void *in, void *out, int size, int dim_mo
     }
 
 namespace infini {
-void rope_kernel(int dType, int * pos, void *input, void *output, int size,
-                 int dim_model, int dim_head, int hidden_stride, int pos_stride) {
+void rope_kernel(int dType, int64_t * pos, void *input, void *output,
+                 int dim_model, int dim_head, int batchsize, int pos_stride) {
     dim3 blocksize = dim3(32,1,1);
-    dim3 gridsize = dim3(1, 1, dim_model/32);
+    dim3 gridsize = dim3(batchsize, pos_stride, dim_model/32);
     SWITCH_DTYPE(dType)
 }
 

src/kernels/kunlun/element_wise.cc

@@ -97,14 +97,11 @@ class DivXdnn : public KUNLUNKernelWithoutConfig {
         auto aDim = op->getInputs(0)->getDims();
         auto bSize = op->getInputs(1)->size();
         auto bDim = op->getInputs(1)->getDims();
+        auto dtype = op->getDType();
 
-        // op input a, b is scalar while aDim and b Dim is empty
         if (bDim.size() == 0) {
             bDim.push_back(1);
         }
-        if (aDim.size() == 0) {
-            aDim.push_back(1);
-        }
 
         if (aSize == bSize) {
             // Do ElementWise Sub with no broadcast
@@ -112,9 +109,23 @@ class DivXdnn : public KUNLUNKernelWithoutConfig {
                                               (float *)aData, (float *)bData,
                                               (float *)cData, aSize));
         } else {
-            checkKUNLUNError(xdnn::broadcast_div<float>(
-                context->KUNLUNHandle(), (float *)aData, (float *)bData,
-                (float *)cData, aDim, bDim));
+            // Do broadcast div
+            Shape aligned = infer_broadcast(aDim, bDim);
+            if (aligned == aDim) {
+                // BData need to be broadcasted
+                checkKUNLUNError(xdnn::broadcast_div<float>(
+                    context->KUNLUNHandle(), (float *)aData, (float *)bData,
+                    (float *)cData, aDim, bDim));
+            } else {
+                // Use workspace to broadcast aData
+                KUNLUNPtr wks = context->getWorkspace(bSize * dtype.getSize());
+                checkKUNLUNError(xdnn::broadcast<float>(
+                    context->KUNLUNHandle(), (float *)aData, (float *)wks, aDim,
+                    bDim));
+                checkKUNLUNError(xdnn::div<float>(context->KUNLUNHandle(),
+                                                  (float *)wks, (float *)bData,
+                                                  (float *)cData, bSize));
+            }
         }
         return;
     }

src/kernels/kunlun/unary.cc

@@ -570,7 +570,6 @@ REGISTER_KERNEL(Device::KUNLUN, OpType::Reciprocal, ReciprocalXdnn,
 REGISTER_KERNEL(Device::KUNLUN, OpType::Reshape, CopyXdnn, "Reshape_xdnn");
 REGISTER_KERNEL(Device::KUNLUN, OpType::Flatten, CopyXdnn, "Flatten_xdnn");
 REGISTER_KERNEL(Device::KUNLUN, OpType::Identity, CopyXdnn, "Identity_xdnn");
-REGISTER_KERNEL(Device::KUNLUN, OpType::Squeeze, CopyXdnn, "Squeeze_xdnn");
 REGISTER_KERNEL(Device::KUNLUN, OpType::Abs, AbsXdnn, "Abs_xdnn");
 REGISTER_KERNEL(Device::KUNLUN, OpType::Atan, ATanXdnn, "Atan_xdnn");
 REGISTER_KERNEL(Device::KUNLUN, OpType::Log, LogXdnn, "Log_xdnn");