add kunlun squeeze kernel (#229)

Co-authored-by: Haojie Wang <haojie0429@gmail.com>

.gitmodules

@@ -13,3 +13,6 @@
 [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/distributed/README.md

@@ -1,5 +1,7 @@
 # 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。
@@ -15,3 +17,23 @@ 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/bang_launch.py

@@ -1,35 +1,39 @@
+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=2, help="number of processes per node"
+        "--nproc_per_node", type=int, default=1, 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="/data/onnx_models/llama2/llama_bs1_seq1024.onnx", 
+        "--model", type=str, required=True, help="path to the ONNX model file."
-        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 (
@@ -40,39 +44,46 @@ 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):
+def run_model(model, runtime, world_size=1, rank=0, n=10, data_type="default"):
-    stub = OnnxStub(model, runtime)
+    stub = OnnxStub(model, runtime, matmul_compute_type=data_type)
     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
+    avg_time = (end - begin) / (n * 2)
     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):
+
+def run_and_compare(name, model, runtime, world_size=1, rank=0, data_type="default"):
     results = np.load(f"./data/output.npy")
-    outputs = run_model(model, runtime, world_size, rank)
+    outputs = run_model(model, runtime, world_size, rank, data_type=data_type)
-    print("answer argmax:", np.argmax(results))
+    print("outputs abs mean:", abs(outputs).mean())
-    print("output argmax:", np.argmax(outputs))
+    print("max abs diff:", abs(outputs - results).max())
-    #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
+    name: str, world_size: int, rank: int, local_rank: int, model: onnx.ModelProto, data_type: str
 ):
     dist_name = name + "_dist"
     model = parallel_model(model, world_size, rank)
@@ -85,7 +96,7 @@ 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(
@@ -93,13 +104,12 @@ def start_worker(
         world_size,
         rank,
     )
-    run_and_compare(name, model, runtime, world_size, rank)
+    run_and_compare(name, model, runtime, world_size, rank, data_type)
 
 
-def start_single(name, model):
+def start_single(name, model, data_type):
     runtime = backend.BangRuntime(0)
-    run_and_compare(name, model, runtime)
+    run_and_compare(name, model, runtime, data_type=data_type)
-
 
 def generate_input_output(model):
     os.makedirs(os.path.dirname("./data/"), exist_ok=True)
@@ -132,55 +142,36 @@ 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 = parse_args()
+    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
+    
     model = onnx.load(model_path)
 
     # generate standart output
     if gen_std:
-        print("Generate inputs and outputs.")
+        print(f"generate standard data for {name}.")
-        p = mp.Process(target=generate_input_output, args=[model])
+        # a small vocabulary size to fit all LLM.
-        p.start()
+        generate_input_output(model)
-        p.join()
         return
 
-    # run single process.
+    if nproc_per_node == 1:
-    # use standalone process to isolate cuda.
+        # run single process.
-    print("run model by single MLU.")
+        # use standalone process to isolate bang.
-    p = mp.Process(target=start_single, args=(name, model))
+        print("run model by single MLU.")
-    p.start()
+        # p = mp.Process(target=start_single, args=(name, model, data_type))
-    p.join()
+        # p.start()
+        # p.join()
+        start_single(name, model, data_type)
+        return
 
     # run distributed parallel.
     world_size = nnodes * nproc_per_node
-    print(f"run model by {world_size} MLUs in parallel.")
+    print(f"run model by {world_size} MLU in parallel.")
     workers = [
         mp.Process(
             target=start_worker,
-            args=(name, world_size, rank, rank % nproc_per_node, model),
+            args=(name, world_size, rank, rank % nproc_per_node, model, data_type),
         )
         for rank in range(world_size)
     ]

examples/distributed/bang/run_pytorch_mlu.py

@@ -0,0 +1,249 @@
+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/cuda/cuda_launch.py

@@ -80,18 +80,16 @@ def start_worker(
     name: str, world_size: int, rank: int, local_rank: int, model: onnx.ModelProto, data_type: str
 ):
     dist_name = name + "_dist"
-    #model = parallel_model(model, world_size, rank)
+    model = parallel_model(model, world_size, rank)
-    graph = parallel_model(model, world_size, rank)
+    extern_path = f"./{dist_name}_rank{rank}.pb"
-
+    if os.path.exists(extern_path):
-    # extern_path = f"./{dist_name}_rank{rank}.pb"
+        os.remove(extern_path)
-    # if os.path.exists(extern_path):
+    onnx.save_model(
-    #     os.remove(extern_path)
+        model,
-    # onnx.save_model(
+        f"./{dist_name}_rank{rank}.onnx",
-    #     model,
+        save_as_external_data=True,
-    #     f"./{dist_name}_rank{rank}.onnx",
+        location=extern_path,
-    #     save_as_external_data=True,
+    )
-    #     location=extern_path,
-    # )
     #infer_shapes_path(f"./{dist_name}_rank{rank}.onnx")
     runtime = backend.CudaRuntime(local_rank)
     # print("init comm")
@@ -100,8 +98,7 @@ def start_worker(
         world_size,
         rank,
     )
-    #run_and_compare(name, model, runtime, data_type)
+    run_and_compare(name, model, runtime, data_type)
-    run_and_compare(name, graph, runtime, data_type)
 
 
 def start_single(name, model, data_type):

examples/distributed/cuda/run_pytorch.py

@@ -57,7 +57,7 @@ def get_model(modelname):
     return model, voc_size
 
 def run_pytorch(torch_model, voc_size, batchsize, len):
-    data = np.random.randint(0, voc_size, (batchsize, len), dtype=np.int64)
+    data = np.random.randint(0, voc_size, (batchsize, len), dtype=np.int32)
     np.save("test_inputs", data)
     inputs = torch.from_numpy(data).to("cuda")
     torch_model = torch_model.to("cuda")
@@ -177,7 +177,7 @@ def main():
     if export_path is not None:
         filename = "{}_{}_{}.onnx".format(modelname, batchsize, seqlen)
         path = os.path.join(export_path, filename)
-        param = torch.zeros((batchsize, seqlen), dtype=torch.int64)
+        param = torch.zeros((batchsize, seqlen), dtype=torch.int)
         export_onnx(model, param, path, True)
 
     if data_type == "fp16":

examples/distributed/kunlun/export_onnx.sh

@@ -0,0 +1,14 @@
+ 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

@@ -0,0 +1,280 @@
+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

@@ -0,0 +1,36 @@
+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

@@ -0,0 +1,35 @@
+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

@@ -0,0 +1,245 @@
+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

@@ -1,213 +0,0 @@
-import argparse
-import os
-import time
-import multiprocessing as mp
-from pyinfinitensor.onnx import OnnxStub, backend
-import onnx
-from onnx.external_data_helper import convert_model_to_external_data
-from onnx.shape_inference import infer_shapes_path
-import numpy as np
-from parallel_opt import parallel_model
-
-st_input_dir = "standard/inputs/"
-st_output_dir = "standard/outputs/"
-
-def parse_args():
-    parser = argparse.ArgumentParser(description="launch distributed infinitensor")
-    parser.add_argument("--num_nodes", type=int, default=1, help="number of nodes")
-    parser.add_argument(
-        "--nproc_per_node", type=int, default=2, help="number of processes per node"
-    )
-    parser.add_argument(
-        "--name", type=str, default="test", help="name of this instance."
-    )
-    parser.add_argument(
-        "--model", type=str, default="/data1/shared/panzezhong/llama/fp32/my_llama_fp32.sim.onnx", help="path to the ONNX model file."
-    )
-    parser.add_argument("--batch_size", type=int, default=1, help="batch size.")
-    parser.add_argument("--length", type=int, default=1, help="sequence length.")
-    parser.add_argument(
-        "--gen_std",
-        default=False,
-        action="store_true",
-        help="whether to generate the standard results.",
-    )
-    parser.add_argument(
-        "--run_single",
-        default=False,
-        action="store_true",
-        help="whether run model with single process with standard inputs"
-    )
-    args = parser.parse_args()
-    print("arg setting: ", args)
-    return (
-        args.num_nodes,
-        args.nproc_per_node,
-        args.name,
-        args.model,
-        args.batch_size,
-        args.length,
-        args.gen_std,
-        args.run_single
-    )
-
-
-def run_model(model, runtime, world_size=1, rank=0, n=10):
-    stub = OnnxStub(model, runtime)
-    load_inputs(stub, world_size, rank)
-    # stub.tune()
-    stub.run()
-    # get outputs
-    time.sleep(0.01)
-    outputs = next(stub.outputs.values().__iter__()).copyout_numpy()
-
-    # bench
-    begin = time.time()
-    for _ in range(n):
-        stub.run()
-    end = time.time()
-    avg_time = (end - begin) / n
-    print(f"average time: {avg_time}")
-    return outputs
-
-
-
-def run_and_compare(name, model, runtime, world_size=1, rank = 0):
-    results = np.load(os.path.join(st_output_dir,f"output.npy"))
-    outputs = run_model(model, runtime, world_size, rank)
-    print(outputs[:100])
-    if np.isnan(outputs).any():
-        print("Nan in output")
-    print("answer argmax:", np.argmax(results))
-    print("output argmax:", np.argmax(outputs))
-    #np.testing.assert_allclose(outputs, results, rtol=1e-3, atol=1e-3)
-    getDiff(results, outputs)
-
-
-def start_worker(
-    name: str, world_size: int, rank: int, local_rank: int, model: onnx.ModelProto
-):
-    dist_name = name + "_dist"
-    model = parallel_model(model, world_size, rank)
-    extern_path = f"./{dist_name}_rank{rank}.pb"
-    if os.path.exists(extern_path):
-        os.remove(extern_path)
-    onnx.save_model(
-        model,
-        f"./{dist_name}_rank{rank}.onnx",
-        save_as_external_data=True,
-        location=extern_path,
-    )
-    infer_shapes_path(f"./{dist_name}_rank{rank}.onnx")
-    runtime = backend.KUNLUNRuntime(local_rank)
-    # print("init comm")
-    runtime.init_comm(
-        dist_name,
-        world_size,
-        rank,
-    )
-    run_and_compare(name, model, runtime, world_size, rank)
-
-
-def start_single(name, model):
-    runtime = backend.KUNLUNRuntime(0)
-    run_and_compare(name, model, runtime)
-
-
-def generate_input_output(model):
-    runtime = backend.KUNLUNRuntime(0)
-    stub = OnnxStub(model, runtime)
-    position_id = 0
-    for i, (name, tensor) in enumerate(stub.inputs.items()):
-        input = tensor.copyout_numpy()
-        if np.issubdtype(input.dtype, np.integer):
-            if input.size == 1:
-                # input = np.array([position_id])
-                input = np.random.randint(0,2,size=input.shape, dtype=input.dtype)
-            else:
-                input = np.random.randint(0,2,size=input.shape, dtype=input.dtype)
-        elif input.dtype == np.bool_:
-            input = np.random.randint(0,2,size=input.shape) > 0
-        else:
-            if i == 0:
-                input = np.ones(input.shape).astype(input.dtype)
-                position_id = input.shape[-1] - 1
-            else:
-                input = np.random.rand(*input.shape).astype(input.dtype)
-        tensor.copyin_numpy(input)
-        np.save(os.path.join(st_input_dir, f"input_{i}"), input)
-    stub.run()
-    # print(stub.outputs)
-    time.sleep(0.01)
-    output = next(stub.outputs.values().__iter__()).copyout_numpy()
-    print(output[:100])
-    if np.isnan(output).any():
-        print("Nan in output")
-    np.save(os.path.join(st_output_dir, f"output"), output)
-
-
-def load_inputs(stub, world_size=1, rank=0):
-    for i, (name, tensor) in enumerate(stub.inputs.items()):
-        input = np.load(os.path.join(st_input_dir, f"input_{i}.npy"))
-        if all(x == y for x,y in zip(input.shape,tensor.shape())):
-            tensor.copyin_numpy(input)
-        else:
-            tensor.copyin_numpy(np.hsplit(input, world_size)[rank])
-
-
-def getDiff(base, test):
-    absolute_diff = np.abs(np.subtract(base, test))
-    max_absolute_diff = np.max(absolute_diff)
-
-    baseCopy = base.astype(np.float64).ravel()
-    testCopy = test.astype(np.float64).ravel()
-    upValue = np.sum(np.abs(baseCopy - testCopy))
-    downValue = np.sum(np.abs(baseCopy)) + np.float64(1e-9)
-    max_relative_diff = upValue / downValue
-    print(f"Max absolute difference: {max_absolute_diff}\nMax relative difference: {max_relative_diff}")
-
-    return max_absolute_diff, max_relative_diff
-
-
-def main():
-    nnodes, nproc_per_node, name, model_path, bs, length, gen_std, run_single = parse_args()
-
-    model = onnx.load(model_path)
-
-    # generate standart output
-    if gen_std:
-        print("Generate inputs and outputs.")
-        p = mp.Process(target=generate_input_output, args=[model])
-        p.start()
-        p.join()
-        return
-
-    # # run single process.
-    # # use standalone process to isolate cuda.
-    if run_single:
-        print("run model by single GPU.")
-        p = mp.Process(target=start_single, args=(name, model))
-        p.start()
-        p.join()
-        return 
-
-    # run distributed parallel.
-    world_size = nnodes * nproc_per_node
-    print(f"run model by {world_size} GPU in parallel.")
-    workers = [
-        mp.Process(
-            target=start_worker,
-            args=(name, world_size, rank, rank % nproc_per_node, model),
-        )
-        for rank in range(world_size)
-    ]
-
-    for w in workers:
-        w.start()
-
-    for w in workers:
-        w.join()
-
-
-if __name__ == "__main__":
-    main()

examples/distributed/onnxsim_large_model

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

examples/distributed/parallel_opt.py

@@ -110,7 +110,6 @@ 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
@@ -243,7 +242,6 @@ def parallel_model(model: ModelProto, tp_world_size: int = 1, tp_rank: int = 0):
         tt = output.type.tensor_type
         if tt.HasField("shape"):
             tt.ClearField("shape")
-    #model = helper.make_model(graph)
+    model = helper.make_model(graph)
     #model = onnx.shape_inference.infer_shapes(model)
-    #return model
+    return model
-    return graph

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 = 3llu << 30; // 3 GB
+        size_t workspaceSize = 2llu << 30; // 2 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_ex((void **)&ptr, size, XPUMemoryKind::XPU_MEM_MAIN));
+            xpu_malloc((void **)&ptr, size, XPUMemoryKind::XPU_MEM_HBM));
         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(10),
+                _IT_ASSERT_2(now < begin + std::chrono::seconds(100),
-                             "time limit (10s) exceeded.");
+                             "time limit (100s) exceeded.");
                 std::this_thread::sleep_for(std::chrono::milliseconds(100));
             }
             std::ifstream ifs(filePath, std::ios::binary);

pyinfinitensor/src/pyinfinitensor/onnx.py

@@ -6,7 +6,6 @@ from onnx import (
     AttributeProto,
     TensorShapeProto,
     ValueInfoProto,
-    GraphProto,
 )
 from onnx.helper import (
     make_node,
@@ -41,27 +40,22 @@ class OnnxStub:
 
     def __init__(
         self,
-        model: ModelProto | GraphProto,
+        model: ModelProto,
         runtime,
         use_naive_allocator: bool = False,
         matmul_compute_type: str = "default",
     ):
-        if isinstance(model, ModelProto):
+        # We use some user-defined operators for distributed inference
-            # We use some user-defined operators for distributed inference
+        try:
-            try:
+            # onnx simplifier performs inplace simplify
-                # onnx simplifier performs inplace simplify
+            model_simp, check = simplify(copy.deepcopy(model))
-                model_simp, check = simplify(copy.deepcopy(model))
+            if check:
-                if check:
+                model = model_simp
-                    model = model_simp
+        except ValidationError:
-            except ValidationError:
+            pass
-                pass
+        except RuntimeError:
-            except RuntimeError:
+            pass
-                pass
+
-            graph = model.graph
-        elif isinstance(model, GraphProto):
-            graph = model
-        else:
-            raise RuntimeError("Expect onnx ModelProto or GraphProto as input.")
         self.inputs: Dict[str, backend.Tensor] = {}
         self.outputs: Dict[str, backend.Tensor] = {}
         self.tensors: Dict[str, backend.Tensor] = {}
@@ -73,12 +67,10 @@ class OnnxStub:
         # except:
         #     warnings.warn("infer_shapes failed.")
         self.handler = backend.GraphHandler(runtime)
-        self.init_graph(graph, matmul_compute_type)
 
-    def init_graph(self, graph: GraphProto, matmul_compute_type: str):
         # 处理重名和匿名算子
         names = {}
-        for node in graph.node:
+        for node in model.graph.node:
             if node.name == "":
                 node.name = "missing_name(" + node.op_type + ")"
             if node.name in names:
@@ -88,11 +80,11 @@ class OnnxStub:
                 names[node.name] = 0
         # 拓扑排序
         sorted_nodes = []
-        known_edge = set(t.name for t in graph.input)
+        known_edge = set(t.name for t in model.graph.input)
-        known_edge.update(t.name for t in graph.initializer)
+        known_edge.update(t.name for t in model.graph.initializer)
-        while len(sorted_nodes) < len(graph.node):
+        while len(sorted_nodes) < len(model.graph.node):
             updated = False
-            for i, node in enumerate(graph.node):
+            for i, node in enumerate(model.graph.node):
                 if all(t in known_edge for t in node.input):
                     node.name = str(len(sorted_nodes)) + "_" + node.name
                     sorted_nodes.append(i)
@@ -106,13 +98,13 @@ class OnnxStub:
         tensors: Dict[str, backend.Tensor] = dict()
         data: Dict[str, TensorProto] = dict()
 
-        for initializer in graph.initializer:
+        for initializer in model.graph.initializer:
             dims = [d for d in initializer.dims]
             tensors[initializer.name] = self.handler.tensor(dims, initializer.data_type)
             data[initializer.name] = initializer
             tensors[initializer.name].set_weight()
 
-        for input in graph.input:
+        for input in model.graph.input:
             dims = _take_shape_dim(input.type.tensor_type.shape)
             if input.name not in tensors.keys():
                 tensors[input.name] = self.handler.tensor(
@@ -122,7 +114,7 @@ class OnnxStub:
 
 
         for node_idx in sorted_nodes:
-            node = graph.node[node_idx]
+            node = model.graph.node[node_idx]
             if node.op_type == "Conv":
                 attributes = _parse_attribute(
                     node,
@@ -975,7 +967,7 @@ class OnnxStub:
                     tensors[node.input[0]],
                     tensors.get(node.output[0]),
                 )
-            elif node.op_type == "Constant":
+            elif node.op_type in ["Constant", "ConstantOfShape"]:
                 output_name = node.output[0]
                 attributes = _parse_attribute(node)
                 tensor = attributes["value"]
@@ -1002,7 +994,7 @@ class OnnxStub:
             else:
                 raise Exception('Unsupported operator "{}"'.format(node.op_type))
 
-        for output in graph.output:
+        for output in model.graph.output:
             tensors[output.name].set_output()
         ################################
         # Allocate memory space for data
@@ -1015,7 +1007,7 @@ class OnnxStub:
         for name, obj in tensors.items():
             tensor = data.get(name)
             if tensor == None:
-                if any(input.name == name for input in graph.input):
+                if any(input.name == name for input in model.graph.input):
                     self.inputs[name] = obj
             else:
                 self.initializer[obj.fuid()] = tensor
@@ -1041,7 +1033,7 @@ class OnnxStub:
         for name, obj in tensors.items():
             self.tensors[name] = obj
 
-        for output in graph.output:
+        for output in model.graph.output:
             self.outputs[output.name] = tensors[output.name]
 
     def to_onnx(self, name: str) -> ModelProto:

src/kernels/bang/cast.cc

@@ -199,6 +199,24 @@ 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

@@ -19,14 +19,16 @@ 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();
 
-        cnnlTensorDescriptor_t inDesc, fiterDesc, outDesc;
+        Shape outMeanDims(outDims);
+        outMeanDims.erase(outMeanDims.begin() + axis);
 
+        cnnlTensorDescriptor_t inDesc, fiterDesc, outDesc, outMeanDesc;
         checkCnnlError(cnnlCreateTensorDescriptor(&inDesc));
         checkCnnlError(cnnlSetTensorDescriptor(
             inDesc, CNNL_LAYOUT_ARRAY, cnnlDataTypeConvert(op->getDType()),
@@ -39,15 +41,23 @@ 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,
-            inDesc, NULL, NULL);
+            outMeanDesc, meanData, rstdData);
 
         if (stat != CNNL_STATUS_SUCCESS)
             return;

src/kernels/bang/matmul.cc

@@ -66,6 +66,13 @@ 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/kunlun/element_wise.cc

@@ -97,11 +97,14 @@ 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
@@ -109,23 +112,9 @@ class DivXdnn : public KUNLUNKernelWithoutConfig {
                                               (float *)aData, (float *)bData,
                                               (float *)cData, aSize));
         } else {
-            // Do broadcast div
+            checkKUNLUNError(xdnn::broadcast_div<float>(
-            Shape aligned = infer_broadcast(aDim, bDim);
+                context->KUNLUNHandle(), (float *)aData, (float *)bData,
-            if (aligned == aDim) {
+                (float *)cData, aDim, bDim));
-                // 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

@@ -570,6 +570,7 @@ 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");