Tutel MoE: An Optimized Mixture-of-Experts Implementation

Last update: Dec 29, 2022

Overview

Project Tutel

Tutel MoE: An Optimized Mixture-of-Experts Implementation.

Supported Framework: Pytorch
Supported GPUs: CUDA(fp32 + fp16), ROCm(fp32)

How to setup Tutel MoE for Pytorch:

* Install Online:

        $ python3 -m pip install --user --upgrade git+https://github.com/microsoft/[email protected]

* Build from Source:

        $ git clone https://github.com/microsoft/tutel
        $ python3 ./tutel/setup.py install --user

How to use Tutel-optimized MoE in Pytorch:

* Tutel MoE Example:

        moe_layer = MOELayer('Top2Gate', model_dim, experts={
            'count_per_node': 2,
            'type': 'ffn', 'hidden_size_per_expert': 1024, 'activation_fn': lambda x: F.relu(x), ..
        })
        y = moe_layer(x)

* Usage of MOELayer Args:

        gate             : the string type of MOE gate, e.g: Top1Gate, Top2Gate, Top3Gate, Top4Gate
        model_dim        : the number of channels for MOE's input tensor
        experts          : a dict-type config for builtin expert network, or a torch.nn.Module-type custom expert network
        fp32_gate        : option of enabling mixed precision for gate network
        scan_expert_func : allow users to specify a lambda function to iterate each experts param, e.g. `scan_expert_func = lambda name, param: setattr(param, 'expert', True)`
        result_func      : allow users to specify a lambda function to format the MoE output and aux_loss, e.g. `result_func = lambda output: (output, output.l_aux)`
        group            : specify the explicit communication group of all_to_all
        seeds            : a tuple containing a pair of int to specify manual seed of (shared params, local params)

* Usage of dict-type Experts Config:

        count_per_node   : the number of local experts per device (by default, the value is 1 if not specified)
        type             : available built-in experts implementation, e.g: ffn
        hidden_size_per_expert : the hidden size between two linear layers for each expert (used for type == 'ffn' only)
        activation_fn    : the custom-defined activation function between two linear layers (used for type == 'ffn' only)

* Running MoE Hello World Model by torch.distributed.all_reduce:

        $ python3 -m torch.distributed.launch --nproc_per_node=1 ./examples/helloworld.py

* Running MoE Hello World Model by torch.nn.parallel.DistributedDataParallel (requires torch >= 1.8.0):

        $ python3 -m torch.distributed.launch --nproc_per_node=1 ./examples/helloworld_ddp.py

Contributing

This project welcomes contributions and suggestions. Most contributions require you to agree to a Contributor License Agreement (CLA) declaring that you have the right to, and actually do, grant us the rights to use your contribution. For details, visit https://cla.opensource.microsoft.com.

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This project has adopted the Microsoft Open Source Code of Conduct. For more information see the Code of Conduct FAQ or contact [email protected] with any additional questions or comments.

Trademarks

This project may contain trademarks or logos for projects, products, or services. Authorized use of Microsoft trademarks or logos is subject to and must follow Microsoft's Trademark & Brand Guidelines. Use of Microsoft trademarks or logos in modified versions of this project must not cause confusion or imply Microsoft sponsorship. Any use of third-party trademarks or logos are subject to those third-party's policies.

Comments

question about how to set data Parallelism

Thanks for your contributions. Tutel is definitely a great work! But I have difficulty applying this Tutel to a new framework. If I have 8GPUs, And I want to set the number of experts to 4 while there are 2 local experts on a device (GPU). Thus the GPUs will be divided into 4 groups, which each has 2 GPUs and contain 4 experts. I think the GPU0, GPU2, GPU4, and GPU6 have the experts with the same parameters. How can I implement this setting?

opened by Lechatelia 13
100x slower when using 4nodes than 1node to run the helloworld_ddp example

Hello, I meet a problem that it is 100x slower when using 2node than 1node to run the helloworld_ddp example. I compile tutel with cuda11.3, pytorch 1.11 and nccl 2.9.9 on a nvidia-a100 GPU cluster with 100G IB. When I run tutel.examples.helloworld_ddp on a single node with 8 gpus and batch size 16, the speed meets the results in your table(step_time = 0.012315). But when I test with 4nodes, the step time becomes about 1 second, which is about 100x slower. Other multi-node tasks can normally run on my cluster, so I think maybe something is wrong with the environment when I build the project. It will be very nice if you can share the detailed environment information, such as the pytorch version, cuda version, g++ version, etc. Thanks.
libnccl issue

opened by a157801 12
The output of nccl_all_to_all_scatter_async may be incomplete when num_local_experts>1.

Describe the bug The output of nccl_all_to_all_scatter_async may be incomplete.

To Reproduce Steps to reproduce the behavior:

on host0(master): SKIP_EXPERT=1 python3 -m torch.distributed.launch --nproc_per_node=1 --nnodes=2 --node_rank=0 --master_addr=host0 -m tutel.examples.helloworld --batch_size=4 --num_tokens=1 --model_dim=2 --hidden_size=2 --num_steps=1 --a2a_ffn_overlap_degree=1 on host1: SKIP_EXPERT=1 python3 -m torch.distributed.launch --nproc_per_node=1 --nnodes=2 --node_rank=1 --master_addr=host0 -m tutel.examples.helloworld --batch_size=4 --num_tokens=1 --model_dim=2 --hidden_size=2 --num_steps=1 --a2a_ffn_overlap_degree=1

Log The value of https://github.com/microsoft/tutel/blob/2c0cad3a742ecf4c0b0a989d6db629fcc2022bc0/tutel/impls/moe_layer.py#L244 tensor([[[ 1.5410, -0.2934], [-1.0845, -1.3986]], [[ 1.5410, -0.2934], [ 0.4033, 0.8380]], [[-2.1788, 0.5684], [-1.0845, -1.3986]], [[ 0.4033, 0.8380], [-2.1788, 0.5684]]], device='cuda:0')

The value of https://github.com/microsoft/tutel/blob/2c0cad3a742ecf4c0b0a989d6db629fcc2022bc0/tutel/impls/moe_layer.py#L253 tensor([[[ 1.5410, -0.2934], [-1.0845, -1.3986]], [[ 1.5410, -0.2934], [ 0.4033, 0.8380]], [[-2.1788, 0.5684], [-1.0845, -1.3986]], [[ 0.4033, 0.8380], [-2.1788, 0.5684]]], device='cuda:0')

This is the result I expect. However, when on host0(master): SKIP_EXPERT=1 python3 -m torch.distributed.launch --nproc_per_node=1 --nnodes=2 --node_rank=0 --master_addr=host0 -m tutel.examples.helloworld --batch_size=4 --num_tokens=1 --model_dim=2 --hidden_size=2 --num_steps=1 --a2a_ffn_overlap_degree=2 on host1: SKIP_EXPERT=1 python3 -m torch.distributed.launch --nproc_per_node=1 --nnodes=2 --node_rank=1 --master_addr=host0 -m tutel.examples.helloworld --batch_size=4 --num_tokens=1 --model_dim=2 --hidden_size=2 --num_steps=1 --a2a_ffn_overlap_degree=2

The value of https://github.com/microsoft/tutel/blob/2c0cad3a742ecf4c0b0a989d6db629fcc2022bc0/tutel/impls/moe_layer.py#L244 tensor([[[ 1.5410, -0.2934], [-1.0845, -1.3986]], [[ 1.5410, -0.2934], [ 0.4033, 0.8380]], [[-2.1788, 0.5684], [-1.0845, -1.3986]], [[ 0.4033, 0.8380], [-2.1788, 0.5684]]], device='cuda:0')

The value of https://github.com/microsoft/tutel/blob/2c0cad3a742ecf4c0b0a989d6db629fcc2022bc0/tutel/impls/moe_layer.py#L249 tensor([[[ 0.0000, 0.0000], [ 0.0000, 0.0000]], [[ 1.5410, -0.2934], [ 0.4033, 0.8380]], [[ 0.0000, 0.0000], [ 0.0000, 0.0000]], [[ 0.4033, 0.8380], [-2.1788, 0.5684]]], device='cuda:0')

It seems incomplete.

The possible code is: https://github.com/microsoft/tutel/blob/2c0cad3a742ecf4c0b0a989d6db629fcc2022bc0/tutel/custom/custom_kernel.cpp#L472-L489

It looks like the NCCL group keeps only the last send-recv pair in each peer. There is no same problem when num_local_experts=1.
wontfix

opened by Fragile-azalea 11

Cannot Import JIT optimized kernels?

I used TUTEL for a while and it works greatly fine. But today I updated my environment and reinstall tutel, I found it crashed during importing module. Do you have any idea on why this happen? Thanks!

>>> from tutel import moe as tutel_moe
Traceback (most recent call last):
  File "/mnt/lustre/bli/anaconda3/envs/scale/lib/python3.9/site-packages/tutel/impls/jit_compiler.py", line 8, in <module>
    import tutel_custom_kernel
ImportError: /mnt/lustre/bli/anaconda3/envs/scale/lib/python3.9/site-packages/tutel_custom_kernel.cpython-39-x86_64-linux-gnu.so: undefined symbol: _ZNSt15__exception_ptr13exception_ptr9_M_addrefEv

During handling of the above exception, another exception occurred:

Traceback (most recent call last):
  File "<stdin>", line 1, in <module>
  File "/mnt/lustre/bli/anaconda3/envs/scale/lib/python3.9/site-packages/tutel/moe.py", line 6, in <module>
    from .jit_kernels.gating import fast_cumsum_sub_one
  File "/mnt/lustre/bli/anaconda3/envs/scale/lib/python3.9/site-packages/tutel/jit_kernels/gating.py", line 7, in <module>
    from ..impls.jit_compiler import tutel_custom_kernel
  File "/mnt/lustre/bli/anaconda3/envs/scale/lib/python3.9/site-packages/tutel/impls/jit_compiler.py", line 10, in <module>
    raise Exception("Cannot import JIT optimized kernels. Did you forget to install Custom Kernel Extension?")
Exception: Cannot import JIT optimized kernels. Did you forget to install Custom Kernel Extension?

opened by Luodian 11

Cannot compile tutel kernels and got runtime error

I have installed tutel on my machine and have set up the related environment variables, such as the $CUDA_HOME and $CFLAGS. However, when I try to run examples/hello_world.py, I got the following error:

[E custom_kernel.cpp:124] default_program(1): catastrophic error: cannot open source file "cuda_runtime.h"

1 catastrophic error detected in the compilation of "default_program". Compilation terminated. Failed to use NVRTC for JIT compilation in this Pytorch version, try another approach using CUDA compiler.. (To always disable NVRTC, please: export USE_NVRTC=0)

File "/private/home/hyhuang/.local/lib/python3.9/site-packages/tutel/impls/jit_compiler.py", line 26, in func tutel_custom_kernel.invoke(inputs, ctx) RuntimeError: (true) == (fp != nullptr)INTERNAL ASSERT FAILED at "/tmp/pip-req-build-pcbbciia/tutel/custom/custom_kernel.cpp":40, please report a bug to PyTorch. CHECK_EQ fails.

I am using PyTorch 1.10.1 + CUDA 11.3. Is there any other parameter I should fix to use tutel?

opened by hyhuang00 10

module 'tutel_custom_kernel' has no attribute 'inject_source'

My cuda version is 11.4, python version is 3.6.5 Following the requirement, my torch and torchvision versions are torch==1.10.0+cu113 and torchvision==0.11.1+cu113. Then I run git clone https://github.com/microsoft/tutel --branch v0.1.x python ./tutel/setup.py install --user then run the tutorial: python ./tutel/examples/helloworld.py --batch_size=16 but meet the following error:

Traceback (most recent call last):
  File "./tutel/examples/helloworld.py", line 118, in <module>
    output = model(x)
  File "/home/fanj/.local/lib/python3.6/site-packages/torch/nn/modules/module.py", line 1102, in _call_impl
    return forward_call(*input, **kwargs)
  File "./tutel/examples/helloworld.py", line 85, in forward
    result = self._moe_layer(input)
  File "/home/fanj/.local/lib/python3.6/site-packages/torch/nn/modules/module.py", line 1102, in _call_impl
    return forward_call(*input, **kwargs)
  File "/home/fanj/tutel/tutel/impls/moe_layer.py", line 424, in forward
    result_output, l_aux = self.gates[gate_index].apply_on_expert_fn(reshaped_input, self)
  File "/home/fanj/tutel/tutel/impls/moe_layer.py", line 73, in apply_on_expert_fn
    critical_data, l_loss = extract_critical(gates, self.top_k, self.capacity_factor, self.fp32_gate, self.batch_prioritized_routing)
  File "/home/fanj/tutel/tutel/impls/fast_dispatch.py", line 163, in extract_critical
    locations1 = compute_location(masks_se[0])
  File "/home/fanj/tutel/tutel/jit_kernels/gating.py", line 83, in fast_cumsum_sub_one
    return get_cumsum_kernel(int(data.size(0)), int(data.size(1)))(data)
  File "/home/fanj/tutel/tutel/jit_kernels/gating.py", line 68, in get_cumsum_kernel
    ''')
  File "/home/fanj/tutel/tutel/impls/jit_compiler.py", line 31, in generate_kernel
    return JitCompiler.create_raw(template)
  File "/home/fanj/tutel/tutel/impls/jit_compiler.py", line 21, in create_raw
    __ctx__ = tutel_custom_kernel.inject_source(source)
AttributeError: module 'tutel_custom_kernel' has no attribute 'inject_source'

Do you know how to solve this problem? Thank you very much!

opened by LisaWang0306 10

New Tutel checkpoint loading is incompatible with old models

Hi, I have been using Swin-MoE pre-trained models with Tutel. However, after the recent update in Tutel library in model loading format, the pre-trained model has different dict structure than the current required expert model resulting in loading error. Can you please create compatible versions of these released pre-trained models? or release any script to do so? Any help would be highly appreciated.

opened by jinga-lala 7

fast_cumsum_sub_one fails when the module is wrapped by ORTModule

As title, I got the following errors when my module is wrapped with ORTModule

[E custom_kernel.cpp:123] default_program(14): error: identifier "tensor" is undefined

1 error detected in the compilation of "default_program".
 Failed to use NVRTC for JIT compilation in this Pytorch version, try another approach using CUDA compiler.. (To always disable NVRTC, please: export USE_NVRTC=0)
/tmp/torch-tutel-o0geuH.cu(14): error: identifier "tensor" is undefined

1 error detected in the compilation of "/tmp/torch-tutel-o0geuH.cu".
/opt/conda/lib/python3.7/site-packages/onnxruntime/training/ortmodule/_training_manager.py:224: UserWarning: Fast path enabled - skipping checks. Rebuild graph: True, Execution agent: True, Device check: True
  f" Device check: {self._skip_check.is_set(_SkipCheck.SKIP_CHECK_DEVICE)}", UserWarning)
RuntimeError: There was an error while exporting the PyTorch model to ONNX:

Traceback (most recent call last):
  File "/opt/conda/lib/python3.7/site-packages/onnxruntime/training/ortmodule/_utils.py", line 254, in get_exception_as_string
    raise exception
  File "/opt/conda/lib/python3.7/site-packages/onnxruntime/training/ortmodule/_graph_execution_manager.py", line 389, in _get_exported_model
    **self._export_extra_kwargs)
  File "/opt/conda/lib/python3.7/site-packages/torch/onnx/__init__.py", line 280, in export
    custom_opsets, enable_onnx_checker, use_external_data_format)
  File "/opt/conda/lib/python3.7/site-packages/torch/onnx/utils.py", line 94, in export
    use_external_data_format=use_external_data_format)
  File "/opt/conda/lib/python3.7/site-packages/torch/onnx/utils.py", line 695, in _export
    dynamic_axes=dynamic_axes)
  File "/opt/conda/lib/python3.7/site-packages/torch/onnx/utils.py", line 459, in _model_to_graph
    _retain_param_name)
  File "/opt/conda/lib/python3.7/site-packages/torch/onnx/utils.py", line 422, in _create_jit_graph
    graph, torch_out = _trace_and_get_graph_from_model(model, args)
  File "/opt/conda/lib/python3.7/site-packages/torch/onnx/utils.py", line 373, in _trace_and_get_graph_from_model
    torch.jit._get_trace_graph(model, args, strict=False, _force_outplace=False, _return_inputs_states=True)
  File "/opt/conda/lib/python3.7/site-packages/torch/jit/_trace.py", line 1160, in _get_trace_graph
    outs = ONNXTracedModule(f, strict, _force_outplace, return_inputs, _return_inputs_states)(*args, **kwargs)
  File "/opt/conda/lib/python3.7/site-packages/torch/nn/modules/module.py", line 1051, in _call_impl
    return forward_call(*input, **kwargs)
  File "/opt/conda/lib/python3.7/site-packages/torch/jit/_trace.py", line 132, in forward
    self._force_outplace,
  File "/opt/conda/lib/python3.7/site-packages/torch/jit/_trace.py", line 118, in wrapper
    outs.append(self.inner(*trace_inputs))
  File "/opt/conda/lib/python3.7/site-packages/torch/nn/modules/module.py", line 1051, in _call_impl
    return forward_call(*input, **kwargs)
  File "/opt/conda/lib/python3.7/site-packages/torch/nn/modules/module.py", line 1039, in _slow_forward
    result = self.forward(*input, **kwargs)
  File "/opt/conda/lib/python3.7/site-packages/onnxruntime/training/ortmodule/_io.py", line 430, in forward
    return _transform_output_to_flat_tuple(self._original_module(*new_args, **new_kwargs))
  File "/opt/conda/lib/python3.7/site-packages/torch/nn/modules/module.py", line 1051, in _call_impl
    return forward_call(*input, **kwargs)
  File "/opt/conda/lib/python3.7/site-packages/torch/nn/modules/module.py", line 1039, in _slow_forward
    result = self.forward(*input, **kwargs)
  File "test_ort.py", line 17, in forward
    x_cumsum = fast_cumsum_sub_one(x, dim=0)
  File "/opt/conda/lib/python3.7/site-packages/tutel/jit_kernels/gating.py", line 83, in fast_cumsum_sub_one
    return get_cumsum_kernel(data.size(0), data.size(1))(data)
  File "/opt/conda/lib/python3.7/site-packages/tutel/jit_kernels/gating.py", line 72, in optimized_cumsum
    base_kernel(mask1.to(torch.int32).contiguous(), locations1)
  File "/opt/conda/lib/python3.7/site-packages/tutel/impls/jit_compiler.py", line 26, in func
    tutel_custom_kernel.invoke(inputs, __ctx__)
RuntimeError: (true) == (fp != nullptr)INTERNAL ASSERT FAILED at "/tmp/pip-req-build-qjgbz25n/tutel/custom/custom_kernel.cpp":39, please report a bug to PyTorch. CHECK_EQ fails.


The above exception was the direct cause of the following exception:

Traceback (most recent call last):
  File "test_ort.py", line 24, in <module>
    output = cumsum_module(input)
  File "/opt/conda/lib/python3.7/site-packages/torch/nn/modules/module.py", line 1051, in _call_impl
    return forward_call(*input, **kwargs)
  File "/opt/conda/lib/python3.7/site-packages/onnxruntime/training/ortmodule/_utils.py", line 309, in _forward
    return ortmodule._torch_module.forward(*inputs, **kwargs)
  File "/opt/conda/lib/python3.7/site-packages/onnxruntime/training/ortmodule/_utils.py", line 289, in _forward
    torch_module_ort.is_training()).forward(*inputs, **kwargs)
  File "/opt/conda/lib/python3.7/site-packages/onnxruntime/training/ortmodule/_training_manager.py", line 292, in forward
    log_level=self._debug_options.logging.log_level)
  File "/opt/conda/lib/python3.7/site-packages/onnxruntime/training/ortmodule/_fallback.py", line 151, in handle_exception
    raise exception
  File "/opt/conda/lib/python3.7/site-packages/onnxruntime/training/ortmodule/_training_manager.py", line 231, in forward
    build_gradient_graph = self._export_model(*inputs, **kwargs)
  File "/opt/conda/lib/python3.7/site-packages/onnxruntime/training/ortmodule/_graph_execution_manager.py", line 322, in _export_model
    schema, *inputs, **kwargs)
  File "/opt/conda/lib/python3.7/site-packages/onnxruntime/training/ortmodule/_graph_execution_manager.py", line 392, in _get_exported_model
    RuntimeError(f'There was an error while exporting the PyTorch model to ONNX: '
  File "/opt/conda/lib/python3.7/site-packages/onnxruntime/training/ortmodule/_fallback_exceptions.py", line 72, in wrap_exception
    raise new_exception(raised_exception) from raised_exception
onnxruntime.training.ortmodule._fallback_exceptions.ORTModuleONNXModelException: There was an error while exporting the PyTorch model to ONNX:

Traceback (most recent call last):
  File "/opt/conda/lib/python3.7/site-packages/onnxruntime/training/ortmodule/_utils.py", line 254, in get_exception_as_string
    raise exception
  File "/opt/conda/lib/python3.7/site-packages/onnxruntime/training/ortmodule/_graph_execution_manager.py", line 389, in _get_exported_model
    **self._export_extra_kwargs)
  File "/opt/conda/lib/python3.7/site-packages/torch/onnx/__init__.py", line 280, in export
    custom_opsets, enable_onnx_checker, use_external_data_format)
  File "/opt/conda/lib/python3.7/site-packages/torch/onnx/utils.py", line 94, in export
    use_external_data_format=use_external_data_format)
  File "/opt/conda/lib/python3.7/site-packages/torch/onnx/utils.py", line 695, in _export
    dynamic_axes=dynamic_axes)
  File "/opt/conda/lib/python3.7/site-packages/torch/onnx/utils.py", line 459, in _model_to_graph
    _retain_param_name)
  File "/opt/conda/lib/python3.7/site-packages/torch/onnx/utils.py", line 422, in _create_jit_graph
    graph, torch_out = _trace_and_get_graph_from_model(model, args)
  File "/opt/conda/lib/python3.7/site-packages/torch/onnx/utils.py", line 373, in _trace_and_get_graph_from_model
    torch.jit._get_trace_graph(model, args, strict=False, _force_outplace=False, _return_inputs_states=True)
  File "/opt/conda/lib/python3.7/site-packages/torch/jit/_trace.py", line 1160, in _get_trace_graph
    outs = ONNXTracedModule(f, strict, _force_outplace, return_inputs, _return_inputs_states)(*args, **kwargs)
  File "/opt/conda/lib/python3.7/site-packages/torch/nn/modules/module.py", line 1051, in _call_impl
    return forward_call(*input, **kwargs)
  File "/opt/conda/lib/python3.7/site-packages/torch/jit/_trace.py", line 132, in forward
    self._force_outplace,
  File "/opt/conda/lib/python3.7/site-packages/torch/jit/_trace.py", line 118, in wrapper
    outs.append(self.inner(*trace_inputs))
  File "/opt/conda/lib/python3.7/site-packages/torch/nn/modules/module.py", line 1051, in _call_impl
    return forward_call(*input, **kwargs)
  File "/opt/conda/lib/python3.7/site-packages/torch/nn/modules/module.py", line 1039, in _slow_forward
    result = self.forward(*input, **kwargs)
  File "/opt/conda/lib/python3.7/site-packages/onnxruntime/training/ortmodule/_io.py", line 430, in forward
    return _transform_output_to_flat_tuple(self._original_module(*new_args, **new_kwargs))
  File "/opt/conda/lib/python3.7/site-packages/torch/nn/modules/module.py", line 1051, in _call_impl
    return forward_call(*input, **kwargs)
  File "/opt/conda/lib/python3.7/site-packages/torch/nn/modules/module.py", line 1039, in _slow_forward
    result = self.forward(*input, **kwargs)
  File "test_ort.py", line 17, in forward
    x_cumsum = fast_cumsum_sub_one(x, dim=0)
  File "/opt/conda/lib/python3.7/site-packages/tutel/jit_kernels/gating.py", line 83, in fast_cumsum_sub_one
    return get_cumsum_kernel(data.size(0), data.size(1))(data)
  File "/opt/conda/lib/python3.7/site-packages/tutel/jit_kernels/gating.py", line 72, in optimized_cumsum
    base_kernel(mask1.to(torch.int32).contiguous(), locations1)
  File "/opt/conda/lib/python3.7/site-packages/tutel/impls/jit_compiler.py", line 26, in func
    tutel_custom_kernel.invoke(inputs, __ctx__)
RuntimeError: (true) == (fp != nullptr)INTERNAL ASSERT FAILED at "/tmp/pip-req-build-qjgbz25n/tutel/custom/custom_kernel.cpp":39, please report a bug to PyTorch. CHECK_EQ fails.

To reproduce the problem, please try the following code, thanks.

from torch_ort import ORTModule
from onnxruntime.training import ortmodule
ortmodule.ONNX_OPSET_VERSION=12
from onnxruntime.training.ortmodule._custom_autograd_function import enable_custom_autograd_support
enable_custom_autograd_support()

from tutel.jit_kernels.gating import fast_cumsum_sub_one

import torch

class CumsumModule(torch.nn.Module):
    def __init__(self):
        super(CumsumModule, self).__init__()
        self.param = torch.nn.Parameter(torch.ones(5, 5))
    def forward(self, x):
        x = x + self.param
        x_cumsum = fast_cumsum_sub_one(x, dim=0)
        return x_cumsum


input = torch.randint(0, 5, (5, 5), device='cuda:0')
cumsum_module = CumsumModule().to(device='cuda:0')
cumsum_module = ORTModule(cumsum_module)
output = cumsum_module(input)

opened by foreveronehundred 7

Is simple_all_reduce also required for capacity_factor > 0 cases?

My code seems to hang when unbalanced workloads exist in two different GPUs(i.e. scores.size(0) is unequal in different GPUs such as, at the end of a dataset). It further leads to inequality in the capacity of Line 178 in different GPUs. Is simple_all_reduce also required for capacity_factor > 0 cases?

https://github.com/microsoft/tutel/blob/ceba363909a673203a356a71f0b1a6a9113a6845/tutel/impls/fast_dispatch.py#L177-L183
bug

opened by Fragile-azalea 6

Error met when using multi nodes

Dear contributors, I meet an error with tutel's moe layer. The error occurred when I run the tutel/examples/helloworld_ddp.py in the torch distributed mode with more than one GPU node (i.e.: 16 GPUs on 2 machines). However, It is fine when I run this script with 8 GPUs or less.

The error log is following:


[Benchmark] world_size = 16, dtype = float32, model_dim = 2048, hidden_size = 2048, samples = 65536, num_local_experts = 2, topK = 1, device = `cuda:0`
Traceback (most recent call last):
  File "tutel/examples/helloworld_ddp.py", line 154, in <module>
    output = model(x)
  File "/mnt/cache/zhujinguo/anaconda3/envs/xmodaler/lib/python3.7/site-packages/torch/nn/modules/module.py", line 889, in _call_impl
    result = self.forward(*input, **kwargs)
  File "/mnt/cache/zhujinguo/anaconda3/envs/xmodaler/lib/python3.7/site-packages/torch/nn/parallel/distributed.py", line 705, in forward
    output = self.module(*inputs[0], **kwargs[0])
  File "/mnt/cache/zhujinguo/anaconda3/envs/xmodaler/lib/python3.7/site-packages/torch/nn/modules/module.py", line 889, in _call_impl
    result = self.forward(*input, **kwargs)
  File "tutel/examples/helloworld_ddp.py", line 119, in forward
    result = self._moe_layer(input)
  File "/mnt/cache/zhujinguo/anaconda3/envs/xmodaler/lib/python3.7/site-packages/torch/nn/modules/module.py", line 889, in _call_impl
    result = self.forward(*input, **kwargs)
  File "/mnt/lustre/zhujinguo/codes/tutel/tutel/impls/moe_layer.py", line 387, in forward
    result_output, l_aux = self.gate.apply_on_expert_fn(reshaped_input, self.expert_fn, self.group)
  File "/mnt/lustre/zhujinguo/codes/tutel/tutel/impls/moe_layer.py", line 103, in apply_on_expert_fn
    locations1 = self.compute_location(masks_se[0])
  File "/mnt/lustre/zhujinguo/codes/tutel/tutel/jit_kernels/gating.py", line 81, in fast_cumsum_sub_one
    return get_cumsum_kernel(data.size(0), data.size(1))(data)
  File "/mnt/lustre/zhujinguo/codes/tutel/tutel/jit_kernels/gating.py", line 72, in optimized_cumsum
    base_kernel(mask1.to(torch.int32).contiguous(), locations1)
  File "/mnt/lustre/zhujinguo/codes/tutel/tutel/impls/jit_compiler.py", line 39, in func
    tutel_custom_kernel.invoke_with_source(inputs, __ctx__, no_nvrtc, source)
RuntimeError: (0) == (cuModuleGetFunction(&gm.hFunc, gm.hMod, std::string(pos, tail - pos).c_str())) INTERNAL ASSERT FAILED at "/mnt/lustre/zhujinguo/codes/tutel/tutel/custom/custom_kernel.cpp":208, please report a bug to PyTorch. CHECK_EQ fails.

Also, I use tutel moe layer in another project, where the same thing happened.

opened by Lechatelia 5

What is the purpose of the "use_2dh" option?

Hi Tutel authors, thank you for this great framework.

I have a question about commit 901a65cb54c386c8f75bd15e87cf221ff2463d99. What is the purpose of the use_2dh option? And what problem does PrimAllToAll2D intend to solve? It would be great if you can provide more context. Thanks.
question

opened by ymjiang 4
How the experts' gradients are handled under data parallelism?

When count_per_node is set to negative, one expert should be paralleled on multiple GPUs like ZERO, with each GPU holding a slice of the expert's parameters. There are also all_gathers performed within the ffn_zero_group in the forward of the expert.

My question is how these gradients and parameter update of the expert is handled in Tutel under DP. Examples seem to indicate that there are no extra efforts for users to manually handle it. However, I cannot find the corresponding implementation in either moe_layer or TutelDistributedOptimizer.

Any help will be appreciated!

opened by yzs981130 0

RuntimeError: No such operator tutel_ops::cumsum

Hello, thanks for providing such a great work. However, I cannot use tutel successfully. I have followed the library installation steps:

* Install Pytorch for NVIDIA CUDA >= 11.3:
        $ python3 -m pip install --user torch==1.10.0+cu113 torchvision==0.11.1+cu113 -f https://download.pytorch.org/whl/torch_stable.html
       

* Install Tutel Online:

        $ python3 -m pip uninstall tutel -y
        $ python3 -m pip install --user --upgrade git+https://github.com/microsoft/[email protected]
        $ python3 ./tutel/setup.py install --user

But when I try the followed test:

* Quick Test on Single-GPU:

        $ python3 -m tutel.examples.helloworld --batch_size=16               # Test Tutel-optimized MoE + manual distribution

The followed error is reported：

Traceback (most recent call last):
  File "/root/miniconda3/envs/widenet/lib/python3.7/runpy.py", line 193, in _run_module_as_main
    "__main__", mod_spec)
  File "/root/miniconda3/envs/widenet/lib/python3.7/runpy.py", line 85, in _run_code
    exec(code, run_globals)
  File "/root/tutel-main/tutel/examples/helloworld.py", line 120, in <module>
    output = model(x)
  File "/root/.local/lib/python3.7/site-packages/torch/nn/modules/module.py", line 1102, in _call_impl
    return forward_call(*input, **kwargs)
  File "/root/tutel-main/tutel/examples/helloworld.py", line 85, in forward
    result = self._moe_layer(input)
  File "/root/.local/lib/python3.7/site-packages/torch/nn/modules/module.py", line 1102, in _call_impl
    return forward_call(*input, **kwargs)
  File "/root/tutel-main/tutel/impls/moe_layer.py", line 267, in forward
    logits_dtype, (crit, l_aux) = routing()
  File "/root/tutel-main/tutel/impls/moe_layer.py", line 261, in routing
    inequivalent_tokens = inequivalent_tokens,
  File "/root/tutel-main/tutel/impls/fast_dispatch.py", line 158, in extract_critical
    locations1 = compute_location(masks_se[0])
  File "/root/tutel-main/tutel/jit_kernels/gating.py", line 22, in fast_cumsum_sub_one
    return torch.ops.tutel_ops.cumsum(data)
  File "/root/.local/lib/python3.7/site-packages/torch/_ops.py", line 63, in __getattr__
    op = torch._C._jit_get_operation(qualified_op_name)
RuntimeError: No such operator tutel_ops::cumsum

opened by sharkdrop 4

[installation errors] fatal error: nccl.h: No such file or directory

Hello, thanks for providing such a great work. However, I cannot install tutel successfully during the compilation. I have exported the lib path of nccl_2.7.8-1-cuda10.1/include/nccl.h into LD_LIBRARY_PATH. But the error logs seem that it still cannot find the NCCL path. Do you have any idea on how to solve this error? Thanks!

running install_lib running build_py running build_ext building 'tutel_custom_kernel' extension Emitting ninja build file /mnt/lustre/chengguangliang/zhouqianyu/segdgformer/cvpr_2023/tutel-main/build/temp.linux-x86_64-3.7/build.ninja... Compiling objects... Allowing ninja to set a default number of workers... (overridable by setting the environment variable MAX_JOBS=N) [1/1] /mnt/cache/share/gcc/gcc-7.3.0/bin/g++ -MMD -MF /mnt/lustre/chengguangliang/zhouqianyu/tutel-main/build/temp.linux-x86_64-3.7/ tutel/custom/custom_kernel.o.d -Wsign-compare -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC -I/mnt/lustre/chengguangliang/miniconda3/lib/python3 .7/site-packages/torch/include -I/mnt/lustre/chengguangliang/miniconda3/lib/python3.7/site-packages/torch/include/torch/csrc/api/include -I/mnt/lustre/che ngguangliang/miniconda3/lib/python3.7/site-packages/torch/include/TH -I/mnt/lustre/chengguangliang/miniconda3/lib/python3.7/site-packages/torch/include/TH C -I/mnt/lustre/share/cuda-10.1/include -I/mnt/lustre/chengguangliang/miniconda3/include/python3.7m -c -c /mnt/lustre/chengguangliang/zhouqianyu/tutel-main/tutel/custom/custom_kernel.cpp -o /mnt/lustre/chengguangliang/zhouqianyu/tutel-main/build/temp.linux-x86_64- 3.7/tutel/custom/custom_kernel.o -Wno-sign-compare -Wno-unused-but-set-variable -Wno-terminate -Wno-unused-function -Wno-strict-aliasing -DUSE_GPU -DUSE_N CCL -DTORCH_API_INCLUDE_EXTENSION_H '-DPYBIND11_COMPILER_TYPE="_gcc"' '-DPYBIND11_STDLIB="_libstdcpp"' '-DPYBIND11_BUILD_ABI="_cxxabi1011"' -DTORCH_EXTENS ION_NAME=tutel_custom_kernel -D_GLIBCXX_USE_CXX11_ABI=0 -std=c++14 FAILED: /mnt/lustre/chengguangliang/zhouqianyu/tutel-main/build/temp.linux-x86_64-3.7/tutel/custom/custom_kernel.o /mnt/cache/share/gcc/gcc-7.3.0/bin/g++ -MMD -MF /mnt/lustre/chengguangliang/zhouqianyu/tutel-main/build/temp.linux-x86_64-3.7/tutel/ custom/custom_kernel.o.d -Wsign-compare -DNDEBUG -g -fwrapv -O3 -Wall -Wstrict-prototypes -fPIC -I/mnt/lustre/chengguangliang/miniconda3/lib/python3.7/sit e-packages/torch/include -I/mnt/lustre/chengguangliang/miniconda3/lib/python3.7/site-packages/torch/include/torch/csrc/api/include -I/mnt/lustre/chengguan gliang/miniconda3/lib/python3.7/site-packages/torch/include/TH -I/mnt/lustre/chengguangliang/miniconda3/lib/python3.7/site-packages/torch/include/THC -I/m nt/lustre/share/cuda-10.1/include -I/mnt/lustre/chengguangliang/miniconda3/include/python3.7m -c -c /mnt/lustre/chengguangliang/zhouqianyu/tutel-main/tutel/custom/custom_kernel.cpp -o /mnt/lustre/chengguangliang/zhouqianyu/tutel-main/build/temp.linux-x86_64-3.7/tu tel/custom/custom_kernel.o -Wno-sign-compare -Wno-unused-but-set-variable -Wno-terminate -Wno-unused-function -Wno-strict-aliasing -DUSE_GPU -DUSE_NCCL -D TORCH_API_INCLUDE_EXTENSION_H '-DPYBIND11_COMPILER_TYPE="_gcc"' '-DPYBIND11_STDLIB="_libstdcpp"' '-DPYBIND11_BUILD_ABI="_cxxabi1011"' -DTORCH_EXTENSION_NA ME=tutel_custom_kernel -D_GLIBCXX_USE_CXX11_ABI=0 -std=c++14 cc1plus: warning: command line option ‘-Wstrict-prototypes’ is valid for C/ObjC but not for C++ /mnt/lustre/chengguangliang/zhouqianyu/tutel-main/tutel/custom/custom_kernel.cpp:19:10: fatal error: nccl.h: No such file or directory #include <nccl.h> ^~~~~~~~ compilation terminated. ninja: build stopped: subcommand failed. Try installing without NCCL extension..

My machine details are as follows:

nvcc: NVIDIA (R) Cuda compiler driver Copyright (c) 2005-2019 NVIDIA Corporation Built on Wed_Apr_24_19:10:27_PDT_2019 Cuda compilation tools, release 10.1, V10.1.168

I used pytorch1.8.1 with cuda 10.1. I wonder whether tutal can be installed with cuda10.1?

I used the following commands for the installation:

export PATH=/mnt/lustre/share/gcc/gcc-5.4/bin/:$PATH export LD_LIBRARY_PATH=/mnt/lustre/share/polaris/dep/nccl_2.7.8-1-cuda10.1/include/nccl.h:$LD_LIBRARY_PATH export LD_LIBRARY_PATH=/mnt/lustre/share/gcc/gmp-4.3.2/lib:/mnt/lustre/share/gcc/mpfr-2.4.2/lib:/mnt/lustre/share/gcc/mpc-0.8.1/lib:$LD_LIBRARY_PATH export TORCH_CUDA_ARCH_LIST='3.5;5.0+PTX;6.0;7.0'

python setup.py install --user

opened by qianyuzqy 1
Multi-nodes training is much more slower than single node

hi, when I train models using tutel, I find that, in each step, multi-nodes training will need much more step time (if n nodes, it will take around n times of training time of 1-node) than single node training. Thus multi-node training will take even more time than single-node training to finish one epoch. Any debugging suggestions with this issue? Thanks!!!

opened by YingqingHe 1

NCCL Asynchronous update timeout crash with Tutel MoE

Hi, I am using Tutel library with MMAction framework to replicate Swin-v2 MoE performance described in the paper. However, I am facing this error when I try to train MoE in DDP setting. Can someone please help me in resolving this error? Alternatively, can you release the object detection code that was used in the Tutel paper.

E ProcessGroupNCCL.cpp:587] [Rank 1] Watchdog caught collective operation timeout: WorkNCCL(OpType=ALLTOALL_BASE, Timeout(ms)=300000) ran for 306666 milliseconds before timing out.

[E ProcessGroupNCCL.cpp:341] Some NCCL operations have failed or timed out. Due to the asynchronous nature of CUDA kernels, subsequent GPU operations might run on corrupted/incomplete data. To avoid this inconsistency, we are taking the entire process down.

terminate called after throwing an instance of 'std::runtime_error'

  what():  [Rank 1] Watchdog caught collective operation timeout: WorkNCCL(OpType=ALLTOALL_BASE, Timeout(ms)=300000) ran for 306666 milliseconds before timing out.

WARNING:torch.distributed.elastic.multiprocessing.api:Sending process 6056 closing signal SIGTERM

opened by jinga-lala 5

My code seems to hang when skip_remainder_batch=False.

Describe the bug Hi, Authors. My code seems to hang when skip_remainder_batch=False.

To Reproduce Steps to reproduce the behavior:

git clone https://github.com/microsoft/tutel --branch main
python3 -m pip uninstall tutel -y
python3 ./tutel/setup.py

cd ./tutel/tutel/examples/fairseq_moe
git clone https://github.com/facebookresearch/fairseq --branch main
cd fairseq/ && git checkout b5e7b250913120409b872a940fbafec4d43c7b13
# This patch is an example to train Fairseq MoE transformers.
# Note that the current patch only works for `legacy_ddp` backend, and `--checkpoint-activations` must be disabled.
git apply ../fairseq_patch.diff
python3 -m pip install omegaconf==2.0.5 hydra-core==1.0.7
python3 -m pip install --no-deps --editable .

#fix bug in https://github.com/facebookresearch/fairseq/blob/main/fairseq/tasks/translation.py#L441-L442
#get dataset followed by https://github.com/facebookresearch/fairseq/tree/main/examples/translation


 CUDA_VISIBLE_DEVICES=0,1  MOE=3 L_AUX_WT=0.01 SKIP_EXPERT=1 fairseq-train  fairseq/data-bin/iwslt14.tokenized.de-en     --arch transformer_iwslt_de_en --share-decoder-input-output-embed     --optimizer adam --adam-betas '(0.9, 0.98)' --clip-norm 0.0     --lr 10e-4 --lr-scheduler inverse_sqrt --warmup-updates 4000     --dropout 0.3 --weight-decay 0.0001     --criterion label_smoothed_cross_entropy --label-smoothing 0.1     --max-tokens 4096 --eval-bleu     --eval-bleu-args '{"beam": 5, "max_len_a": 1.2, "max_len_b": 10}'     --eval-bleu-detok moses     --eval-bleu-remove-bpe  --eval-bleu-print-samples     --best-checkpoint-metric bleu --maximize-best-checkpoint-metric  --ddp-backend legacy_ddp --max-update 100000

Logs

2022-08-09 10:51:01 | INFO | fairseq.utils | rank   0: capabilities =  7.5  ; total memory = 10.761 GB ;[0/1773]
NVIDIA GeForce RTX 2080 Ti
2022-08-09 10:51:01 | INFO | fairseq.utils | rank   1: capabilities =  7.5  ; total memory = 10.761 GB ; name =
NVIDIA GeForce RTX 2080 Ti
2022-08-09 10:51:01 | INFO | fairseq.utils | ***********************CUDA enviroments for all 2 workers**********
*************
2022-08-09 10:51:01 | INFO | fairseq_cli.train | training on 2 devices (GPUs/TPUs)
2022-08-09 10:51:01 | INFO | fairseq_cli.train | max tokens per device = 4096 and max sentences per device = Non
e
2022-08-09 10:51:01 | INFO | fairseq.trainer | Preparing to load checkpoint checkpoints/checkpoint_last.pt
2022-08-09 10:51:01 | INFO | fairseq.trainer | No existing checkpoint found checkpoints/checkpoint_last.pt
2022-08-09 10:51:01 | INFO | fairseq.trainer | loading train data for epoch 1
2022-08-09 10:51:01 | INFO | fairseq.data.data_utils | loaded 160,239 examples from: fairseq/data-bin/iwslt14.to
kenized.de-en/train.de-en.de
2022-08-09 10:51:01 | INFO | fairseq.data.data_utils | loaded 160,239 examples from: fairseq/data-bin/iwslt14.to
kenized.de-en/train.de-en.en
2022-08-09 10:51:01 | INFO | fairseq.tasks.translation | fairseq/data-bin/iwslt14.tokenized.de-en train de-en 16
0239 examples
2022-08-09 10:51:01 | INFO | fairseq.trainer | NOTE: your device may support faster training with --fp16 or --am
p
2022-08-09 10:51:01 | INFO | fairseq.data.iterators | grouped total_num_itrs = 551
epoch 001:   0%|                                                          | 0/551 [00:00<?, ?it/s]2022-08-09 10:
51:01 | INFO | fairseq.trainer | begin training epoch 1
2022-08-09 10:51:01 | INFO | fairseq_cli.train | Start iterating over samples
/home/xinglinpan/tutel/tutel/examples/fairseq_moe/fairseq/fairseq/utils.py:374: UserWarning: amp_C fused kernels
 unavailable, disabling multi_tensor_l2norm; you may get better performance by installing NVIDIA's apex library
  warnings.warn(
/home/xinglinpan/tutel/tutel/examples/fairseq_moe/fairseq/fairseq/utils.py:374: UserWarning: amp_C fused kernels
 unavailable, disabling multi_tensor_l2norm; you may get better performance by installing NVIDIA's apex library
  warnings.warn(
epoch 001: 100%|▉| 550/551 [02:12<00:00,  4.54it/s, loss=9.244, nll_loss=8.59, ppl=385.3, wps=31462022-08-09 10:
53:14 | INFO | fairseq_cli.train | begin validation on "valid" subset
                                                                                                 2022-08-09 10:5
3:19 | INFO | fairseq.tasks.translation | example hypothesis: they don't don't don't don't don't don't don't't't
't
2022-08-09 10:53:19 | INFO | fairseq.tasks.translation | example reference: they're just not moving.

The possible problem is that not all devices are provided with data in the last iteration on the valid, so alltoall is always pending other processes. If SKIP_MOE=1, there is no this phenomenon.

application patch

opened by Fragile-azalea 7

Releases(v0.2.0)

v0.2.0(Aug 11, 2022)
What's New in v0.2.0:

Support Windows Python3 + Torch Installation;

Add examples to enable Tutel MoE in Fairseq;

Refactor MoE Layer implementation, letting all features (e.g. top-X, overlap, parallel_type, capacity, ..) be able to change at different forward interations;

New features: load_importance_loss, cosine router, inequivalent_tokens;

Extend capacity_factor value that includes zero value and negative values for smarter capacity estimation;

Add tutel.checkpoint conversion tools to reformat checkpoint files, making it able to use existing checkpoints to train/infer with a different world size.

How to Setup: python3 -m pip install --user https://github.com/microsoft/tutel/archive/refs/tags/v0.2.0.tar.gz
Source code(tar.gz)
Source code(zip)
v0.1.5(Feb 26, 2022)
What's New in v0.1.5:

Add 2D hierarchical a2a algorithm used for extremely-large scaling;

Support different parallel_type for MoE computation: data, model, auto;

Combine different expert granularities (e.g. normal, sharded experts, megatron dense ffn) into same programming interface & style;

New features: is_postscore to specify whether gating scores are weighed during encoding or decoding;

Enhance existing features: JIT compiler, a2a overlap with 2D.

How to Setup: python3 -m pip install --user https://github.com/microsoft/tutel/archive/refs/tags/v0.1.5.tar.gz

Contributors: @abuccts, @yzygitzh, @ghostplant, @EricWangCN
Source code(tar.gz)
Source code(zip)
v0.1.4(Feb 9, 2022)
What's New in v0.1.4:

Enhance communication features: a2a overlap with computation, support different granularity of group creation, etc.

Add single-thread CPU implementation for correctness check & reference;

Refine JIT compiler interface for flexible usability: jit::inject_source && jit::jit_execute;

Enhance examples: fp64 support, cuda amp, checkpointing, etc.

Support execution inside torch.distributed.pipeline.

How to Setup: python3 -m pip install --user https://github.com/microsoft/tutel/archive/refs/tags/v0.1.4.tar.gz

Contributors: @yzygitzh, @ghostplant, @EricWangCN
Source code(tar.gz)
Source code(zip)
v0.1.3(Dec 29, 2021)
What's New in v0.1.3:

Add Tutel Launcher Support based on Open MPI;

Support Establishing Data Model Parallel in Initialization;

Support Single Expert Evenly Sharded on Multiple GPUs;

Support List of Gates and Forwarding MoE Layer with Specified Gating Index;

Fix NVRTC Compatibility when Enabling USE_NVRTC=1;

Other Implementation Enhancements & Correctness Checking;

How to Setup: python3 -m pip install --user https://github.com/microsoft/tutel/archive/refs/tags/v0.1.3.tar.gz

Contributors: @ghostplant, @EricWangCN, @guoshzhao.
Source code(tar.gz)
Source code(zip)
v0.1.2(Nov 16, 2021)
What's New in v0.1.2:

General-purpose top-k gating with {'type': 'top', 'k': 2};

Add Megatron-ML Tensor Parallel as gating type;

Add deepspeed-based & megatron-based helloworld example for fair comparison;

Add torch.bfloat16 datatype support for single-GPU;

How to Setup: python3 -m pip install --user https://github.com/microsoft/tutel/archive/refs/tags/v0.1.2.tar.gz

Contributors: @ghostplant, @EricWangCN, @foreveronehundred.
Source code(tar.gz)
Source code(zip)
v0.1.1(Oct 10, 2021)
What's New in v0.1.1:

Enable fp16 support for AMDGPU.

Using NVRTC for JIT compilation if available.

Add new system_init interface for initializing NUMA settings in distributed GPUs.

Extend more gating types: Top3Gate & Top4Gate.

Allow high level to change capacity value in Tutel fast dispatcher.

Add custom AllToAll extension for old Pytorch version without builtin AllToAll operator support.

How to Setup: python3 -m pip install --user https://github.com/microsoft/tutel/archive/refs/tags/v0.1.1.tar.gz

Contributors: @jspark1105 , @ngoyal2707 , @guoshzhao, @ghostplant .
Source code(tar.gz)
Source code(zip)
v0.1.0(Sep 14, 2021)
The first version of Tutel for efficient MoE implementation.

How to setup: python3 -m pip install --user https://github.com/microsoft/tutel/archive/refs/tags/v0.1.0.tar.gz
Source code(tar.gz)
Source code(zip)

Tutel MoE: An Optimized Mixture-of-Experts Implementation

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Overview

Project Tutel

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Releases(v0.2.0)

v0.2.0(Aug 11, 2022)

v0.1.5(Feb 26, 2022)

v0.1.4(Feb 9, 2022)

v0.1.3(Dec 29, 2021)

v0.1.2(Nov 16, 2021)

v0.1.1(Oct 10, 2021)

v0.1.0(Sep 14, 2021)

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Code for the Image similarity challenge.

Markov Attention Models

Official implementation for "QS-Attn: Query-Selected Attention for Contrastive Learning in I2I Translation" (CVPR 2022)

PyTorch implementation of ENet

Pytorch implementation of MLP-Mixer with loading pre-trained models.

Learning Efficient Online 3D Bin Packing on Packing Configuration Trees

A code repository associated with the paper A Benchmark for Rough Sketch Cleanup by Chuan Yan, David Vanderhaeghe, and Yotam Gingold from SIGGRAPH Asia 2020.

Medical-Image-Triage-and-Classification-System-Based-on-COVID-19-CT-and-X-ray-Scan-Dataset

A "gym" style toolkit for building lightweight Neural Architecture Search systems