Merge pull request #80 from ROCmSoftwarePlatform/IFU-master-2022-07-29

IFU-master-2022-07-29

.github/ISSUE_TEMPLATE/bug_report.md

+---
+name: Bug report
+about: Create a report to help us improve apex
+title: ''
+labels: bug
+assignees: ''
+
+---
+
+**Describe the Bug**
+
+**Minimal Steps/Code to Reproduce the Bug**
+<!--
+Please list the *minimal* steps or provide a code snippet for us to be able to reproduce the bug.
+
+A helpful guide on on how to craft a minimal bug report http://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports.
+--> 
+
+**Expected Behavior**
+<!-- A clear and concise description of what you expected to happen. -->
+
+**Environment**
+<!-- OS, version of Python, CUDA, PyTorch; collect these via `python -m torch.utils.collect_env` -->

README.md

 # Introduction
 
-This repository holds NVIDIA-maintained utilities to streamline
-mixed precision and distributed training in Pytorch.
+This repository holds NVIDIA-maintained utilities to streamline mixed precision and distributed training in Pytorch.
 Some of the code here will be included in upstream Pytorch eventually.
-The intention of Apex is to make up-to-date utilities available to
-users as quickly as possible.
+The intent of Apex is to make up-to-date utilities available to users as quickly as possible.
 
 ## Full API Documentation: [https://nvidia.github.io/apex](https://nvidia.github.io/apex)
 
@@ -98,30 +96,22 @@ amp.load_state_dict(checkpoint['amp'])
 
 Note that we recommend restoring the model using the same `opt_level`. Also note that we recommend calling the `load_state_dict` methods after `amp.initialize`.
 
-# Requirements
+# Installation
 
-Python 3
+## Containers
+NVIDIA PyTorch Containers are available on NGC: https://catalog.ngc.nvidia.com/orgs/nvidia/containers/pytorch.
+The containers come with all the custom extensions available at the moment. 
 
-CUDA 9 or newer
+See [the NGC documentation](https://docs.nvidia.com/deeplearning/frameworks/pytorch-release-notes/index.html) for details such as:
+- how to pull a container
+- how to run a pulled container
+- release notes
 
-PyTorch 0.4 or newer.  The CUDA and C++ extensions require pytorch 1.0 or newer.
+## From Source
 
-We recommend the latest stable release, obtainable from
-[https://pytorch.org/](https://pytorch.org/).  We also test against the latest master branch, obtainable from [https://github.com/pytorch/pytorch](https://github.com/pytorch/pytorch).
+To install Apex from source, we recommend using the nightly Pytorch obtainable from https://github.com/pytorch/pytorch.
 
-It's often convenient to use Apex in Docker containers.  Compatible options include:
-* [NVIDIA Pytorch containers from NGC](https://ngc.nvidia.com/catalog/containers/nvidia%2Fpytorch), which come with Apex preinstalled.  To use the latest Amp API, you may need to `pip uninstall apex` then reinstall Apex using the **Quick Start** commands below.
-* [official Pytorch -devel Dockerfiles](https://hub.docker.com/r/pytorch/pytorch/tags), e.g. `docker pull pytorch/pytorch:nightly-devel-cuda10.0-cudnn7`, in which you can install Apex using the **Quick Start** commands.
-
-See the [Docker example folder](https://github.com/NVIDIA/apex/tree/master/examples/docker) for details.
-
-## On ROCm:
-* Python 3.6
-* Pytorch 1.5 or newer, The HIPExtensions require 1.5 or newer.
-* We recommend follow the instructions from [ROCm-Pytorch](https://github.com/ROCmSoftwarePlatform/pytorch) to install pytorch on ROCm.
-* Note: For pytorch versions < 1.8, building from source is no longer supported, please use the release package [ROCm-Apex v0.3](https://github.com/ROCmSoftwarePlatform/apex/releases/tag/v0.3) . 
-
-# Quick Start
+The latest stable release obtainable from https://pytorch.org should also work.
 
 ### Rocm
 Apex on ROCm supports both python only build and extension build.
@@ -145,29 +135,27 @@ pip install -v --install-option="--cpp_ext" --install-option="--cuda_ext" 'git+h
 ```
 
 ### Linux
-
 For performance and full functionality, we recommend installing Apex with
 CUDA and C++ extensions via
-```
+```bash
 git clone https://github.com/NVIDIA/apex
 cd apex
 pip install -v --disable-pip-version-check --no-cache-dir --global-option="--cpp_ext" --global-option="--cuda_ext" ./
 ```
 
-Apex also supports a Python-only build (required with Pytorch 0.4) via
-```
+Apex also supports a Python-only build via
+```bash
 pip install -v --disable-pip-version-check --no-cache-dir ./
 ```
 A Python-only build omits:
 - Fused kernels required to use `apex.optimizers.FusedAdam`.
-- Fused kernels required to use `apex.normalization.FusedLayerNorm`.
+- Fused kernels required to use `apex.normalization.FusedLayerNorm` and `apex.normalization.FusedRMSNorm`.
 - Fused kernels that improve the performance and numerical stability of `apex.parallel.SyncBatchNorm`.
 - Fused kernels that improve the performance of `apex.parallel.DistributedDataParallel` and `apex.amp`.
 `DistributedDataParallel`, `amp`, and `SyncBatchNorm` will still be usable, but they may be slower.
 
-Pyprof support has been moved to its own [dedicated repository](https://github.com/NVIDIA/PyProf).
-The codebase is deprecated in Apex and will be removed soon.
 
-### Windows support
-Windows support is experimental, and Linux is recommended.  `pip install -v --no-cache-dir --global-option="--cpp_ext" --global-option="--cuda_ext" .` may work if you were able to build Pytorch from source
-on your system.  `pip install -v --no-cache-dir .` (without CUDA/C++ extensions) is more likely to work.  If you installed Pytorch in a Conda environment, make sure to install Apex in that same environment.
+### [Experimental] Windows
+`pip install -v --no-cache-dir --global-option="--cpp_ext" --global-option="--cuda_ext" .` may work if you were able to build Pytorch from source
+on your system. A Python-only build via `pip install -v --no-cache-dir .` is more likely to work.  
+If you installed Pytorch in a Conda environment, make sure to install Apex in that same environment.

apex/__init__.py

 import logging
+import warnings
 
 # May help avoid undefined symbol errors https://pytorch.org/cppdocs/notes/faq.html#undefined-symbol-errors-from-pytorch-aten
 import torch
@@ -19,14 +20,10 @@ from . import fp16_utils
 # load time) the error message is timely and visible.
 from . import optimizers
 from . import normalization
-from . import pyprof
-
-#common utilties to run tests on ROCm.
-from . import testing
 from . import transformer
 
 
-# Logging utilities mainly for apex.transformer module
+# Logging utilities for apex.transformer module
 class RankInfoFormatter(logging.Formatter):
 
     def format(self, record):
@@ -37,6 +34,18 @@ class RankInfoFormatter(logging.Formatter):
 
 _library_root_logger = logging.getLogger(__name__)
 handler = logging.StreamHandler()
-handler.setFormatter(RankInfoFormatter("%(asctime)s - %(name)s - %(levelname)s - %(rank_info)s - %(message)s"))
+handler.setFormatter(RankInfoFormatter("%(asctime)s - PID:%(process)d - rank:%(rank_info)s - %(filename)s:%(lineno)d - %(levelname)s - %(message)s", "%y-%m-%d %H:%M:%S"))
 _library_root_logger.addHandler(handler)
 _library_root_logger.propagate = False
+
+
+def check_cudnn_version_and_warn(global_option: str, required_cudnn_version: int) -> bool:
+    cudnn_available = torch.backends.cudnn.is_available()
+    cudnn_version = torch.backends.cudnn.version() if cudnn_available else None
+    if not (cudnn_available and (cudnn_version >= required_cudnn_version)):
+        warnings.warn(
+            f"`{global_option}` depends on cuDNN {required_cudnn_version} or later, "
+            f"but {'cuDNN is not available' if not cudnn_available else cudnn_version}"
+        )
+        return False
+    return True

apex/_autocast_utils.py

-from typing import Optional
+from typing import Optional, Sequence
 
 import torch
 
 
+def _get_autocast_dtypes() -> Sequence[torch.dtype]:
+    if torch.cuda.is_bf16_supported():
+        return [torch.half, torch.bfloat16]
+    return [torch.half]
+
+
 def _get_current_dtype(dtype: Optional[torch.dtype] = None) -> torch.dtype:
     if not torch.is_autocast_enabled():
         return torch.float or dtype

apex/contrib/bottleneck/__init__.py

 from .bottleneck import Bottleneck, SpatialBottleneck
+from .halo_exchangers import HaloExchangerNoComm, HaloExchangerAllGather, HaloExchangerSendRecv, HaloExchangerPeer

apex/contrib/bottleneck/halo_exchangers.py

+import torch
+import torch.distributed as dist
+from torch import nn
+import nccl_p2p_cuda as inc
+import peer_memory_cuda as pm
+
+# Communication free halo exchanger.
+# NB! This halo exchanger does not exchange halos with neighbors as it should, it merely swaps the inputs
+# NB! This is only useful for performance testing.
+# NB! Do not use for actual production runs
+class HaloExchanger(object):
+    def __init__(self, ranks, rank_in_group):
+        self.stream1 = torch.cuda.Stream()
+        self.stream2 = torch.cuda.Stream()
+        self.stream3 = torch.cuda.Stream()
+        self.group_size = len(ranks)
+        self.ranks = ranks
+        self.rank_in_group = rank_in_group
+        self.wrap_around_left_rank_in_group = (rank_in_group + self.group_size - 1) % self.group_size
+        self.wrap_around_right_rank_in_group = (rank_in_group + 1) % self.group_size
+        self.left_rank = ranks[rank_in_group-1] if rank_in_group > 0 else -1
+        self.left_zero = True if rank_in_group == 0 else False
+        self.right_rank = ranks[rank_in_group+1] if rank_in_group < self.group_size - 1 else -1
+        self.right_zero = True if rank_in_group == self.group_size - 1 else False
+
+class HaloExchangerNoComm(HaloExchanger):
+    def __init__(self, ranks, rank_in_group):
+        super(HaloExchangerNoComm, self).__init__(ranks, rank_in_group)
+
+    def left_right_halo_exchange(self, left_output_halo, right_output_halo, left_input_halo=None, right_input_halo=None):
+        if left_input_halo is None:
+            return right_output_halo, left_output_halo
+        else:
+            left_input_halo.copy_(right_output_halo)
+            right_input_halo.copy_(left_output_halo)
+
+class HaloExchangerAllGather(HaloExchanger):
+    def __init__(self, ranks, rank_in_group, comm):
+        super(HaloExchangerAllGather, self).__init__(ranks, rank_in_group)
+        # self.comm must be NCCL process_group created with torch.distributed.new_group(ranks=ranks)
+        self.comm = comm
+
+    def left_right_halo_exchange(self, left_output_halo, right_output_halo, left_input_halo=None, right_input_halo=None):
+        N,Hh,W,C = list(left_output_halo.shape)
+        send_halos = torch.empty((N,2*Hh,W,C),dtype=left_output_halo.dtype,device=left_output_halo.device)
+        send_halos[:,:Hh,:,:].copy_(left_output_halo)
+        send_halos[:,Hh:,:,:].copy_(right_output_halo)
+        all_halos = torch.empty((N,2*Hh*self.group_size,W,C),dtype=left_output_halo.dtype,device=left_output_halo.device)
+        all_halos = [all_halos[:,i*2*Hh:(i+1)*2*Hh,:,:] for i in range(self.group_size)]
+        torch.distributed.all_gather(all_halos,send_halos,group=self.comm,no_copy=True)
+        ag_left_input_halo = all_halos[self.wrap_around_left_rank_in_group][:,Hh:,:,:]
+        ag_right_input_halo = all_halos[self.wrap_around_right_rank_in_group][:,:Hh,:,:]
+        if left_input_halo is None:
+            if self.left_zero:
+                ag_left_input_halo.zero_()
+            if self.right_zero:
+                ag_right_input_halo.zero_()
+            return ag_left_input_halo, ag_right_input_halo
+        else:
+            if self.left_zero:
+                left_input_halo.zero_()
+            else:
+                left_input_halo.copy_(ag_left_input_halo)
+            if self.right_zero:
+                right_input_halo.zero_()
+            else:
+                right_input_halo.copy_(ag_right_input_halo)
+
+class HaloExchangerSendRecv(HaloExchanger):
+    def __init__(self, ranks, rank_in_group):
+        super(HaloExchangerSendRecv, self).__init__(ranks, rank_in_group)
+        nccl_id = inc.get_unique_nccl_id(1).cuda()
+        torch.distributed.broadcast(nccl_id, 0)
+        nccl_id = nccl_id.cpu()
+        print("%d :: nccl_id = %s" % (torch.distributed.get_rank(), str(nccl_id)))
+        # Create another global nccl communicator in addition to the one created by torch.distributed.init_process_group("nccl")
+        # This is unavoidable because the underlying NCCL communicator torch.distributed creates is a protected variable, hence
+        # it cannot be accessed from another class.
+        # TODO: Figure out a way to avoid creating a second global communicator
+        assert(torch.distributed.get_rank() == self.ranks[self.rank_in_group]), "ranks[%d](%d) != torch.distributed.get_rank()(%d)" % (self.rank_in_group, self.ranks[self.rank_in_group], torch.distributed.get_rank())
+        self.handle = inc.init_nccl_comm(nccl_id, torch.distributed.get_rank(), torch.distributed.get_world_size())
+
+    def left_right_halo_exchange(self, left_output_halo, right_output_halo, left_input_halo=None, right_input_halo=None):
+        if left_input_halo is None:
+            left_input_halo, right_input_halo = inc.left_right_halo_exchange(self.handle, self.left_rank, self.right_rank , left_output_halo, right_output_halo)
+            return left_input_halo, right_input_halo
+        else:
+            inc.left_right_halo_exchange_inplace(self.handle, self.left_rank, self.right_rank, left_output_halo, right_output_halo, left_input_halo, right_input_halo)
+
+class HaloExchangerPeer(HaloExchanger):
+    def __init__(self, ranks, rank_in_group, peer_pool, explicit_nhwc, numSM=1):
+        super(HaloExchangerPeer, self).__init__(ranks, rank_in_group)
+        self.diagnostics = False
+        self.explicit_nhwc = explicit_nhwc
+        self.numSM = numSM
+        self.peer_pool = peer_pool
+        self.signals = peer_pool.allocate_peer_tensors([2,4], torch.int32, False, False)
+        self.signals[self.rank_in_group].zero_()
+
+    def left_right_halo_exchange(self, left_output_halo, right_output_halo, left_input_halo=None, right_input_halo=None):
+        inplace = False if left_input_halo is None and right_input_halo is None else True
+        if not inplace:
+            left_input_halo = torch.empty_like(right_output_halo)
+            right_input_halo = torch.empty_like(left_output_halo)
+        channels_last = left_output_halo.is_contiguous(memory_format=torch.channels_last) and not self.explicit_nhwc
+        left_tx = self.peer_pool.allocate_peer_tensors(list(left_output_halo.shape), left_output_halo.dtype, channels_last, True)
+        right_tx = self.peer_pool.allocate_peer_tensors(list(right_output_halo.shape), right_output_halo.dtype, channels_last, True)
+        pm.push_pull_halos_1d(
+                self.diagnostics, self.explicit_nhwc, self.numSM,
+                left_output_halo,  left_tx[self.rank_in_group],  right_tx[self.wrap_around_left_rank_in_group], left_input_halo,
+                right_output_halo, right_tx[self.rank_in_group], left_tx[self.wrap_around_right_rank_in_group],  right_input_halo,
+                self.signals[self.wrap_around_left_rank_in_group], self.signals[self.wrap_around_right_rank_in_group], self.signals[self.rank_in_group]
+                )
+        # TODO: Add to push_pull_halos_1d kernel
+        if self.left_zero:
+            left_input_halo.zero_()
+        if self.right_zero:
+            right_input_halo.zero_()
+        if not inplace:
+            return left_input_halo, right_input_halo
+
+# Class that combines input volume with halos from neighbors (1d).
+class HaloPadder:
+    def __init__(self, halo_ex):
+        self.halo_ex = halo_ex
+        self.stream1 = torch.cuda.Stream()
+        self.stream2 = torch.cuda.Stream()
+
+    def __call__(self, y, half_halo, explicit_nhwc, H_split):
+        channels_last = not explicit_nhwc and y.is_contiguous(memory_format=torch.channels_last)
+        if explicit_nhwc:
+            N,H,W,C = list(y.shape)
+            if H_split:
+                padded_shape = [N,H+2*half_halo,W,C]
+                ypad = torch.empty(shape=padded_shape, dtype=y.dtype, device=y.device, memory_format=torch.contiguous_format)
+                yleft = ypad[:,:half_halo,:,:]
+                ymid = ypad[:,half_halo:H+half_halo,:,:]
+                yright = ypad[:,H+half_halo:H+2*half_halo,:,:]
+                oleft = y[:,:half_halo,:,:]
+                oright = y[:,H-half_halo:,:,:]
+            else:
+                padded_shape = [N,H,W+2*half_halo,C]
+                ypad = torch.empty(shape=padded_shape, dtype=y.dtype, device=y.device, memory_format=torch.contiguous_format)
+                yleft = ypad[:,:,:half_halo,:]
+                ymid = ypad[:,:,half_halo:W+half_halo,:]
+                yright = ypad[:,:,W+half_halo:W+2*half_halo,:]
+                oleft = y[:,:,:half_halo,:]
+                oright = y[:,:,W-half_halo:,:]
+        else:
+            N,C,H,W = list(y.shape)
+            if H_split:
+                padded_shape = [N,C,H+2*half_halo,W]
+                ypad = torch.empty(shape=padded_shape, dtype=y.dtype, device=y.device, memory_format=torch.channels_last)
+                yleft = ypad[:,:,:half_halo,:]
+                ymid = ypad[:,:,half_halo:H+half_halo,:]
+                yright = ypad[:,:,H+half_halo:H+2*half_halo,:]
+                oleft = y[:,:,:half_halo,:]
+                oright = y[:,:,H-half_halo:,:]
+            else:
+                padded_shape = [N,C,H,W+2*half_halo]
+                ypad = torch.empty(shape=padded_shape, dtype=y.dtype, device=y.device, memory_format=torch.channels_last)
+                yleft = ypad[:,:,:,:half_halo]
+                ymid = ypad[:,:,:,half_halo:W+half_halo]
+                yright = ypad[:,:,:,W+half_halo:W+2*half_halo]
+                oleft = y[:,:,:,:half_halo]
+                oright = y[:,:,:,W-half_halo:]
+        with torch.cuda.stream(self.stream1):
+            self.halo_ex(oleft, oright, yleft, yright)
+        with torch.cuda.stream(self.stream2):
+            ymid.copy_(y)
+        return ypad
+
+    def wait(self):
+        current_stream = torch.cuda.current_stream()
+        current_stream.wait_stream(self.stream1)
+        current_stream.wait_stream(self.stream2)

apex/contrib/clip_grad/__init__.py

+from .clip_grad import clip_grad_norm_

apex/contrib/clip_grad/clip_grad.py

+import torch
+from torch._six import inf
+from typing import Union, Iterable
+
+_kernel_import_succeeded = False
+try:
+    import amp_C
+    from apex.multi_tensor_apply import multi_tensor_applier
+    _kernel_import_succeeded = True
+except:
+    _kernel_import_succeeded = False
+
+_tensor_or_tensors = Union[torch.Tensor, Iterable[torch.Tensor]]
+
+def clip_grad_norm_(
+        parameters: _tensor_or_tensors, max_norm: float, norm_type: float = 2.0,
+        error_if_nonfinite: bool = False) -> torch.Tensor:
+    r"""Clips gradient norm of an iterable of parameters.
+
+    The norm is computed over all gradients together, as if they were
+    concatenated into a single vector. Gradients are modified in-place.
+
+    This is identical to torch.nn.utils.clip_grad_norm_, except it
+    uses a fused CUDA kernel when computing the 2-norm of GPU tensors
+    in float32 and float16.
+
+    Args:
+        parameters (Iterable[Tensor] or Tensor): an iterable of Tensors or a
+            single Tensor that will have gradients normalized
+        max_norm (float or int): max norm of the gradients
+        norm_type (float or int): type of the used p-norm. Can be ``'inf'`` for
+            infinity norm.
+        error_if_nonfinite (bool): if True, an error is thrown if the total
+            norm of the gradients from :attr:`parameters` is ``nan``,
+            ``inf``, or ``-inf``. Default: False (will switch to True in the future)
+
+    Returns:
+        Total norm of the parameters (viewed as a single vector).
+
+    """
+    if isinstance(parameters, torch.Tensor):
+        parameters = [parameters]
+    parameters = [p for p in parameters if p.grad is not None]
+    max_norm = float(max_norm)
+    norm_type = float(norm_type)
+
+    # Trivial case
+    if len(parameters) == 0:
+        return torch.tensor(0.)
+
+    # Fallback implementation
+    if not (_kernel_import_succeeded
+            and norm_type == 2.0
+            and any(p.is_cuda for p in parameters)):
+        return torch.nn.utils.clip_grad_norm_(
+            parameters,
+            max_norm,
+            norm_type=norm_type,
+            error_if_nonfinite = error_if_nonfinite,
+        )
+
+    # Find fp32 and fp16 gradients on GPU
+    device = next(p.device for p in parameters if p.is_cuda)
+    grads_fp32, grads_fp16, grads_misc = [], [], []
+    for p in parameters:
+        grad = p.grad.detach()
+        if p.dtype == torch.float32 and p.device == device:
+            grads_fp32.append(grad)
+        elif p.dtype == torch.float16 and p.device == device:
+            grads_fp16.append(grad)
+        else:
+            grads_misc.append(grad)
+
+    # Compute gradient L2 norms
+    norms = []
+    dummy_overflow_buf = torch.zeros([1], dtype=torch.int32, device=device)
+    if grads_fp32:
+        norms.append(
+            multi_tensor_applier(
+                amp_C.multi_tensor_l2norm,
+                dummy_overflow_buf,
+                [grads_fp32],
+                False,
+            )[0]
+        )
+    if grads_fp16:
+        norms.append(
+            multi_tensor_applier(
+                amp_C.multi_tensor_l2norm,
+                dummy_overflow_buf,
+                [grads_fp16],
+                False,
+            )[0],
+        )
+    for g in grads_misc:
+        norms.append(torch.linalg.norm(g).unsqueeze(0).to(device))
+    total_norm = torch.linalg.norm(torch.cat(norms))
+
+    # Check for non-finite values
+    if error_if_nonfinite and torch.logical_or(total_norm.isnan(), total_norm.isinf()):
+        raise RuntimeError(
+            f'The total norm of order {norm_type} for gradients from '
+            '`parameters` is non-finite, so it cannot be clipped. To disable '
+            'this error and scale the gradients by the non-finite norm anyway, '
+            'set `error_if_nonfinite=False`')
+
+    # Scale gradients
+    clip_coef = max_norm / (total_norm + 1e-6)
+    clip_coef_clamped = torch.clamp(clip_coef, max=1.0)
+    if grads_fp32:
+        multi_tensor_applier(
+            amp_C.multi_tensor_scale,
+            dummy_overflow_buf,
+            [grads_fp32, grads_fp32],
+            clip_coef_clamped,
+        )
+    if grads_fp16:
+        multi_tensor_applier(
+            amp_C.multi_tensor_scale,
+            dummy_overflow_buf,
+            [grads_fp16, grads_fp16],
+            clip_coef_clamped,
+        )
+    for g in grads_misc:
+        g.mul_(clip_coef_clamped.to(g.device))
+
+    return total_norm

apex/contrib/conv_bias_relu/__init__.py

+from .conv_bias_relu import ConvBiasReLU, ConvBias, ConvBiasMaskReLU 
+

apex/contrib/conv_bias_relu/conv_bias_relu.py

+import pdb
+
+import torch
+from torch.autograd import gradcheck
+
+from apex import check_cudnn_version_and_warn
+import fused_conv_bias_relu
+
+check_cudnn_version_and_warn(__name__, 8400)
+
+
+class ConvBiasReLU_(torch.autograd.Function):
+    @staticmethod
+    @torch.cuda.amp.custom_fwd(cast_inputs=torch.half)
+    def forward(ctx, x, weight, bias, padding, stride):
+        outputs = fused_conv_bias_relu.forward([x, weight, bias], padding, stride)
+        ctx.save_for_backward(x, weight, outputs[0])
+        ctx.padding = padding
+        ctx.stride = stride
+
+        return outputs[0]
+
+    @staticmethod
+    @torch.cuda.amp.custom_bwd
+    def backward(ctx, grad_output):
+        bwd_args = [*ctx.saved_tensors, grad_output]
+        padding = ctx.padding
+        stride = ctx.stride
+        grads = fused_conv_bias_relu.backward(bwd_args, padding, stride)
+
+        return grads[0], grads[1], grads[2], None, None
+
+
+class ConvBiasMaskReLU_(torch.autograd.Function):
+    @staticmethod
+    @torch.cuda.amp.custom_fwd(cast_inputs=torch.half)
+    def forward(ctx, x, weight, bias, mask, padding, stride):
+        outputs = fused_conv_bias_relu.forward_mask([x, weight, bias, mask], padding, stride)
+        ctx.save_for_backward(x, weight, outputs[0])
+        ctx.padding = padding
+        ctx.stride = stride
+
+        return outputs[0]
+
+    @staticmethod
+    @torch.cuda.amp.custom_bwd
+    def backward(ctx, grad_output):
+        bwd_args = [*ctx.saved_tensors, grad_output]
+        padding = ctx.padding
+        stride = ctx.stride
+        grads = fused_conv_bias_relu.backward(bwd_args, padding, stride)
+
+        return grads[0], grads[1], grads[2], None, None, None
+
+
+class ConvBias_(torch.autograd.Function):
+    @staticmethod
+    @torch.cuda.amp.custom_fwd(cast_inputs=torch.half)
+    def forward(ctx, x, weight, bias, padding, stride):
+        outputs = fused_conv_bias_relu.forward_no_relu([x, weight, bias], padding, stride)
+        ctx.save_for_backward(x, weight)
+        ctx.padding = padding
+        ctx.stride = stride
+
+        return outputs[0]
+
+    @staticmethod
+    @torch.cuda.amp.custom_bwd
+    def backward(ctx, grad_output):
+        bwd_args = [*ctx.saved_tensors, grad_output]
+        padding = ctx.padding
+        stride = ctx.stride
+        grads = fused_conv_bias_relu.backward_no_relu(bwd_args, padding, stride)
+
+        return grads[0], grads[1], grads[2], None, None
+
+
+ConvBiasReLU = ConvBiasReLU_.apply
+ConvBiasMaskReLU = ConvBiasMaskReLU_.apply
+ConvBias = ConvBias_.apply
+

cudnn-frontend @ fa611998

-Subproject commit b4e1ad9613b89199982c9baf6ee91f6f98f5606d
+Subproject commit fa611998a360cbabaa2dcc7c9859748144114fc0

apex/contrib/csrc/fmha/fmha_api.cpp

@@ -72,7 +72,7 @@ void set_params(Fused_multihead_attention_fprop_params &params,
     constexpr float scale_softmax = 1.f;
     constexpr float scale_bmm2 = 1.f;
 
-    set_alpha(params.scale_bmm1, scale_bmm1, acc_type);
+    set_alpha(params.scale_bmm1, scale_bmm1, data_type);
     set_alpha(params.scale_softmax, scale_softmax, acc_type);
     set_alpha(params.scale_bmm2, scale_bmm2, data_type);
 
@@ -83,15 +83,21 @@ void set_params(Fused_multihead_attention_fprop_params &params,
     set_alpha(params.scale_dropout, params.rp_dropout, data_type);
 }
 
-std::vector<at::Tensor>
-mha_fwd(const at::Tensor &qkv,  // total x num_heads x 3 x head_size, total := \sum_{i=0}^{b} s_i
+std::vector<at::Tensor> 
+mha_fwd(const at::Tensor &qkv,         // total x num_heads x 3 x head_size, total := \sum_{i=0}^{b} s_i
         const at::Tensor &cu_seqlens,  // b+1
         const float p_dropout,
         const int max_seq_len,
         const bool is_training,
+        const bool is_nl,
+        const bool zero_tensors,
         c10::optional<at::Generator> gen_) {
+
     auto dprops = at::cuda::getCurrentDeviceProperties();
     TORCH_CHECK(dprops->major == 8 && dprops->minor == 0);
+    auto stream = at::cuda::getCurrentCUDAStream().stream();
+    Launch_params<Fused_multihead_attention_fprop_params> launch_params(dprops, stream, is_training, is_nl);
+
     int seq_len = 512;
     auto launch = &run_fmha_fp16_512_64_sm80;
     if( max_seq_len <= 128 ) {
@@ -110,18 +116,6 @@ mha_fwd(const at::Tensor &qkv,  // total x num_heads x 3 x head_size, total := \
         TORCH_CHECK(false);
     }
 
-    constexpr int warps_m = 1;
-    constexpr int warps_n = 4;  // this leads to an upper bound
-    const int mmas_m = seq_len / 16 / warps_m;
-    const int mmas_n = seq_len / 16 / warps_n;
-    
-    const int elts_per_thread = 8 * mmas_m * mmas_n;
-
-    auto stream = at::cuda::getCurrentCUDAStream().stream();
-
-    TORCH_CHECK(qkv.dtype() == torch::kFloat16);
-    TORCH_CHECK(cu_seqlens.dtype() == torch::kInt32);
-
     TORCH_CHECK(qkv.is_cuda())
     TORCH_CHECK(cu_seqlens.is_cuda())
 
@@ -147,12 +141,16 @@ mha_fwd(const at::Tensor &qkv,  // total x num_heads x 3 x head_size, total := \
 
     auto s = torch::empty({ batch_size, num_heads, seq_len, seq_len }, opts);
 
+    if( zero_tensors ) {
+        ctx.zero_();
+        s.zero_();
+    }
+
     auto gen = at::get_generator_or_default<at::CUDAGeneratorImpl>(
         gen_, at::cuda::detail::getDefaultCUDAGenerator());
 
-    Fused_multihead_attention_fprop_params params;
 
-    set_params(params,
+    set_params(launch_params.params,
                batch_size,
                seq_len,
                num_heads,
@@ -163,29 +161,32 @@ mha_fwd(const at::Tensor &qkv,  // total x num_heads x 3 x head_size, total := \
                s.data_ptr(),
                p_dropout);
 
-    // number of times random will be generated per thread, to offset philox counter in the random
+    launch(launch_params, /*configure=*/ true);
+    // number of times random will be generated per thread, to offset philox counter in thc random
     // state
-    int64_t counter_offset = elts_per_thread;
+    int64_t counter_offset = launch_params.elts_per_thread;
     at::PhiloxCudaState rng_engine_inputs;
 
     if( is_training ) {
         // See Note [Acquire lock when using random generators]
         std::lock_guard<std::mutex> lock(gen->mutex_);
-        params.philox_args = gen->philox_cuda_state(counter_offset);
+        launch_params.params.philox_args = gen->philox_cuda_state(counter_offset);
     }
 
-    launch(params, is_training, stream);
+    launch(launch_params, /*configure=*/ false);
 
     return { ctx, s };
 }
 
+
 std::vector<at::Tensor>
 mha_bwd(const at::Tensor &dout,  // total x num_heads, x head_size
         const at::Tensor &qkv,   // total x num_heads x 3 x head_size, total := \sum_{i=0}^{b} s_i
         at::Tensor &softmax,     // b x h x s x s softmax and dmask - will be overwritten with dP
         const at::Tensor &cu_seqlens,  // b+1
         const float p_dropout,         // probability to drop
-        const int max_seq_len          // max sequence length to choose the kernel
+        const int max_seq_len,          // max sequence length to choose the kernel
+        const bool zero_tensors
 ) {
     auto dprops = at::cuda::getCurrentDeviceProperties();
     TORCH_CHECK(dprops->major == 8 && dprops->minor == 0);
@@ -235,6 +236,10 @@ mha_bwd(const at::Tensor &dout,  // total x num_heads, x head_size
 
     auto dqkv = torch::empty_like(qkv);
 
+    if( zero_tensors ) {
+        dqkv.zero_();
+    }
+
     Fused_multihead_attention_fprop_params params;
 
     set_params(params,
@@ -259,92 +264,13 @@ mha_bwd(const at::Tensor &dout,  // total x num_heads, x head_size
     return { dqkv, softmax };
 }
 
-std::vector<at::Tensor> mha_fwd_nl(const at::Tensor &qkv,         // total x num_heads x 3 x head_size, total := \sum_{i=0}^{b} s_i
-                                const at::Tensor &cu_seqlens,  // b+1
-                                const float p_dropout,
-                                const int max_seq_len,
-                                const bool is_training,
-                                c10::optional<at::Generator> gen_) {
-    int seq_len = 512;
-    auto launch = &run_fmha_fp16_512_64_sm80_nl;
-    TORCH_CHECK(max_seq_len == seq_len);
-
-    constexpr int warps_m = 1;
-    constexpr int warps_n = 4;  // this leads to an upper bound
-    const int mmas_m = seq_len / 16 / warps_m;
-    const int mmas_n = seq_len / 16 / warps_n;
-    // static_assert( mmas_m == 32 );
-    // static_assert( mmas_n == 4 );
-    const int elts_per_thread = 8 * mmas_m * mmas_n;
-
-    auto stream = at::cuda::getCurrentCUDAStream().stream();
-
-    TORCH_CHECK(qkv.is_cuda())
-    TORCH_CHECK(cu_seqlens.is_cuda())
-
-    TORCH_CHECK(qkv.is_contiguous())
-    TORCH_CHECK(cu_seqlens.is_contiguous())
-
-    TORCH_CHECK(cu_seqlens.dim() == 1);
-    TORCH_CHECK(qkv.dim() == 4);
-
-    const auto sizes = qkv.sizes();
-
-    TORCH_CHECK(sizes[THREE_DIM] == 3);
-
-    const int batch_size = cu_seqlens.numel() - 1;
-    const int total = sizes[TOTAL_DIM];
-    const int num_heads = sizes[H_DIM];
-    const int head_size = sizes[D_DIM];
-    TORCH_CHECK(batch_size > 0);
-    TORCH_CHECK(head_size == 64);
-    auto opts = qkv.options();
-
-    auto ctx = torch::empty({ total, num_heads, head_size }, opts);
-
-    auto s = torch::empty({ batch_size, num_heads, seq_len, seq_len }, opts);
-
-    auto gen = at::get_generator_or_default<at::CUDAGeneratorImpl>(gen_, at::cuda::detail::getDefaultCUDAGenerator());
-
-    Fused_multihead_attention_fprop_params params;
-
-    set_params(params,
-               batch_size,
-               seq_len,
-               num_heads,
-               head_size,
-               qkv.data_ptr(),
-               cu_seqlens.data_ptr(),
-               ctx.data_ptr(),
-               s.data_ptr(),
-               p_dropout);
-
-    // number of times random will be generated per thread, to offset philox counter in the random
-    // state
-    int64_t counter_offset = elts_per_thread;
-    at::PhiloxCudaState rng_engine_inputs;
-
-    if( is_training ) {
-        // See Note [Acquire lock when using random generators]
-        std::lock_guard<std::mutex> lock(gen->mutex_);
-        params.philox_args = gen->philox_cuda_state(counter_offset);
-    }
-    int num_chunks = 3;
-    if(batch_size == 3) {
-        num_chunks = 2;
-    }
-
-    launch(params, is_training, num_chunks, stream);
-
-    return { ctx, s };
-}
-
 std::vector<at::Tensor> mha_bwd_nl(const at::Tensor &dout,        // total x num_heads, x head_size
                                 const at::Tensor &qkv,         // total x num_heads x 3 x head_size, total := \sum_{i=0}^{b} s_i
                                 at::Tensor &softmax,           // b x h x s x s softmax and dmask - will be overwritten with dP
                                 const at::Tensor &cu_seqlens,  // b+1
                                 const float p_dropout,         // probability to drop
-                                const int max_seq_len          // max sequence length to choose the kernel
+                                const int max_seq_len,          // max sequence length to choose the kernel
+                                const bool zero_tensors
 ) {
 
     auto stream = at::cuda::getCurrentCUDAStream().stream();
@@ -378,6 +304,10 @@ std::vector<at::Tensor> mha_bwd_nl(const at::Tensor &dout,        // total x num
 
     auto dqkv = torch::empty_like(qkv);
 
+    if( zero_tensors ) {
+        dqkv.zero_();
+    }
+    
     int num_chunks = 2;
     if( batch_size == 1 ) {
         num_chunks = 4;
@@ -427,6 +357,5 @@ PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
     m.doc() = "Fused Multi-head Self-attention for BERT";  
     m.def("fwd", &mha_fwd, "Forward pass");
     m.def("bwd", &mha_bwd, "Backward pass");
-    m.def("fwd_nl", &mha_fwd_nl, "Forward pass (small-batch)");
     m.def("bwd_nl", &mha_bwd_nl, "Backward pass (small-batch)");
 }

apex/contrib/csrc/fmha/src/fmha.h

@@ -30,7 +30,7 @@
 #include <cuda.h>
 #include <vector>
 
-#if !defined(NEW_GENERATOR_PATH)
+#ifdef OLD_GENERATOR_PATH
 #include <ATen/CUDAGeneratorImpl.h>
 #else
 #include <ATen/cuda/CUDAGeneratorImpl.h>
@@ -50,7 +50,7 @@ constexpr int D_DIM = 3;
 
 struct Qkv_params {
     // The QKV matrices.
-    void *qkv_ptr;
+    void * __restrict__ qkv_ptr;
 
     // The stride between rows of the Q, K and V matrices.
     size_t qkv_stride_in_bytes;
@@ -64,19 +64,19 @@ struct Qkv_params {
 struct Fused_multihead_attention_fprop_params : public Qkv_params {
 
     // The dQKV matrices.
-    void *dqkv_ptr;
+    void * __restrict__ dqkv_ptr;
 
     // Temporary for dKV.
-    void *dkv_ptr;
+    void * __restrict__ dkv_ptr;
 
     // The O matrix (output).
-    void *o_ptr;
+    void * __restrict__ o_ptr;
 
     // The stride between rows of O.
     int64_t o_stride_in_bytes;
 
     // The pointer to the S matrix, overwritten by the dP matrix (bwd).
-    void *s_ptr;
+    void * __restrict__ s_ptr;
     // The stride between rows of the S matrix.
     int64_t s_stride_in_bytes;
 
@@ -87,7 +87,7 @@ struct Fused_multihead_attention_fprop_params : public Qkv_params {
     uint32_t scale_bmm1, scale_softmax, scale_bmm2;
 
     // array of length b+1 holding starting offset of each sequence.
-    int *cu_seqlens;
+    int * __restrict__ cu_seqlens;
 
     // The dropout probability (probability of keeping an activation).
     float p_dropout;
@@ -104,10 +104,43 @@ struct Fused_multihead_attention_fprop_params : public Qkv_params {
 
 ////////////////////////////////////////////////////////////////////////////////////////////////////
 
-void run_fmha_fp16_128_64_sm80(const Fused_multihead_attention_fprop_params &params, bool is_training, cudaStream_t stream);
-void run_fmha_fp16_256_64_sm80(const Fused_multihead_attention_fprop_params &params, bool is_training, cudaStream_t stream);
-void run_fmha_fp16_384_64_sm80(const Fused_multihead_attention_fprop_params &params, bool is_training, cudaStream_t stream);
-void run_fmha_fp16_512_64_sm80(const Fused_multihead_attention_fprop_params &params, bool is_training, cudaStream_t stream);
+template<typename Kernel_params> 
+struct Launch_params{
+    Launch_params(cudaDeviceProp * props_,
+                  cudaStream_t stream_,
+                  bool is_training_,
+                  bool is_nl_) 
+        : elts_per_thread(0)
+        , props(props_)
+        , stream(stream_)
+        , is_training(is_training_)
+        , is_nl(is_nl_) {
+    }
+
+    size_t elts_per_thread;
+
+    cudaDeviceProp * props;
+
+    cudaStream_t stream;
+
+    bool is_training;
+
+    Kernel_params params;
+    int num_full_heads;
+    int num_main_groups;
+    int heads_last_wave;
+    int main_steps;
+    int rest_steps;
+    bool is_nl;
+
+};
+
+////////////////////////////////////////////////////////////////////////////////////////////////////
+
+void run_fmha_fp16_128_64_sm80(Launch_params<Fused_multihead_attention_fprop_params> &launch_params, const bool configure);
+void run_fmha_fp16_256_64_sm80(Launch_params<Fused_multihead_attention_fprop_params> &launch_params, const bool configure);
+void run_fmha_fp16_384_64_sm80(Launch_params<Fused_multihead_attention_fprop_params> &launch_params, const bool configure);
+void run_fmha_fp16_512_64_sm80(Launch_params<Fused_multihead_attention_fprop_params> &launch_params, const bool configure);
 
 void run_fmha_dgrad_fp16_128_64_sm80(const Fused_multihead_attention_fprop_params &params, cudaStream_t stream);
 void run_fmha_dgrad_fp16_256_64_sm80(const Fused_multihead_attention_fprop_params &params, cudaStream_t stream);

apex/contrib/csrc/fmha/src/fmha/gemm.h

@@ -210,9 +210,6 @@ struct Clear_accumulator<float, WARPS_K> {
   }
 };
 
-////////////////////////////////////////////////////////////////////////////////////////////////////
-
-
 ////////////////////////////////////////////////////////////////////////////////////////////////////
 
 template<typename Acc, typename A, typename B, int M, int N>

apex/contrib/csrc/fmha/src/fmha/gmem_tile.h

@@ -60,7 +60,7 @@ struct Gmem_tile_qkv {
 
     // Ctor.
     template< typename Params, typename BInfo >
-    inline __device__ Gmem_tile_qkv(const Params &params, int qkv_offset, const BInfo &binfo, int tidx)
+    inline __device__ Gmem_tile_qkv(const Params &params, const int qkv_offset, const BInfo &binfo, const int tidx)
         : params_qkv_stride_in_bytes_(params.qkv_stride_in_bytes)
         , actual_seqlen(binfo.actual_seqlen)
         , qkv_ptr_(reinterpret_cast<char *>(params.qkv_ptr)) {
@@ -125,6 +125,11 @@ struct Gmem_tile_qkv {
         actual_seqlen -= ROWS;
     }
 
+    inline __device__ void move(int steps) {
+        qkv_ptr_ += (int64_t)ROWS * params_qkv_stride_in_bytes_ * steps;
+        actual_seqlen -= ROWS * steps;
+    }
+
     // The stride between rows for the QKV matrice.
     int64_t params_qkv_stride_in_bytes_;
     // The pointer.
@@ -224,6 +229,11 @@ struct Gmem_tile_o {
         o_ptr_ += (int64_t)ROWS * params_o_stride_in_bytes_;
     }
 
+    inline __device__ void move(const int steps) {
+        row_ += ROWS * steps;
+        o_ptr_ += (int64_t)ROWS * params_o_stride_in_bytes_ * steps;
+    }
+
     // The stride between rows for the QKV matrice.
     int64_t params_o_stride_in_bytes_;
     // The pointer.
@@ -270,13 +280,9 @@ struct Gmem_tile_mma_sd {
 
     // Ctor.
     template<typename Params>
-    inline __device__ Gmem_tile_mma_sd(void *ptr, const Params &params, const int tidx) 
+    inline __device__ Gmem_tile_mma_sd(void *ptr, const Params &params, const int bidb, const int bidh, const int tidx) 
         : ptr_(static_cast<char *>(ptr)) {
 
-        // The block index for the batch.
-        const int bidb = blockIdx.y;
-        // The block index for the head.
-        const int bidh = blockIdx.x;
         // The block index.
         size_t bidx = bidb * params.h + bidh;
 
@@ -300,6 +306,9 @@ struct Gmem_tile_mma_sd {
     inline __device__ void move() {
         ptr_ += LOOP_STRIDE_BYTES;
     }
+    inline __device__ void move(const int steps) {
+        ptr_ += LOOP_STRIDE_BYTES * steps;
+    }
 
     // The pointer in global memory.
     char *ptr_;
@@ -318,9 +327,9 @@ struct Gmem_tile_mma_s : public Base {
     using Type = typename Base::Type;
 
     // Ctor.
-    template< typename Params >
-    inline __device__ Gmem_tile_mma_s(void *ptr, const Params &params, const int tidx) 
-        : Base(ptr, params, tidx) {
+    template< typename Params, typename Block_info >
+    inline __device__ Gmem_tile_mma_s(const Params &params, const Block_info& binfo, const int tidx) 
+        : Base(params.s_ptr, params, binfo.bidb, binfo.bidh, tidx) {
     }
 
     // Store to global memory.
@@ -353,6 +362,25 @@ struct Gmem_tile_mma_s : public Base {
         }
     }
 
+    // Store to global memory.
+    template<typename Mask, typename Fragment>
+    inline __device__ void store(const Fragment (&frag)[N][M], const Mask& mask){
+        #pragma unroll
+        for( int mi = 0; mi < M; mi++ ) {
+            #pragma unroll
+            for( int ni = 0; ni < N; ni++ ) {
+                uint4 dst;
+                dst.x = frag[ni][mi].reg(0);
+                dst.y = frag[ni][mi].reg(2);
+                dst.z = frag[ni][mi].reg(1);
+                dst.w = frag[ni][mi].reg(3);
+                if( mask.any_valid(mi, ni) ) {
+                    Base::store(dst, mi, ni);
+                }
+            }
+        }
+    }
+
     // Load from global memory.
     template<typename Mask>
     inline __device__ void load(uint4 (&regs)[M][N], const Mask &mask) {
@@ -361,7 +389,7 @@ struct Gmem_tile_mma_s : public Base {
             #pragma unroll
             for( int ni = 0; ni < N; ni++ ) {
                 regs[mi][ni] = make_uint4(0, 0, 0, 0);
-                if( mask.is_valid(mi, ni, 0, 0) ) {
+                if( mask.any_valid(mi, ni) ) {
                     Base::load(regs[mi][ni], mi, ni);
                 }
             }

apex/contrib/csrc/fmha/src/fmha/kernel_traits.h

@@ -29,7 +29,7 @@
 
 ////////////////////////////////////////////////////////////////////////////////////////////////////
 
-template<int S, int D, int STEP, int WARPS_M, int WARPS_N, uint32_t FLAGS = 0x8u>
+template<int S, int D, int STEP, int WARPS_M, int WARPS_N, uint32_t FLAGS = 0x08u>
 struct FMHA_kernel_traits {
 
     // The CTA description for the 1st GEMM.
@@ -38,7 +38,9 @@ struct FMHA_kernel_traits {
     using Cta_tile_o = fmha::Cta_tile_extd<STEP, D, S, WARPS_M, 1, WARPS_N>;
 
     // Do we use one buffer for K and V.
-    enum { SHARE_SMEM_FOR_K_AND_V = (FLAGS & 0x8u) != 0u };
+    enum { SHARE_SMEM_FOR_K_AND_V = (FLAGS & 0x08u) != 0u };
+    // Do we keep K in registers.
+    enum { K_IN_REGS = (FLAGS & 0x10u) == 0u };
 
     // The global memory tile to load Q.
     using Gmem_tile_q = fmha::Gmem_tile_qkv<Cta_tile_p, fmha::BITS_PER_ELEMENT_A, STEP, D>;