transformer utils (NVIDIA#1181)

Co-authored-by: Piotr Bialecki <pbialecki@nvidia.com>
Co-authored-by: Eddie Yan <eddiey@nvidia.com>
Co-authored-by: Rishi Puri <riship@nvidia.com>
Co-authored-by: Sangkug Lym <slym@nvidia.com>

.gitignore

@@ -4,5 +4,144 @@ build
 docs/build
 *~
 __pycache__
+.vscode
+
+# Copied from https://raw.githubusercontent.com/github/gitignore/master/Python.gitignore
+# Byte-compiled / optimized / DLL files
+__pycache__/
+*.py[cod]
+*$py.class
+
+# C extensions
 *.so
-.vscode
+
+# Distribution / packaging
+.Python
+build/
+develop-eggs/
+dist/
+downloads/
+eggs/
+.eggs/
+lib/
+lib64/
+parts/
+sdist/
+var/
+wheels/
+share/python-wheels/
+*.egg-info/
+.installed.cfg
+*.egg
+MANIFEST
+
+# PyInstaller
+#  Usually these files are written by a python script from a template
+#  before PyInstaller builds the exe, so as to inject date/other infos into it.
+*.manifest
+*.spec
+
+# Installer logs
+pip-log.txt
+pip-delete-this-directory.txt
+
+# Unit test / coverage reports
+htmlcov/
+.tox/
+.nox/
+.coverage
+.coverage.*
+.cache
+nosetests.xml
+coverage.xml
+*.cover
+*.py,cover
+.hypothesis/
+.pytest_cache/
+cover/
+
+# Translations
+*.mo
+*.pot
+
+# Django stuff:
+*.log
+local_settings.py
+db.sqlite3
+db.sqlite3-journal
+
+# Flask stuff:
+instance/
+.webassets-cache
+
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+.scrapy
+
+# Sphinx documentation
+docs/_build/
+
+# PyBuilder
+.pybuilder/
+target/
+
+# Jupyter Notebook
+.ipynb_checkpoints
+
+# IPython
+profile_default/
+ipython_config.py
+
+# pyenv
+#   For a library or package, you might want to ignore these files since the code is
+#   intended to run in multiple environments; otherwise, check them in:
+# .python-version
+
+# pipenv
+#   According to pypa/pipenv#598, it is recommended to include Pipfile.lock in version control.
+#   However, in case of collaboration, if having platform-specific dependencies or dependencies
+#   having no cross-platform support, pipenv may install dependencies that don't work, or not
+#   install all needed dependencies.
+#Pipfile.lock
+
+# PEP 582; used by e.g. github.com/David-OConnor/pyflow
+__pypackages__/
+
+# Celery stuff
+celerybeat-schedule
+celerybeat.pid
+
+# SageMath parsed files
+*.sage.py
+
+# Environments
+.env
+.venv
+env/
+venv/
+ENV/
+env.bak/
+venv.bak/
+
+# Spyder project settings
+.spyderproject
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+
+# Rope project settings
+.ropeproject
+
+# mkdocs documentation
+/site
+
+# mypy
+.mypy_cache/
+.dmypy.json
+dmypy.json
+
+# Pyre type checker
+.pyre/
+
+# pytype static type analyzer
+.pytype/
+
+# Cython debug symbols
+cython_debug/

apex/__init__.py

@@ -18,3 +18,4 @@
 from . import optimizers
 from . import normalization
 from . import pyprof
+from . import transformer

apex/_autocast_utils.py

@@ -0,0 +1,8 @@
+import torch
+
+
+def _cast_if_autocast_enabled(*args):
+    if not torch.is_autocast_enabled():
+        return args
+    else:
+        return torch.cuda.amp.autocast_mode._cast(args, torch.get_autocast_gpu_dtype())

apex/normalization/__init__.py

@@ -1 +1 @@
-from .fused_layer_norm import FusedLayerNorm
+from .fused_layer_norm import FusedLayerNorm, MixedFusedLayerNorm

apex/normalization/fused_layer_norm.py

@@ -1,71 +1,103 @@
-import math
-import torch
+import importlib
 import numbers
+
+import torch
 from torch.nn.parameter import Parameter
 from torch.nn import init
 from torch.nn import functional as F
-import importlib
+
+from apex._autocast_utils import _cast_if_autocast_enabled
 
 global fused_layer_norm_cuda
 fused_layer_norm_cuda = None
 
+
 class FusedLayerNormAffineFunction(torch.autograd.Function):
+    @staticmethod
+    def forward(ctx, input, weight, bias, normalized_shape, eps):
+        global fused_layer_norm_cuda
+        if fused_layer_norm_cuda is None:
+            fused_layer_norm_cuda = importlib.import_module("fused_layer_norm_cuda")
+        ctx.normalized_shape = normalized_shape
+        ctx.eps = eps
+        input_ = input.contiguous()
+        weight_ = weight.contiguous()
+        bias_ = bias.contiguous()
+        output, mean, invvar = fused_layer_norm_cuda.forward_affine(
+            input_, ctx.normalized_shape, weight_, bias_, ctx.eps
+        )
+        ctx.save_for_backward(input_, weight_, bias_, mean, invvar)
+        return output
+
+    @staticmethod
+    def backward(ctx, grad_output):
+        input_, weight_, bias_, mean, invvar = ctx.saved_tensors
+        grad_input = grad_weight = grad_bias = None
+        grad_input, grad_weight, grad_bias = fused_layer_norm_cuda.backward_affine(
+            grad_output.contiguous(), mean, invvar, input_, ctx.normalized_shape, weight_, bias_, ctx.eps
+        )
+        return grad_input, grad_weight, grad_bias, None, None
+
+
+class FusedLayerNormAffineMixedDtypesFunction(FusedLayerNormAffineFunction):
+
+    @staticmethod
+    def forward(ctx, input, weight, bias, normalized_shape, eps):
+        global fused_layer_norm_cuda
+        if fused_layer_norm_cuda is None:
+            fused_layer_norm_cuda = importlib.import_module("fused_layer_norm_cuda")
+        ctx.normalized_shape = normalized_shape
+        ctx.eps = eps
+        input_ = input.contiguous()
+        weight_ = weight.contiguous()
+        bias_ = bias.contiguous()
+        output, mean, invvar = fused_layer_norm_cuda.forward_affine_mixed_dtypes(
+            input_, ctx.normalized_shape, weight_, bias_, ctx.eps
+        )
+        ctx.save_for_backward(input_, weight_, bias_, mean, invvar)
+        return output
 
-  @staticmethod
-  def forward(ctx, input, weight, bias, normalized_shape, eps):
-    global fused_layer_norm_cuda
-    if fused_layer_norm_cuda is None:
-        fused_layer_norm_cuda = importlib.import_module("fused_layer_norm_cuda")
-    ctx.normalized_shape = normalized_shape
-    ctx.eps = eps
-    input_ = input.contiguous()
-    weight_ = weight.contiguous()
-    bias_ = bias.contiguous()
-    output, mean, invvar = fused_layer_norm_cuda.forward_affine(
-        input_, ctx.normalized_shape, weight_, bias_, ctx.eps)
-    ctx.save_for_backward(input_, weight_, bias_, mean, invvar)
-    return output
-
-  @staticmethod
-  def backward(ctx, grad_output):
-    input_, weight_, bias_, mean, invvar = ctx.saved_tensors
-    grad_input = grad_weight = grad_bias = None
-    grad_input, grad_weight, grad_bias = fused_layer_norm_cuda.backward_affine(
-        grad_output.contiguous(), mean, invvar,
-        input_, ctx.normalized_shape,
-        weight_, bias_, ctx.eps)
-    return grad_input, grad_weight, grad_bias, None, None
 
 class FusedLayerNormFunction(torch.autograd.Function):
+    @staticmethod
+    def forward(ctx, input, normalized_shape, eps):
+        global fused_layer_norm_cuda
+        if fused_layer_norm_cuda is None:
+            fused_layer_norm_cuda = importlib.import_module("fused_layer_norm_cuda")
+        ctx.normalized_shape = normalized_shape
+        ctx.eps = eps
+        input_ = input.contiguous()
+        output, mean, invvar = fused_layer_norm_cuda.forward(input_, ctx.normalized_shape, ctx.eps)
+        ctx.save_for_backward(input_, mean, invvar)
+        return output
+
+    @staticmethod
+    def backward(ctx, grad_output):
+        input_, mean, invvar = ctx.saved_tensors
+        grad_input = None
+        grad_input = fused_layer_norm_cuda.backward(
+            grad_output.contiguous(), mean, invvar, input_, ctx.normalized_shape, ctx.eps
+        )
+        return grad_input, None, None
+
+
+def fused_layer_norm_affine(input, weight, bias, normalized_shape, eps=1e-6):
+    args = _cast_if_autocast_enabled(input, weight, bias, normalized_shape, eps)
+    with torch.cuda.amp.autocast(enabled=False):
+        return FusedLayerNormAffineFunction.apply(*args)
 
-  @staticmethod
-  def forward(ctx, input, normalized_shape, eps):
-    global fused_layer_norm_cuda
-    if fused_layer_norm_cuda is None:
-        fused_layer_norm_cuda = importlib.import_module("fused_layer_norm_cuda")
-    ctx.normalized_shape = normalized_shape
-    ctx.eps = eps
-    input_ = input.contiguous()
-    output, mean, invvar = fused_layer_norm_cuda.forward(
-        input_, ctx.normalized_shape, ctx.eps)
-    ctx.save_for_backward(input_, mean, invvar)
-    return output
-
-  @staticmethod
-  def backward(ctx, grad_output):
-    input_, mean, invvar = ctx.saved_tensors
-    grad_input = None
-    grad_input = fused_layer_norm_cuda.backward(
-        grad_output.contiguous(), mean, invvar,
-        input_, ctx.normalized_shape,
-        ctx.eps)
-    return grad_input, None, None
-
-def fused_layer_norm_affine(input, normalized_shape, weight, bias, eps=1e-6):
-    return FusedLayerNormAffineFunction.apply(input, weight, bias, normalized_shape, eps)
 
 def fused_layer_norm(input, normalized_shape, eps=1e-6):
-    return FusedLayerNormFunction.apply(input, normalized_shape, eps)
+    args = _cast_if_autocast_enabled(input, normalized_shape, eps)
+    with torch.cuda.amp.autocast(enabled=False):
+        return FusedLayerNormFunction.apply(*args)
+
+
+def mixed_dtype_fused_layer_norm_affine(input, weight, bias, normalized_shape, eps=1e-6):
+    args = _cast_if_autocast_enabled(input, weight, bias, normalized_shape, eps)
+    with torch.cuda.amp.autocast(enabled=False):
+        return FusedLayerNormAffineMixedDtypesFunction.apply(*args)
+
 
 class FusedLayerNorm(torch.nn.Module):
     r"""Applies Layer Normalization over a mini-batch of inputs as described in
@@ -126,8 +158,9 @@ class FusedLayerNorm(torch.nn.Module):
 
     .. _`Layer Normalization`: https://arxiv.org/abs/1607.06450
     """
+
     def __init__(self, normalized_shape, eps=1e-5, elementwise_affine=True):
-        super(FusedLayerNorm, self).__init__()
+        super().__init__()
 
         global fused_layer_norm_cuda
         fused_layer_norm_cuda = importlib.import_module("fused_layer_norm_cuda")
@@ -141,8 +174,8 @@ def __init__(self, normalized_shape, eps=1e-5, elementwise_affine=True):
             self.weight = Parameter(torch.Tensor(*normalized_shape))
             self.bias = Parameter(torch.Tensor(*normalized_shape))
         else:
-            self.register_parameter('weight', None)
-            self.register_parameter('bias', None)
+            self.register_parameter("weight", None)
+            self.register_parameter("bias", None)
         self.reset_parameters()
 
     def reset_parameters(self):
@@ -152,14 +185,34 @@ def reset_parameters(self):
 
     def forward(self, input):
         if not input.is_cuda:
-            return  F.layer_norm(
-                input, self.normalized_shape, self.weight, self.bias, self.eps)
+            return F.layer_norm(input, self.normalized_shape, self.weight, self.bias, self.eps)
         if self.elementwise_affine:
-          return FusedLayerNormAffineFunction.apply(
-              input, self.weight, self.bias, self.normalized_shape,self.eps)
+            return fused_layer_norm_affine(input, self.weight, self.bias, self.normalized_shape, self.eps)
         else:
-          return FusedLayerNormFunction.apply(input, self.normalized_shape, self.eps)
+            return fused_layer_norm(input, self.normalized_shape, self.eps)
 
     def extra_repr(self):
-        return '{normalized_shape}, eps={eps}, ' \
-            'elementwise_affine={elementwise_affine}'.format(**self.__dict__)
+        return "{normalized_shape}, eps={eps}, " "elementwise_affine={elementwise_affine}".format(**self.__dict__)
+
+
+# NOTE (mkozuki): Why "mixed"?
+# MixedFusedLayerNorm differs from FusedLayerNorm in that this layer norm uses parameter's dtype
+# as output tensor's dtype while FusedLayerNorm uses input tensor's dtype for output tensor's dtype.
+# See: `layer_norm_affine` and `layer_norm_affine_mixed_dtypes` in "csrc/layer_norm_cuda.cpp"
+class MixedFusedLayerNorm(FusedLayerNorm):
+
+    def __init__(self, normalized_shape, eps=1e-5, **kwargs):
+        if "elementwise_affine" in kwargs:
+            import warnings
+            warnings.warn("MixedFusedLayerNorm does not support `elementwise_affine` argument")
+            elementwise_affine = kwargs.pop("elementwise_affine")
+            if not elementwise_affine:
+                raise RuntimeError("MixedFusedLayerNorm does not support `elementwise_affine = False`")
+
+        super().__init__(normalized_shape=normalized_shape, eps=eps, elementwise_affine=True)
+
+    def forward(self, input: torch.Tensor):
+        # NOTE (mkozuki): CPU path is here mainly for unittest sake.
+        if not input.is_cuda:
+            return F.layer_norm(input, self.normalized_shape, self.weight, self.bias, self.eps)
+        return mixed_dtype_fused_layer_norm_affine(input, self.weight, self.bias, self.normalized_shape, self.eps)

apex/transformer/README.md

@@ -0,0 +1,5 @@
+# apex.transformer
+
+`apex.transformer` is a module which enables efficient large Transformer models at scale.
+
+`apex.transformer.tensor_parallel` is based on [NVIDIA/Megatron-LM](https://github.com/NVIDIA/Megatron-LM)'s `megatron.mpu` module.