FusedRMSNorm/"T5LayerNorm" based on FusedLayerNorm

apex/normalization/__init__.py

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

apex/normalization/fused_layer_norm.py

@@ -12,6 +12,23 @@
 fused_layer_norm_cuda = None
 
 
+# Reference implementation from Huggingface
+def manual_rms_norm(input, normalized_shape, weight, eps):
+    # layer norm should always be calculated in float32
+    dims = tuple(i for i in range(-1, -len(normalized_shape)-1, -1))
+    variance = input.to(torch.float32).pow(2).mean(dims, keepdim=True)
+    input = input * torch.rsqrt(variance + eps)
+
+    if weight is None:
+        return input
+
+    # convert into half-precision if necessary
+    if weight.dtype in [torch.float16, torch.bfloat16]:
+        input = input.to(self.weight.dtype)
+
+    return weight * input
+
+
 class FusedLayerNormAffineFunction(torch.autograd.Function):
     @staticmethod
     def forward(ctx, input, weight, bias, normalized_shape, eps):
@@ -39,6 +56,31 @@ def backward(ctx, grad_output):
         return grad_input, grad_weight, grad_bias, None, None
 
 
+class FusedRMSNormAffineFunction(torch.autograd.Function):
+    @staticmethod
+    def forward(ctx, input, weight, 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()
+        output, invvar = fused_layer_norm_cuda.rms_forward_affine(
+            input_, ctx.normalized_shape, weight_, ctx.eps)
+        ctx.save_for_backward(input_, weight_, invvar)
+        return output
+
+    @staticmethod
+    def backward(ctx, grad_output):
+        input_, weight_, invvar = ctx.saved_tensors
+        grad_input = grad_weight = None
+        grad_input, grad_weight = fused_layer_norm_cuda.rms_backward_affine(
+           grad_output.contiguous(), invvar, input_, ctx.normalized_shape, weight_, ctx.eps
+        )
+        return grad_input, grad_weight, None, None
+
+
 class FusedLayerNormAffineMixedDtypesFunction(FusedLayerNormAffineFunction):
 
     @staticmethod
@@ -58,6 +100,25 @@ def forward(ctx, input, weight, bias, normalized_shape, eps):
         return output
 
 
+class FusedRMSNormAffineMixedDtypesFunction(FusedRMSNormAffineFunction):
+
+    @staticmethod
+    def forward(ctx, input, weight, 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()
+        output, invvar = fused_layer_norm_cuda.rms_forward_affine_mixed_dtypes(
+            input_, ctx.normalized_shape, weight_, ctx.eps
+        )
+
+        ctx.save_for_backward(input_, weight_, invvar)
+        return output
+
+
 class FusedLayerNormFunction(torch.autograd.Function):
     @staticmethod
     def forward(ctx, input, normalized_shape, eps):
@@ -81,6 +142,29 @@ def backward(ctx, grad_output):
         return grad_input, None, None
 
 
+class FusedRMSNormFunction(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, invvar = fused_layer_norm_cuda.rms_forward(input_, ctx.normalized_shape, ctx.eps)
+        ctx.save_for_backward(input_, invvar)
+        return output
+
+    @staticmethod
+    def backward(ctx, grad_output):
+        input_, invvar = ctx.saved_tensors
+        grad_input = None
+        grad_input = fused_layer_norm_cuda.rms_backward(
+            grad_output.contiguous(), 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):
@@ -99,6 +183,24 @@ def mixed_dtype_fused_layer_norm_affine(input, weight, bias, normalized_shape, e
         return FusedLayerNormAffineMixedDtypesFunction.apply(*args)
 
 
+def fused_rms_norm_affine(input, weight, normalized_shape, eps=1e-6):
+    args = _cast_if_autocast_enabled(input, weight, normalized_shape, eps)
+    with torch.cuda.amp.autocast(enabled=False):
+        return FusedRMSNormAffineFunction.apply(*args)
+
+
+def fused_rms_norm(input, normalized_shape, eps=1e-6):
+    args = _cast_if_autocast_enabled(input, normalized_shape, eps)
+    with torch.cuda.amp.autocast(enabled=False):
+        return FusedRMSNormFunction.apply(*args)
+
+
+def mixed_dtype_fused_rms_norm_affine(input, weight, normalized_shape, eps=1e-6):
+    args = _cast_if_autocast_enabled(input, weight, normalized_shape, eps)
+    with torch.cuda.amp.autocast(enabled=False):
+        return FusedRMSNormAffineMixedDtypesFunction.apply(*args)
+
+
 class FusedLayerNorm(torch.nn.Module):
     r"""Applies Layer Normalization over a mini-batch of inputs as described in
     the paper `Layer Normalization`_ .
@@ -195,6 +297,99 @@ def extra_repr(self):
         return "{normalized_shape}, eps={eps}, " "elementwise_affine={elementwise_affine}".format(**self.__dict__)
 
 
+class FusedRMSNorm(torch.nn.Module):
+    r"""Applies RMS Normalization over a mini-batch of inputs
+
+    Currently only runs on cuda() tensors.
+
+    .. math::
+        y = \frac{x - \mathrm{E}[x]}{ \sqrt{\mathrm{Var}[x] + \epsilon}} * \gamma + \beta
+
+    The mean and standard-deviation are calculated separately over the last
+    certain number dimensions which have to be of the shape specified by
+    :attr:`normalized_shape`.
+    :math:`\gamma` and :math:`\beta` are learnable affine transform parameters of
+    :attr:`normalized_shape` if :attr:`elementwise_affine` is ``True``.
+
+    .. note::
+        Unlike Batch Normalization and Instance Normalization, which applies
+        scalar scale and bias for each entire channel/plane with the
+        :attr:`affine` option, Layer Normalization applies per-element scale and
+        bias with :attr:`elementwise_affine`.
+
+    This layer uses statistics computed from input data in both training and
+    evaluation modes.
+
+    Args:
+        normalized_shape (int or list or torch.Size): input shape from an expected input
+            of size
+
+            .. math::
+                [* \times \text{normalized}\_\text{shape}[0] \times \text{normalized}\_\text{shape}[1]
+                    \times \ldots \times \text{normalized}\_\text{shape}[-1]]
+
+            If a single integer is used, it is treated as a singleton list, and this module will
+            normalize over the last dimension which is expected to be of that specific size.
+        eps: a value added to the denominator for numerical stability. Default: 1e-5
+        elementwise_affine: a boolean value that when set to ``True``, this module
+            has learnable per-element affine parameters initialized to ones (for weights)
+            and zeros (for biases). Default: ``True``.
+
+    Shape:
+        - Input: :math:`(N, *)`
+        - Output: :math:`(N, *)` (same shape as input)
+
+    Examples::
+
+        >>> input = torch.randn(20, 5, 10, 10)
+        >>> # With Learnable Parameters
+        >>> m = apex.normalization.FusedRMSNorm(input.size()[1:])
+        >>> # Without Learnable Parameters
+        >>> m = apex.normalization.FusedRMSNorm(input.size()[1:], elementwise_affine=False)
+        >>> # Normalize over last two dimensions
+        >>> m = apex.normalization.FusedRMSNorm([10, 10])
+        >>> # Normalize over last dimension of size 10
+        >>> m = apex.normalization.FusedRMSNorm(10)
+        >>> # Activating the module
+        >>> output = m(input)
+
+    .. _`Layer Normalization`: https://arxiv.org/abs/1607.06450
+    """
+
+    def __init__(self, normalized_shape, eps=1e-5, elementwise_affine=True):
+        super().__init__()
+
+        global fused_layer_norm_cuda
+        fused_layer_norm_cuda = importlib.import_module("fused_layer_norm_cuda")
+
+        if isinstance(normalized_shape, numbers.Integral):
+            normalized_shape = (normalized_shape,)
+        self.normalized_shape = torch.Size(normalized_shape)
+        self.eps = eps
+        self.elementwise_affine = elementwise_affine
+        if self.elementwise_affine:
+            self.weight = Parameter(torch.Tensor(*normalized_shape))
+        else:
+            self.register_parameter("weight", None)
+        self.reset_parameters()
+
+    def reset_parameters(self):
+        if self.elementwise_affine:
+            init.ones_(self.weight)
+
+    def forward(self, input):
+        if not input.is_cuda:
+            return manual_rms_norm(input, self.normalized_shape, self.weight, self.eps)
+
+        if self.elementwise_affine:
+            return fused_rms_norm_affine(input, self.weight, self.normalized_shape, self.eps)
+        else:
+            return fused_rms_norm(input, self.normalized_shape, self.eps)
+
+    def extra_repr(self):
+        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.
@@ -216,3 +411,26 @@ def forward(self, input: torch.Tensor):
         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)
+
+
+# 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 MixedFusedRMSNorm(FusedRMSNorm):
+
+    def __init__(self, normalized_shape, eps=1e-5, **kwargs):
+        if "elementwise_affine" in kwargs:
+            import warnings
+            warnings.warn("MixedFusedRMSNorm does not support `elementwise_affine` argument")
+            elementwise_affine = kwargs.pop("elementwise_affine")
+            if not elementwise_affine:
+                raise RuntimeError("MixedFusedRMSNorm 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.
+        # TODO Manual RMS Norm Implementation Here
+        if not input.is_cuda:
+            return manual_rms_norm(input, self.normalized_shape, self.weight, self.eps)
+        return mixed_dtype_fused_rms_norm_affine(input, self.weight, self.normalized_shape, self.eps)