Merge branch 'FDecaYed-deyuf/fused_optimizer_v2'

apex/contrib/xentropy/softmax_xentropy.py

+import torch
+import xentropy_cuda
+
+class SoftmaxCrossEntropyLoss(torch.autograd.Function):
+    @staticmethod
+    def forward(ctx, logits, labels, smoothing=0.0, padding_idx=0, half_to_float=False):
+        losses, max_log_sum_exp = xentropy_cuda.forward(
+            logits, labels, smoothing, half_to_float)
+        losses.masked_fill_(labels==padding_idx, 0)
+
+        ctx.save_for_backward(logits, max_log_sum_exp, labels,
+            torch.FloatTensor([smoothing]),
+            torch.LongTensor([padding_idx]))
+
+        return losses
+
+    @staticmethod
+    def backward(ctx, grad_loss):
+        logits, max_log_sum_exp, labels, smoothing, padding_idx = ctx.saved_tensors
+
+        if not grad_loss.is_contiguous():
+            grad_loss = grad_loss.contiguous()
+        grad_loss.masked_fill_(labels==padding_idx.item(), 0)
+        grad_logits = xentropy_cuda.backward(
+            grad_loss.contiguous(), logits, max_log_sum_exp,
+            labels, smoothing.item())
+
+        return grad_logits, None, None, None, None

apex/optimizers/__init__.py

+from .fused_sgd import FusedSGD
 from .fused_adam import FusedAdam
+from .fused_novograd import FusedNovoGrad
+from .fused_lamb import FusedLAMB
 from .fp16_optimizer import FP16_Optimizer

apex/optimizers/fp16_optimizer.py

@@ -35,6 +35,8 @@ class FP16_Optimizer(object):
                  dynamic_loss_args=None,
                  verbose=True):
 
+        print("\nfp16_optimizer is designed to only work with apex.optimizers, and will be removed in future")
+        print("To update, use updated optimizers with AMP.")
         # The fused optimizer does all the work. We need this layer for two reason:
         # 1. maintain same user API from apex.fp16_utils
         # 2. keep common stuff here in case we need to add new fused optimizer later

apex/optimizers/fused_adam.py

-import types
 import torch
-import importlib
+from apex.multi_tensor_apply import multi_tensor_applier
+from amp_C import multi_tensor_adam
 
 class FusedAdam(torch.optim.Optimizer):
 
     """Implements Adam algorithm. Currently GPU-only.  Requires Apex to be installed via
     ``python setup.py install --cuda_ext --cpp_ext``.
 
+    This version of fused adam implements 2 fusion:
+      - Fusion of operations within adam optimizer
+      - Apply operation on a list of tensor in single multi-tensor kernel by group
+    It is a breaking change over last version, as API changes and it no longer fuse grad norm and loss scaling.
+
     It has been proposed in `Adam: A Method for Stochastic Optimization`_.
 
     Arguments:
@@ -21,10 +26,8 @@ class FusedAdam(torch.optim.Optimizer):
         amsgrad (boolean, optional): whether to use the AMSGrad variant of this
             algorithm from the paper `On the Convergence of Adam and Beyond`_
             (default: False) NOT SUPPORTED in FusedAdam!
-        eps_inside_sqrt (boolean, optional): in the 'update parameters' step,
-            adds eps to the bias-corrected second moment estimate before
-            evaluating square root instead of adding it to the square root of
-            second moment estimate as in the original paper. (default: False)
+        adam_w_mode (boolean, optional): Apply L2 regularization or weight decay
+            True for decoupled weight decay(also known as AdamW) (default: True)
 
     .. _Adam\: A Method for Stochastic Optimization:
         https://arxiv.org/abs/1412.6980
@@ -32,116 +35,75 @@ class FusedAdam(torch.optim.Optimizer):
         https://openreview.net/forum?id=ryQu7f-RZ
     """
 
-    def __init__(self, params,
-                 lr=1e-3, bias_correction = True,
-                 betas=(0.9, 0.999), eps=1e-8, eps_inside_sqrt = False,
-                 weight_decay=0., max_grad_norm=0., amsgrad=False):
-        global fused_adam_cuda
-        fused_adam_cuda = importlib.import_module("fused_adam_cuda")
+    def __init__(self, params, lr=1e-3, bias_correction=True,
+                 betas=(0.9, 0.999), eps=1e-8, adam_w_mode=True,
+                 weight_decay=0., amsgrad=False):
 
         if amsgrad:
             raise RuntimeError('FusedAdam does not support the AMSGrad variant.')
         defaults = dict(lr=lr, bias_correction=bias_correction,
-                        betas=betas, eps=eps, weight_decay=weight_decay,
-                        max_grad_norm=max_grad_norm)
+                        betas=betas, eps=eps, weight_decay=weight_decay)
         super(FusedAdam, self).__init__(params, defaults)
-        self.eps_mode = 0 if  eps_inside_sqrt else 1
+        self.adam_w_mode = 1 if adam_w_mode else 0
+        self.dummy_overflow_buf = torch.cuda.IntTensor([0])
 
-    def step(self, closure=None, grads=None, output_params=None, scale=1., grad_norms=None):
+    def step(self, closure=None, grads=None, output_params=None, scale=None, grad_norms=None):
         """Performs a single optimization step.
 
         Arguments:
             closure (callable, optional): A closure that reevaluates the model
                 and returns the loss.
-            grads (list of tensors, optional): weight gradient to use for the
-                optimizer update. If gradients have type torch.half, parameters
-                are expected to be in type torch.float. (default: None)
-            output params (list of tensors, optional): A reduced precision copy
-                of the updated weights written out in addition to the regular
-                updated weights. Have to be of same type as gradients. (default: None)
-            scale (float, optional): factor to divide gradient tensor values
-                by before applying to weights. (default: 1)
         """
+        if any(p is not None for p in [grads, output_params, scale, grad_norms]):
+            raise RuntimeError('FusedAdam has been updated, please use with AMP for mixed precision.')
         loss = None
         if closure is not None:
             loss = closure()
 
-        if grads is None:
-            grads_group = [None]*len(self.param_groups)
-        # backward compatibility
-        # assuming a list/generator of parameter means single group
-        elif isinstance(grads, types.GeneratorType):
-            grads_group = [grads]
-        elif type(grads[0])!=list:
-            grads_group = [grads]
-        else:
-            grads_group = grads
-
-        if output_params is None:
-            output_params_group = [None]*len(self.param_groups)
-        elif isinstance(output_params, types.GeneratorType):
-            output_params_group = [output_params]
-        elif type(output_params[0])!=list:
-            output_params_group = [output_params]
-        else:
-            output_params_group = output_params
-
-        if grad_norms is None:
-            grad_norms = [None]*len(self.param_groups)
-
-        for group, grads_this_group, output_params_this_group, grad_norm in zip(self.param_groups, grads_group, output_params_group, grad_norms):
-            if grads_this_group is None:
-               grads_this_group = [None]*len(group['params'])
-            if output_params_this_group is None:
-               output_params_this_group = [None]*len(group['params'])
-
-            # compute combined scale factor for this group
-            combined_scale = scale
-            if group['max_grad_norm'] > 0:
-                # norm is in fact norm*scale
-                clip = ((grad_norm / scale) + 1e-6) / group['max_grad_norm']
-                if clip > 1:
-                    combined_scale = clip * scale
-
+        for group in self.param_groups:
             bias_correction = 1 if group['bias_correction'] else 0
+            beta1, beta2 = group['betas']
+
+            # assume same step across group now to simplify things
+            # per parameter step can be easily support by making it tensor, or pass list into kernel
+            if 'step' in group:
+                group['step'] += 1
+            else:
+                group['step'] = 1
+
+            # create lists for multi-tensor apply
+            p_list, g_list, m1_list, m2_list = [], [], [], []
 
-            for p, grad, output_param in zip(group['params'], grads_this_group, output_params_this_group):
-                #note: p.grad should not ever be set for correct operation of mixed precision optimizer that sometimes sends None gradients
-                if p.grad is None and grad is None:
+            for p in group['params']:
+                if p.grad is None:
                     continue
-                if grad is None:
-                    grad = p.grad.data
-                if grad.is_sparse:
+                if p.grad.data.is_sparse:
                     raise RuntimeError('FusedAdam does not support sparse gradients, please consider SparseAdam instead')
 
                 state = self.state[p]
-
                 # State initialization
                 if len(state) == 0:
-                    state['step'] = 0
                     # Exponential moving average of gradient values
                     state['exp_avg'] = torch.zeros_like(p.data)
                     # Exponential moving average of squared gradient values
                     state['exp_avg_sq'] = torch.zeros_like(p.data)
 
-                exp_avg, exp_avg_sq = state['exp_avg'], state['exp_avg_sq']
-                beta1, beta2 = group['betas']
-
-                state['step'] += 1
-
-                out_p = torch.tensor([], dtype = torch.float) if output_param is None else output_param
-                fused_adam_cuda.adam(p.data,
-                                     out_p,
-                                     exp_avg,
-                                     exp_avg_sq,
-                                     grad,
-                                     group['lr'],
-                                     beta1,
-                                     beta2,
-                                     group['eps'],
-                                     combined_scale,
-                                     state['step'],
-                                     self.eps_mode,
-                                     bias_correction,
-                                     group['weight_decay'])
+                p_list.append(p.data)
+                g_list.append(p.grad.data)
+                m1_list.append(state['exp_avg'])
+                m2_list.append(state['exp_avg_sq'])
+
+            multi_tensor_applier(multi_tensor_adam,
+                                 self.dummy_overflow_buf,
+                                 [g_list, p_list, m1_list, m2_list],
+                                 group['lr'],
+                                 beta1,
+                                 beta2,
+                                 group['eps'],
+                                 group['step'],
+                                 self.adam_w_mode,
+                                 bias_correction,
+                                 group['weight_decay'])
+
+
         return loss

apex/optimizers/fused_lamb.py

+import torch
+from apex.multi_tensor_apply import multi_tensor_applier
+
+class FusedLAMB(torch.optim.Optimizer):
+
+    """Implements LAMB algorithm. Currently GPU-only.  Requires Apex to be installed via
+    ``python setup.py install --cuda_ext --cpp_ext``.
+
+    It has been proposed in `Large Batch Optimization for Deep Learning: Training BERT in 76 minutes`_.
+
+    Arguments:
+        params (iterable): iterable of parameters to optimize or dicts defining
+            parameter groups.
+        lr (float, optional): learning rate. (default: 1e-3)
+        betas (Tuple[float, float], optional): coefficients used for computing
+            running averages of gradient and its norm. (default: (0.9, 0.999))
+        eps (float, optional): term added to the denominator to improve
+            numerical stability. (default: 1e-8)
+        weight_decay (float, optional): weight decay (L2 penalty) (default: 0)
+        amsgrad (boolean, optional): whether to use the AMSGrad variant of this
+            algorithm from the paper `On the Convergence of Adam and Beyond`_
+            NOT SUPPORTED now! (default: False)
+        adam_w_mode (boolean, optional): Apply L2 regularization or weight decay
+            True for decoupled weight decay(also known as AdamW) (default: True)
+        grad_averaging (bool, optional): whether apply (1-beta2) to grad when
+            calculating running averages of gradient. (default: True)
+        set_grad_none (bool, optional): whether set grad to None when zero_grad()
+            method is called. (default: True)
+        max_grad_norm (float, optional): value used to clip global grad norm
+            (default: 1.0)
+
+    """
+
+    def __init__(self, params, lr=1e-3, bias_correction=True,
+                 betas=(0.9, 0.999), eps=1e-6, weight_decay=0.01,
+                 amsgrad=False, adam_w_mode=True,
+                 grad_averaging=True, set_grad_none=True,
+                 max_grad_norm=1.0):
+        if amsgrad:
+            raise RuntimeError('FusedLAMB does not support the AMSGrad variant.')
+        defaults = dict(lr=lr, bias_correction=bias_correction,
+                        betas=betas, eps=eps, weight_decay=weight_decay,
+                        grad_averaging=grad_averaging,
+                        max_grad_norm=max_grad_norm)
+        super(FusedLAMB, self).__init__(params, defaults)
+        if multi_tensor_applier.available:
+            import amp_C
+            # Skip buffer
+            self._dummy_overflow_buf = torch.cuda.IntTensor([0])
+            self.multi_tensor_lamb = amp_C.multi_tensor_lamb
+        else:
+            raise RuntimeError('apex.optimizers.FusedLAMB requires cuda extensions')
+
+        self.adam_w_mode = 1 if adam_w_mode else 0
+        self.set_grad_none = set_grad_none
+
+    def zero_grad(self):
+        if self.set_grad_none:
+            for group in self.param_groups:
+                for p in group['params']:
+                    p.grad = None
+        else:
+            super(FusedLAMB, self).zero_grad()
+
+    def step(self, closure=None):
+        """Performs a single optimization step.
+
+        Arguments:
+            closure (callable, optional): A closure that reevaluates the model
+                and returns the loss.
+        """
+        loss = None
+        if closure is not None:
+            loss = closure()
+
+        for group in self.param_groups:
+            bias_correction = 1 if group['bias_correction'] else 0
+            beta1, beta2 = group['betas']
+            grad_averaging = 1 if group['grad_averaging'] else 0
+
+            # assume same step across group now to simplify things
+            # per parameter step can be easily support by making it tensor, or pass list into kernel
+            if 'step' in group:
+                group['step'] += 1
+            else:
+                group['step'] = 1
+
+            # create lists for multi-tensor apply
+            g_16, p_16, m_16, v_16 = [], [], [], []
+            g_32, p_32, m_32, v_32 = [], [], [], []
+
+            for p in group['params']:
+                if p.grad is None:
+                    continue
+                if p.grad.data.is_sparse:
+                    raise RuntimeError('FusedLAMB does not support sparse gradients, please consider SparseAdam instead')
+
+                state = self.state[p]
+                # State initialization
+                if len(state) == 0:
+                    # Exponential moving average of gradient values
+                    state['exp_avg'] = torch.zeros_like(p.data)
+                    # Exponential moving average of gradient values
+                    state['exp_avg_sq'] = torch.zeros_like(p.data)
+
+                if p.dtype == torch.float16:
+                    g_16.append(p.grad.data)
+                    p_16.append(p.data)
+                    m_16.append(state['exp_avg'])
+                    v_16.append(state['exp_avg_sq'])
+                elif p.dtype == torch.float32:
+                    g_32.append(p.grad.data)
+                    p_32.append(p.data)
+                    m_32.append(state['exp_avg'])
+                    v_32.append(state['exp_avg_sq'])
+                else:
+                    raise RuntimeError('FusedLAMB only support fp16 and fp32.')
+
+            if(len(g_16) > 0):
+                multi_tensor_applier(self.multi_tensor_lamb,
+                                     self._dummy_overflow_buf,
+                                     [g_16, p_16, m_16, v_16],
+                                     group['lr'],
+                                     beta1,
+                                     beta2,
+                                     group['eps'],
+                                     group['step'],
+                                     bias_correction,
+                                     group['weight_decay'],
+                                     grad_averaging,
+                                     self.adam_w_mode,
+                                     group['max_grad_norm'])
+            if(len(g_32) > 0):
+                multi_tensor_applier(self.multi_tensor_lamb,
+                                     self._dummy_overflow_buf,
+                                     [g_32, p_32, m_32, v_32],
+                                     group['lr'],
+                                     beta1,
+                                     beta2,
+                                     group['eps'],
+                                     group['step'],
+                                     bias_correction,
+                                     group['weight_decay'],
+                                     grad_averaging,
+                                     self.adam_w_mode,
+                                     group['max_grad_norm'])
+
+        return loss

apex/optimizers/fused_novograd.py

+import torch
+from apex.multi_tensor_apply import multi_tensor_applier
+
+class FusedNovoGrad(torch.optim.Optimizer):
+
+    """Implements NovoGrad algorithm. Currently GPU-only.  Requires Apex to be installed via
+    ``python setup.py install --cuda_ext --cpp_ext``.
+
+    It has been proposed in `Jasper: An End-to-End Convolutional Neural Acoustic Model`_.
+    More info: https://nvidia.github.io/OpenSeq2Seq/html/optimizers.html#novograd
+
+    Arguments:
+        params (iterable): iterable of parameters to optimize or dicts defining
+            parameter groups.
+        lr (float, optional): learning rate. (default: 1e-3)
+        betas (Tuple[float, float], optional): coefficients used for computing
+            running averages of gradient and its norm. (default: (0.9, 0.999))
+        eps (float, optional): term added to the denominator to improve
+            numerical stability. (default: 1e-8)
+        weight_decay (float, optional): weight decay (L2 penalty) (default: 0)
+        amsgrad (boolean, optional): whether to use the AMSGrad variant of this
+            algorithm from the paper `On the Convergence of Adam and Beyond`_
+            NOT SUPPORTED now! (default: False)
+        reg_inside_moment (bool, optional): whether do regularization (norm and L2)
+            in momentum calculation. True for include, False for not include and
+            only do it on update term. (default: False)
+        grad_averaging (bool, optional): whether apply (1-beta2) to grad when
+            calculating running averages of gradient. (default: True)
+        norm_type (int, optional): which norm to calculate for each layer.
+            2 for L2 norm, and 0 for infinite norm. These 2 are only supported
+            type now. (default: 2)
+        init_zero (bool, optional): whether init norm with 0 (start averaging on
+            1st step) or first step norm (start averaging on 2nd step). True for
+            init with 0. (default: False)
+        set_grad_none (bool, optional): whether set grad to None when zero_grad()
+            method is called. (default: True)
+
+    .. _Jasper\: An End-to-End Convolutional Neural Acoustic Mode:
+        https://arxiv.org/abs/1904.03288
+    .. _On the Convergence of Adam and Beyond:
+        https://openreview.net/forum?id=ryQu7f-RZ
+    """
+
+    def __init__(self, params, lr=1e-3, bias_correction=True,
+                 betas=(0.9, 0.999), eps=1e-8, weight_decay=0.,
+                 amsgrad=False, reg_inside_moment=False,
+                 grad_averaging=True, norm_type=2, init_zero=False,
+                 set_grad_none=True):
+        if amsgrad:
+            raise RuntimeError('FusedNovoGrad does not support the AMSGrad variant.')
+        defaults = dict(lr=lr, bias_correction=bias_correction,
+                        betas=betas, eps=eps, weight_decay=weight_decay,
+                        grad_averaging=grad_averaging, norm_type=norm_type,
+                        init_zero=init_zero)
+        super(FusedNovoGrad, self).__init__(params, defaults)
+        if multi_tensor_applier.available:
+            import amp_C
+            # Skip buffer
+            self._dummy_overflow_buf = torch.cuda.IntTensor([0])
+            self.multi_tensor_novograd = amp_C.multi_tensor_novograd
+        else:
+            raise RuntimeError('apex.optimizers.FusedNovoGrad requires cuda extensions')
+
+        self.moment_mode = 0 if reg_inside_moment else 1
+        self.set_grad_none = set_grad_none
+
+    def zero_grad(self):
+        if self.set_grad_none:
+            for group in self.param_groups:
+                for p in group['params']:
+                    p.grad = None
+        else:
+            super(FusedNovoGrad, self).zero_grad()
+
+    def step(self, closure=None):
+        """Performs a single optimization step.
+
+        Arguments:
+            closure (callable, optional): A closure that reevaluates the model
+                and returns the loss.
+        """
+        loss = None
+        if closure is not None:
+            loss = closure()
+
+        for group in self.param_groups:
+            bias_correction = 1 if group['bias_correction'] else 0
+            beta1, beta2 = group['betas']
+            grad_averaging = 1 if group['grad_averaging'] else 0
+
+            # assume same step across group now to simplify things
+            # per parameter step can be easily support by making it tensor, or pass list into kernel
+            if 'step' in group:
+                group['step'] += 1
+            else:
+                group['step'] = 1
+
+            # create lists for multi-tensor apply
+            g_16, p_16, m_16 = [], [], []
+            g_32, p_32, m_32 = [], [], []
+
+            for p in group['params']:
+                if p.grad is None:
+                    continue
+                if p.grad.data.is_sparse:
+                    raise RuntimeError('FusedNovoGrad does not support sparse gradients, please consider SparseAdam instead')
+
+                state = self.state[p]
+                # State initialization
+                if len(state) == 0:
+                    # Exponential moving average of gradient values
+                    state['exp_avg'] = torch.zeros_like(p.data)
+
+                if p.dtype == torch.float16:
+                    g_16.append(p.grad.data)
+                    p_16.append(p.data)
+                    m_16.append(state['exp_avg'])
+                elif p.dtype == torch.float32:
+                    g_32.append(p.grad.data)
+                    p_32.append(p.data)
+                    m_32.append(state['exp_avg'])
+                else:
+                    raise RuntimeError('FusedNovoGrad only support fp16 and fp32.')
+
+            # we store per weight norm as one tensor for one group/precision combination
+            # different from optim.Adam, we store norm here(not ^2) so we can unify calculation for norm types
+            if 'exp_avg_sq' not in group:
+                group['exp_avg_sq'] = [None, None]
+                if group['init_zero']:
+                    group['exp_avg_sq'][0] = torch.cuda.FloatTensor(len(g_16)).contiguous().fill_(0)
+                    group['exp_avg_sq'][1] = torch.cuda.FloatTensor(len(g_32)).contiguous().fill_(0)
+                else: # init with first step norm, so first blend have no effect
+                    if group['norm_type'] == 0:
+                        v_16 = [torch.max(torch.abs(g.to(torch.float32))).item() for g in g_16]
+                        v_32 = [torch.max(torch.abs(g)).item() for g in g_32]
+                    elif group['norm_type'] == 2:
+                        v_16 = [torch.sum(torch.pow(g.to(torch.float32), 2)).sqrt().item() for g in g_16]
+                        v_32 = [torch.sum(torch.pow(g, 2)).sqrt().item() for g in g_32]
+                    else:
+                        raise RuntimeError('FusedNovoGrad only support l2/inf norm now.')
+                    group['exp_avg_sq'][0] = torch.cuda.FloatTensor(v_16)
+                    group['exp_avg_sq'][1] = torch.cuda.FloatTensor(v_32)
+            else:
+                assert(len(g_16) == group['exp_avg_sq'][0].numel())
+                assert(len(g_32) == group['exp_avg_sq'][1].numel())
+
+            if(len(g_16) > 0):
+                multi_tensor_applier(self.multi_tensor_novograd,
+                                     self._dummy_overflow_buf,
+                                     [g_16, p_16, m_16],
+                                     group['exp_avg_sq'][0],
+                                     group['lr'],
+                                     beta1,
+                                     beta2,
+                                     group['eps'],
+                                     group['step'],
+                                     bias_correction,
+                                     group['weight_decay'],
+                                     grad_averaging,
+                                     self.moment_mode,
+                                     group['norm_type'])
+            if(len(g_32) > 0):
+                multi_tensor_applier(self.multi_tensor_novograd,
+                                     self._dummy_overflow_buf,
+                                     [g_32, p_32, m_32],
+                                     group['exp_avg_sq'][1],
+                                     group['lr'],
+                                     beta1,
+                                     beta2,
+                                     group['eps'],
+                                     group['step'],
+                                     bias_correction,
+                                     group['weight_decay'],
+                                     grad_averaging,
+                                     self.moment_mode,
+                                     group['norm_type'])
+
+
+        return loss

apex/optimizers/fused_sgd.py

+import torch
+from torch.optim.optimizer import Optimizer, required
+
+from apex.multi_tensor_apply import multi_tensor_applier
+
+class FusedSGD(Optimizer):
+    r"""Implements stochastic gradient descent (optionally with momentum).
+
+    Nesterov momentum is based on the formula from
+    `On the importance of initialization and momentum in deep learning`__.
+
+    Args:
+        params (iterable): iterable of parameters to optimize or dicts defining
+            parameter groups
+        lr (float): learning rate
+        momentum (float, optional): momentum factor (default: 0)
+        weight_decay (float, optional): weight decay (L2 penalty) (default: 0)
+        dampening (float, optional): dampening for momentum (default: 0)
+        nesterov (bool, optional): enables Nesterov momentum (default: False)
+
+    Example:
+        >>> optimizer = torch.optim.SGD(model.parameters(), lr=0.1, momentum=0.9)
+        >>> optimizer.zero_grad()
+        >>> loss_fn(model(input), target).backward()
+        >>> optimizer.step()
+
+    __ http://www.cs.toronto.edu/%7Ehinton/absps/momentum.pdf
+
+    .. note::
+        The implementation of SGD with Momentum/Nesterov subtly differs from
+        Sutskever et. al. and implementations in some other frameworks.
+
+        Considering the specific case of Momentum, the update can be written as
+
+        .. math::
+                  v = \rho * v + g \\
+                  p = p - lr * v
+
+        where p, g, v and :math:`\rho` denote the parameters, gradient,
+        velocity, and momentum respectively.
+
+        This is in contrast to Sutskever et. al. and
+        other frameworks which employ an update of the form
+
+        .. math::
+             v = \rho * v + lr * g \\
+             p = p - v
+
+        The Nesterov version is analogously modified.
+    """
+
+    def __init__(self, params, lr=required, momentum=0, dampening=0,
+                 weight_decay=0, nesterov=False,
+                 wd_after_momentum=False,
+                 materialize_master_grads=True):
+        if lr is not required and lr < 0.0:
+            raise ValueError("Invalid learning rate: {}".format(lr))
+        if momentum < 0.0:
+            raise ValueError("Invalid momentum value: {}".format(momentum))
+        if weight_decay < 0.0:
+            raise ValueError("Invalid weight_decay value: {}".format(weight_decay))
+
+        defaults = dict(lr=lr, momentum=momentum, dampening=dampening,
+                        weight_decay=weight_decay, nesterov=nesterov)
+        if nesterov and (momentum <= 0 or dampening != 0):
+            raise ValueError("Nesterov momentum requires a momentum and zero dampening")
+        super(FusedSGD, self).__init__(params, defaults)
+
+        self.wd_after_momentum = wd_after_momentum
+        self.materialize_master_grads = materialize_master_grads
+        self.most_recent_scale = 1.0
+        self.scale_set_by_backward = False
+
+        if multi_tensor_applier.available:
+            import amp_C
+            # Skip buffer
+            self._dummy_overflow_buf = torch.cuda.IntTensor([0])
+            self.multi_tensor_sgd = amp_C.multi_tensor_sgd
+        else:
+            raise RuntimeError('apex.optimizers.FusedSGD requires cuda extensions')
+
+    def __setstate__(self, state):
+        super(FusedSGD, self).__setstate__(state)
+        for group in self.param_groups:
+            group.setdefault('nesterov', False)
+
+    def get_momentums(self, params):
+        momentums = []
+        first_run = True
+        for p in params:
+            param_state = self.state[p]
+            # torch.optim.SGD initializes momentum in the main loop, we have
+            # to do it here, and track whether or not we've done so, so that
+            # momentum application can be skipped in the main kernel.
+            if 'momentum_buffer' not in param_state:
+                first_run = True
+                buf = param_state['momentum_buffer'] = torch.zeros_like(p.data)
+                momentums.append(buf)
+            else:
+                first_run = False
+                momentums.append(param_state['momentum_buffer'])
+        return momentums, first_run
+
+    def step(self, closure=None):
+        """Performs a single optimization step.
+
+        Arguments:
+            closure (callable, optional): A closure that reevaluates the model
+                and returns the loss.
+        """
+        loss = None
+        if closure is not None:
+            loss = closure()
+
+        explicit_master_params = (hasattr(self, "_amp_stash") and
+                                  hasattr(self._amp_stash, "fp32_from_fp16_groups"))
+
+        for gid, group in enumerate(self.param_groups):
+            weight_decay = group['weight_decay']
+            momentum = group['momentum']
+            dampening = group['dampening']
+            nesterov = group['nesterov']
+
+
+            # For each group, there are 3 possible combinations we need to consider:
+            # grad_type, param_to_update_type, momentum_type, requires_fp16_model_copy
+            # 1. fp16, fp16, fp16, No
+            # 2. fp32, fp32, fp32, No
+            # 3. fp16, fp32, fp32, Yes
+
+            first_runs = [True, True]
+
+            # I think a bit of code divergence in exchange for naming clarity is worthwhile
+            if explicit_master_params:
+                stash = self._amp_stash
+
+                fp32_params = [p for p in stash.fp32_from_fp32_groups[gid] if p.grad is not None]
+                fp32_grads = [p.grad for p in stash.fp32_from_fp32_groups[gid] if p.grad is not None]
+                fp32_momentums, first_runs[1] = self.get_momentums(fp32_params)
+
+                if self.materialize_master_grads:
+                    fp16_model_params = [p for i, p in enumerate(
+                        stash.fp16_groups[gid]) if stash.fp32_from_fp16_groups[gid][i].grad is not None]
+                    fp32_from_fp16_grads = [p.grad for p in stash.fp32_from_fp16_groups[gid] if p.grad is not None]
+                    fp32_from_fp16_params = [p for p in stash.fp32_from_fp16_groups[gid] if p.grad is not None]
+                    fp32_from_fp16_momentums, first_runs[0] = self.get_momentums(fp32_from_fp16_params)
+
+                    fp16_set = [fp32_from_fp16_grads, fp32_from_fp16_params,
+                                fp32_from_fp16_momentums, fp16_model_params]
+                else:
+                    fp16_model_params = [p for p in stash.fp16_groups[gid] if p.grad is not None]
+                    fp16_model_grads = [p.grad for p in stash.fp16_groups[gid] if p.grad is not None]
+                    fp32_from_fp16_params = [p for i, p in enumerate(
+                        stash.fp32_from_fp16_groups[gid]) if stash.fp16_groups[gid][i].grad is not None]
+                    fp32_from_fp16_momentums, first_runs[0] = self.get_momentums(fp32_from_fp16_params)
+
+                    fp16_set = [fp16_model_grads, fp32_from_fp16_params,
+                                fp32_from_fp16_momentums, fp16_model_params]
+
+                launch_sets= [fp16_set, [fp32_grads, fp32_params, fp32_momentums]]
+            else:
+                fp16_params = [p for p in group['params'] if (p.dtype == torch.float16 and p.grad is not None)]
+                fp16_grads = [p.grad for p in group['params'] if (p.dtype == torch.float16 and p.grad is not None)]
+                fp16_momentums, first_runs[0] = self.get_momentums(fp16_params)
+
+                fp32_params = [p for p in group['params'] if (p.dtype == torch.float32 and p.grad is not None)]
+                fp32_grads = [p.grad for p in group['params'] if (p.dtype == torch.float32 and p.grad is not None)]
+                fp32_momentums, first_runs[1] = self.get_momentums(fp32_params)
+
+                launch_sets = [[fp16_grads, fp16_params, fp16_momentums],
+                               [fp32_grads, fp32_params, fp32_momentums]]
+
+            for s, (launch_set, first_run) in enumerate(zip(launch_sets, first_runs)):
+                assert len(launch_set[0]) == len(launch_set[1])
+                assert len(launch_set[0]) == len(launch_set[2])
+                if len(launch_set[0]) > 0:
+                    multi_tensor_applier(
+                        self.multi_tensor_sgd,
+                        self._dummy_overflow_buf,
+                        launch_set,
+                        weight_decay,
+                        momentum,
+                        dampening,
+                        group['lr'],
+                        nesterov,
+                        first_run,
+                        self.wd_after_momentum,
+                        1.0/self.most_recent_scale)
+
+        self.most_recent_scale = 1.0
+        self.scale_set_by_backward = False
+
+        return loss

apex/parallel/optimized_sync_batchnorm.py

@@ -55,10 +55,11 @@ class SyncBatchNorm(_BatchNorm):
         >>> inp = torch.randn(10, 14, 14, 100).cuda()
     """
 
-    def __init__(self, num_features, eps=1e-5, momentum=0.1, affine=True, track_running_stats=True, process_group=None, channel_last=False):
+    def __init__(self, num_features, eps=1e-5, momentum=0.1, affine=True, track_running_stats=True, process_group=None, channel_last=False, fuse_relu=False):
         super(SyncBatchNorm, self).__init__(num_features, eps=eps, momentum=momentum, affine=affine, track_running_stats=track_running_stats)
         self.process_group = process_group
         self.channel_last = channel_last
+        self.fuse_relu = fuse_relu
 
     def _specify_process_group(self, process_group):
         self.process_group = process_group
@@ -66,11 +67,11 @@ class SyncBatchNorm(_BatchNorm):
     def _specify_channel_last(self, channel_last):
         self.channel_last = channel_last
 
-    def forward(self, input):
+    def forward(self, input, z = None):
         # if input.dim() == 2, we switch to channel_last for efficient memory accessing
         channel_last = self.channel_last if input.dim() != 2 else True
 
-        if not self.training and self.track_running_stats and not channel_last:
+        if not self.training and self.track_running_stats and not self.channel_last and not self.fuse_relu and z == None:
             # fall back to pytorch implementation for inference
             return F.batch_norm(input, self.running_mean, self.running_var, self.weight, self.bias, False, 0.0, self.eps)
         else:
@@ -81,4 +82,4 @@ class SyncBatchNorm(_BatchNorm):
                     exponential_average_factor = 1.0 / float(self.num_batches_tracked)
                 else:
                     exponential_average_factor = self.momentum
-            return SyncBatchnormFunction.apply(input, self.weight, self.bias, self.running_mean, self.running_var, self.eps, self.training or not self.track_running_stats, exponential_average_factor, self.process_group, channel_last)
+            return SyncBatchnormFunction.apply(input, z, self.weight, self.bias, self.running_mean, self.running_var, self.eps, self.training or not self.track_running_stats, exponential_average_factor, self.process_group, self.channel_last, self.fuse_relu)

apex/parallel/optimized_sync_batchnorm_kernel.py

@@ -7,7 +7,7 @@ from apex.parallel import ReduceOp
 class SyncBatchnormFunction(Function):
 
     @staticmethod
-    def forward(ctx, input, weight, bias, running_mean, running_variance, eps, track_running_stats = True, momentum = 1.0, process_group = None, channel_last = False):
+    def forward(ctx, input, z, weight, bias, running_mean, running_variance, eps, track_running_stats = True, momentum = 1.0, process_group = None, channel_last = False, fuse_relu = False):
         torch.cuda.nvtx.range_push("sync_BN_fw")
         input = input.contiguous()
         world_size = 0
@@ -53,13 +53,14 @@ class SyncBatchnormFunction(Function):
             mean = running_mean.data
             inv_std = 1.0 / torch.sqrt(running_variance.data + eps)
 
-        ctx.save_for_backward(input, weight, mean, inv_std)
+        ctx.save_for_backward(input, weight, mean, inv_std, z, bias)
         ctx.process_group = process_group
         ctx.channel_last = channel_last
         ctx.world_size = world_size
+        ctx.fuse_relu = fuse_relu
 
         if channel_last:
-            out = syncbn.batchnorm_forward_c_last(input, mean, inv_std, weight, bias)
+            out = syncbn.batchnorm_forward_c_last(input, z, mean, inv_std, weight, bias, fuse_relu)
         else:
             out = syncbn.batchnorm_forward(input, mean, inv_std, weight, bias)
 
@@ -73,11 +74,17 @@ class SyncBatchnormFunction(Function):
         # mini batch mean & var are calculated by forward path.
         # mu = 1./N*np.sum(h, axis = 0)
         # var = 1./N*np.sum((h-mu)**2, axis = 0)
-        saved_input, weight, mean, inv_std = ctx.saved_tensors
+        saved_input, weight, mean, inv_std, z, bias = ctx.saved_tensors
         process_group = ctx.process_group
         channel_last = ctx.channel_last
         world_size = ctx.world_size
-        grad_input = grad_weight = grad_bias = None
+        fuse_relu = ctx.fuse_relu
+        grad_input = grad_z = grad_weight = grad_bias = None
+
+        if fuse_relu:
+            grad_output = syncbn.relu_bw_c_last(grad_output, saved_input, z, mean, inv_std, weight, bias)
+        if isinstance(z, torch.Tensor) and ctx.needs_input_grad[1]:
+            grad_z = grad_output.clone()
 
         # TODO(jie): why do I have to clone here? life time of grad_output?
         if channel_last:
@@ -100,11 +107,11 @@ class SyncBatchnormFunction(Function):
             else:
                 grad_input = syncbn.batchnorm_backward(grad_output, saved_input, mean, inv_std, weight, mean_dy, mean_dy_xmu)
 
-        if weight is None or not ctx.needs_input_grad[1]:
+        if weight is None or not ctx.needs_input_grad[2]:
             grad_weight = None
 
-        if weight is None or not ctx.needs_input_grad[2]:
+        if weight is None or not ctx.needs_input_grad[3]:
             grad_bias = None
 
         torch.cuda.nvtx.range_pop()
-        return grad_input, grad_weight, grad_bias, None, None, None, None, None, None, None
+        return grad_input, grad_z, grad_weight, grad_bias, None, None, None, None, None, None, None, None

csrc/amp_C_frontend.cpp

@@ -6,6 +6,19 @@ void multi_tensor_scale_cuda(
   std::vector<std::vector<at::Tensor>> tensor_lists,
   float scale);
 
+void multi_tensor_sgd_cuda(
+  int chunk_size,
+  at::Tensor noop_flag,
+  std::vector<std::vector<at::Tensor>> tensor_lists,
+  float wd,
+  float momentum,
+  float dampening,
+  float lr,
+  bool nesterov,
+  bool first_run,
+  bool wd_after_momentum,
+  float scale);
+
 void multi_tensor_axpby_cuda(
   int chunk_size,
   at::Tensor noop_flag,
@@ -21,28 +34,74 @@ std::tuple<at::Tensor, at::Tensor> multi_tensor_l2norm_cuda(
   at::optional<bool> per_tensor_python);
 
 void multi_tensor_lamb_stage1_cuda(
+    int chunk_size,
+    at::Tensor noop_flag,
+    std::vector<std::vector<at::Tensor>> tensor_lists,
+    at::Tensor per_tensor_decay,
+    const int step,
+    const float beta1,
+    const float beta2,
+    const float epsilon,
+    const float global_grad_norm,
+    const float max_global_grad_norm);
+
+void multi_tensor_lamb_stage2_cuda(
+    int chunk_size,
+    at::Tensor noop_flag,
+    std::vector<std::vector<at::Tensor>> tensor_lists,
+    at::Tensor per_tensor_param_norm,
+    at::Tensor per_tensor_update_norm,
+    const float step_size);
+
+void multi_tensor_adam_cuda(
   int chunk_size,
   at::Tensor noop_flag,
   std::vector<std::vector<at::Tensor>> tensor_lists,
-  at::Tensor per_tensor_decay,
+  const float lr,
+  const float beta1,
+  const float beta2,
+  const float epsilon,
   const int step,
+  const int mode,
+  const int bias_correction,
+  const float weight_decay);
+
+void multi_tensor_novograd_cuda(
+  int chunk_size,
+  at::Tensor noop_flag,
+  std::vector<std::vector<at::Tensor>> tensor_lists,
+  at::Tensor grad_norms,
+  const float lr,
   const float beta1,
   const float beta2,
   const float epsilon,
-  const float global_grad_norm,
-  const float max_global_grad_norm);
+  const int step,
+  const int bias_correction,
+  const float weight_decay,
+  const int grad_averaging,
+  const int mode,
+  const int norm_type);
 
-void multi_tensor_lamb_stage2_cuda(
+void multi_tensor_lamb_cuda(
   int chunk_size,
   at::Tensor noop_flag,
   std::vector<std::vector<at::Tensor>> tensor_lists,
-  at::Tensor per_tensor_param_norm,
-  at::Tensor per_tensor_update_norm,
-  const float step_size);
+  const float lr,
+  const float beta1,
+  const float beta2,
+  const float epsilon,
+  const int step,
+  const int bias_correction,
+  const float weight_decay,
+  const int grad_averaging,
+  const int mode,
+  const float max_grad_norm);
 
 PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
   m.def("multi_tensor_scale", &multi_tensor_scale_cuda,
         "Fused overflow check + scale for a list of contiguous tensors");
+  m.def("multi_tensor_sgd", &multi_tensor_sgd_cuda,
+        "Fused SGD optimizer for list of contiguous tensors");
   m.def("multi_tensor_axpby", &multi_tensor_axpby_cuda,
         "out = a*x + b*y for a list of contiguous tensors");
   m.def("multi_tensor_l2norm", &multi_tensor_l2norm_cuda,
@@ -51,4 +110,10 @@ PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
         "Computes update part of LAMB optimizer");
   m.def("multi_tensor_lamb_stage2_cuda", &multi_tensor_lamb_stage2_cuda,
         "Completes application of gradient to parameters for LAMB optimizer");
+  m.def("multi_tensor_adam", &multi_tensor_adam_cuda,
+        "Compute and apply gradient update to parameters for Adam optimizer");
+  m.def("multi_tensor_novograd", &multi_tensor_novograd_cuda,
+        "Compute and apply gradient update to parameters for Adam optimizer");
+  m.def("multi_tensor_lamb", &multi_tensor_lamb_cuda,
+        "Computes and apply update for LAMB optimizer");
 }

csrc/fused_adam_cuda.cpp

@@ -3,6 +3,9 @@
 // CUDA forward declaration
 void fused_adam_cuda(at::Tensor & p, at::Tensor & p_copy, at::Tensor & m, at::Tensor & v, at::Tensor & g, float lr, float beta1, float beta2, float eps, float grad_scale, int step, int mode, int bias_correction, float decay);
 
+void fused_adam_cuda_mt(int chunk_size, at::Tensor noop_flag, std::vector<std::vector<at::Tensor>> tensor_lists, float lr, float beta1, float beta2, float eps, float grad_scale, int step, int mode, int bias_correction, float decay);
+
+
 #define CHECK_CUDA(x) AT_ASSERTM(x.type().is_cuda(), #x " must be a CUDA tensor")
 #define CHECK_CONTIGUOUS(x) AT_ASSERTM(x.is_contiguous(), #x " must be contiguous")
 #define CHECK_INPUT(x) CHECK_CUDA(x); CHECK_CONTIGUOUS(x)
@@ -25,4 +28,5 @@ void adam(at::Tensor & p, at::Tensor & p_copy, at::Tensor & m, at::Tensor & v, a
 
 PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {
         m.def("adam", &adam, "Adam optimized CUDA implementation.");
+        m.def("adam_mt", &fused_adam_cuda_mt, "Multi tensor Adam optimized CUDA implementation.");
 }