Merge pull request #92 from FDecaYed/deyuf/fused_adam

WIP: improve fused adam

apex/optimizers/__init__.py

 from .fused_adam import FusedAdam
+from .fp16_optimizer import FP16_Optimizer

apex/optimizers/csrc/fused_adam_cuda.cpp

 #include <torch/extension.h>
 
 // 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);
+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);
 
 #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)
 
 // C++ interface
-void adam(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) {
+void adam(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) {
         CHECK_INPUT(p)
         if (p_copy.numel() > 0) CHECK_INPUT(p_copy);
         CHECK_INPUT(m);
@@ -20,7 +20,7 @@ void adam(at::Tensor & p, at::Tensor & p_copy, at::Tensor & m, at::Tensor & v, a
         AT_ASSERTM(g.numel() == num_elem, "number of elements in g and p tensors should be equal");
         AT_ASSERTM(p_copy.numel() == num_elem || p_copy.numel() == 0, "number of elements in p_copy and p tensors should be equal, or p_copy should be empty");
 
-        fused_adam_cuda(p, p_copy, m, v, g, lr, beta1, beta2, eps, grad_scale, step, mode);
+        fused_adam_cuda(p, p_copy, m, v, g, lr, beta1, beta2, eps, grad_scale, step, mode, bias_correction, decay);
 }
 
 PYBIND11_MODULE(TORCH_EXTENSION_NAME, m) {

apex/optimizers/csrc/fused_adam_cuda_kernel.cu

@@ -28,7 +28,8 @@ __global__ void adam_cuda_kernel(
         const float grad_scale,
         const float step_size,
         const size_t tsize,
-        adamMode_t mode) {
+        adamMode_t mode,
+        const float decay) {
 
         //Assuming 2D grids and 2D blocks
         const int blockId = gridDim.x * blockIdx.y + blockIdx.x;
@@ -46,7 +47,8 @@ __global__ void adam_cuda_kernel(
                     denom = sqrtf(v[j] + eps);
                 else // Mode 1
                     denom = sqrtf(v[j]) + eps;
-                p[j] = p[j] - (step_size*m[j]/denom);
+                float update = (m[j]/denom) + (decay*p[j]);
+                p[j] = p[j] - (step_size*update);
                 if (p_copy != NULL) p_copy[j] = (GRAD_T) p[j];
         }
 }
@@ -63,7 +65,9 @@ void fused_adam_cuda(
         float eps,
         float grad_scale,
         int step,
-        int mode) {
+        int mode,
+        int bias_correction,
+        float decay) {
 
         //Get tensor size
         int tsize = p.numel();
@@ -72,9 +76,15 @@ void fused_adam_cuda(
         const dim3 blocks((tsize+threadsPerBlock-1)/threadsPerBlock);
         AT_ASSERTM(at::cuda::detail::canUse32BitIndexMath(p), "parameter tensor is too large to be indexed with int32");
         //Constants
+        float step_size = 0;
+        if (bias_correction == 1) {
             const float bias_correction1 = 1 - std::pow(beta1, step);
             const float bias_correction2 = 1 - std::pow(beta2, step);
-        const float step_size = lr * std::sqrt(bias_correction2)/bias_correction1;
+            step_size = lr * std::sqrt(bias_correction2)/bias_correction1;
+        }
+        else {
+            step_size = lr;
+        }
         cudaStream_t stream = at::cuda::getCurrentCUDAStream();
 
         if (g.type().scalarType() == at::ScalarType::Half) {
@@ -95,7 +105,8 @@ void fused_adam_cuda(
                         grad_scale,
                         step_size,
                         tsize,
-                        (adamMode_t) mode);
+                        (adamMode_t) mode,
+                        decay);
             }));
       } else {
             AT_DISPATCH_FLOATING_TYPES(g.type(), "adam_cuda_kernel", ([&] {
@@ -111,7 +122,8 @@ void fused_adam_cuda(
                         grad_scale,
                         step_size,
                         tsize,
-                        (adamMode_t) mode);
+                        (adamMode_t) mode,
+                        decay);
             }));
       }
       THCudaCheck(cudaGetLastError());

apex/optimizers/fp16_optimizer.py

+import torch
+from torch._utils import _flatten_dense_tensors, _unflatten_dense_tensors
+import ctypes
+
+lib = ctypes.cdll.LoadLibrary(None)
+lib.THCudaHalfTensor_normall.argtypes=[ctypes.c_void_p, ctypes.c_void_p]
+lib.THCudaHalfTensor_normall.restype = ctypes.c_float
+
+def fused_norm(input):
+    if input.type() == 'torch.cuda.HalfTensor':
+        # 16384 is half 2 if you stare at it long enough
+        return lib.THCudaHalfTensor_normall(torch.cuda._state_cdata,
+            input._cdata, 16384)
+    else:
+        return input.norm()
+
+class FP16_Optimizer(object):
+    """
+    :class:`FP16_Optimizer` A cutdown version of apex.fp16_utils.FP16_Optimizer.
+    Design to be used in the same way but support only fused optimizers in apex.
+    Refer to apex.fp16_utils documents for more information.
+
+    Example::
+
+        model = torch.nn.Linear(D_in, D_out).cuda().half()
+        optimizer = apex.optimizers.FusedAdam(model.parameters())
+        # Name the FP16_Optimizer instance to replace the existing optimizer
+        # (recommended but not required):
+        optimizer = FP16_Optimizer(optimizer, static_loss_scale = 128.0)
+        ...
+        # loss.backward() becomes:
+        optimizer.backward(loss)
+        ...
+
+    Example with dynamic loss scaling::
+
+        ...
+        optimizer = FP16_Optimizer(optimizer, dynamic_loss_scale=True)
+                                   # optional arg to control dynamic loss scaling behavior
+                                   # dynamic_loss_args={'scale_window' : 500})
+                                   # Usually, dynamic_loss_args is not necessary.
+    """
+
+    def __init__(self,
+                 init_optimizer,
+                 static_loss_scale=1.0,
+                 dynamic_loss_scale=False,
+                 dynamic_loss_args=None,
+                 verbose=True):
+
+        # 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
+
+        # differences from apex.fp16_utils:
+        # - assume all model params in fp16
+        # - assume all params requires grad
+        # - flat by groups, not keeping state. TODO: remove state explicitly?
+        # - master gard and unflat master weight never exist. TODO: a way to save out unflat master?
+        if not torch.cuda.is_available:
+            raise SystemError("Cannot use fp16 without CUDA.")
+        self.optimizer = init_optimizer
+
+        # param flattened by groups
+        self.fp16_groups = []
+        self.fp16_groups_flat = []
+        self.fp32_groups_flat = []
+
+        # loop to deal with groups
+        for i, param_group in enumerate(self.optimizer.param_groups):
+            # push this group to list before modify
+            self.fp16_groups.append(param_group['params'])
+            # init fp16 weight buffer, flattened
+            self.fp16_groups_flat.append(_flatten_dense_tensors([p.clone().detach() for p in self.fp16_groups[i]]))
+            # set model fp16 weight to slices of flattened buffer
+            updated_params = _unflatten_dense_tensors(self.fp16_groups_flat[i], self.fp16_groups[i])
+            for p,q in zip(self.fp16_groups[i], updated_params):
+                p.data = q.data
+            # init master weight, flattened
+            self.fp32_groups_flat.append(self.fp16_groups_flat[i].clone().float().detach())
+            # modify optimizer of have flat master weight
+            self.fp32_groups_flat[i].requires_grad = True # keep this in case internal optimizer uses it
+            param_group['params'] = [self.fp32_groups_flat[i]]
+
+        # we may have a way of fusing dynamic scale. Do not support for now
+        if dynamic_loss_scale:
+            if dynamic_loss_args is not None:
+                raise SystemError("Do not support dynamic loss scale args for now.")
+            self.dynamic_loss_scale = True
+            self.cur_scale = 2**32
+            self.cur_iter = 0
+            self.last_overflow_iter = -1
+            self.scale_factor = 2
+            self.scale_window = 1000
+        else:
+            self.dynamic_loss_scale = False
+            self.cur_iter = 0
+            self.cur_scale = static_loss_scale
+
+    def zero_grad(self, set_grads_to_None=True):
+        """
+        Zero FP16 parameter grads.
+        """
+        # FP32 grad should never exist.
+        # For speed, set model fp16 grad to None by default
+        for group in self.fp16_groups:
+            for p in group:
+                if set_grads_to_None:
+                    p.grad = None
+                else:
+                    if p.grad is not None:
+                        p.grad.detach_()
+                        p.grad.zero_()
+
+    def _compute_grad_norm(self, fp16_grads_flat, norm_type=2):
+        """
+        Compute fp16 grad norm for later clipping(fused with update).
+        Internal accumulated in fp32.
+        Also fused in NaN check. Possibly other reduction needed for grad.
+
+        Args:
+            fp16_grads_flat (tensor): fp16 grad flattened
+            norm_type (float or int): type of the used p-norm. Can be ``'inf'`` for
+                infinity norm.
+
+        Returns:
+            Total norm of the current fp16 gradients (viewed as a single vector).
+            Returns -1 if the most recently computed fp16 gradients overflowed
+        """
+        # TODO: currently using pre-1.0 api, and not most efficient with copy to cpu and sync
+        # only support 2-norm now
+        norm = float(fused_norm(fp16_grads_flat))
+        if norm == float('inf') or norm == -float('inf') or norm != norm:
+            return -1
+        else:
+            return norm
+
+    def step(self, closure=None):
+        """
+        Not supporting closure.
+        """
+        # First compute norm for all group so we know if there is overflow
+        grads_groups_flat = []
+        norm_groups = []
+        skip = False
+        for i, group in enumerate(self.fp16_groups):
+            grads_groups_flat.append(_flatten_dense_tensors([p.grad for p in group]))
+            norm_groups.append(self._compute_grad_norm(grads_groups_flat[i]))
+            if norm_groups[i] == -1: #TODO: early break
+                skip = True
+
+        if skip:
+            self._update_scale(skip)
+            return
+
+        # norm is in fact norm*cur_scale
+        self.optimizer.step(grads=[[g] for g in grads_groups_flat],
+                            output_params=[[p] for p in self.fp16_groups_flat],
+                            scale=self.cur_scale,
+                            grad_norms=norm_groups)
+
+        # TODO: we probably don't need this? just to be safe
+        for i in range(len(norm_groups)):
+            updated_params = _unflatten_dense_tensors(self.fp16_groups_flat[i], self.fp16_groups[i])
+            for p,q in zip(self.fp16_groups[i], updated_params):
+                p.data = q.data
+
+        self._update_scale(False)
+        return
+
+    def backward(self, loss):
+        """
+        :attr:`backward` performs the following conceptual steps:
+
+        1. fp32_loss = loss.float()
+        2. scaled_loss = fp32_loss*loss_scale
+        3. scaled_loss.backward(), which accumulates scaled gradients into the ``.grad`` attributes of the model's fp16 leaves
+        """
+        scaled_loss = (loss.float()) * self.cur_scale
+        scaled_loss.backward()
+
+    def _update_scale(self, skip):
+        if self.dynamic_loss_scale:
+            if skip:
+                print("\nGrad overflow on iteration", self.cur_iter)
+                print("Using dynamic loss scale of", self.cur_scale)
+                self.cur_scale = max(self.cur_scale/self.scale_factor, 1)
+                self.last_overflow_iter = self.cur_iter
+            else:
+                if (self.cur_iter - self.last_overflow_iter) % self.scale_window == 0:
+                    self.cur_scale *= self.scale_factor
+        else:
+            if skip:
+                print("\nGrad overflow on iteration", self.cur_iter)
+                print("Using static loss scale of", self.cur_scale)
+        self.cur_iter +=1
+        return
+
+    # Promote state so it can be retrieved or set via "fp16_optimizer_instance.state"
+    def _get_state(self):
+        return self.optimizer.state
+
+    def _set_state(self, value):
+        self.optimizer.state = value
+
+    state = property(_get_state, _set_state)
+
+    # Promote param_groups so it can be retrieved or set via "fp16_optimizer_instance.param_groups"
+    # (for example, to adjust the learning rate)
+    def _get_param_groups(self):
+        return self.optimizer.param_groups
+
+    def _set_param_groups(self, value):
+        self.optimizer.param_groups = value
+
+    param_groups = property(_get_param_groups, _set_param_groups)

apex/optimizers/fused_adam.py

+import types
 import torch
 import fused_adam_cuda
 
-class FusedAdam(torch.optim.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``.
@@ -31,14 +32,19 @@ class FusedAdam(torch.optim.Adam):
         https://openreview.net/forum?id=ryQu7f-RZ
     """
 
-    def __init__(self, params, lr=1e-3, betas=(0.9, 0.999), eps=1e-8,
-                 weight_decay=0, amsgrad=False, eps_inside_sqrt = False):
+    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):
         if amsgrad:
             raise RuntimeError('FusedAdam does not support the AMSGrad variant.')
-        super(FusedAdam, self).__init__(params, lr, betas, eps, weight_decay, amsgrad)
+        defaults = dict(lr=lr, bias_correction=bias_correction,
+                        betas=betas, eps=eps, weight_decay=weight_decay,
+                        max_grad_norm=max_grad_norm)
+        super(FusedAdam, self).__init__(params, defaults)
         self.eps_mode = 0 if  eps_inside_sqrt else 1
 
-    def step(self, closure=None, grads=None, output_params=None, scale=1.):
+    def step(self, closure=None, grads=None, output_params=None, scale=1., grad_norms=None):
         """Performs a single optimization step.
 
         Arguments:
@@ -56,14 +62,47 @@ class FusedAdam(torch.optim.Adam):
         loss = None
         if closure is not None:
             loss = closure()
-        if grads is not None:
-           assert len(self.param_groups)==1, "mixed precision optimizer works for a single group only"
-        for group in self.param_groups:
+
         if grads is None:
-               grads = [None]*len(group['params'])
+            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 = [None]*len(group['params'])
-            for p, grad, output_param in zip(group['params'],grads, output_params):
+            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
+
+            bias_correction = 1 if group['bias_correction'] else 0
+
+            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:
                     continue
@@ -86,6 +125,7 @@ class FusedAdam(torch.optim.Adam):
                 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,
@@ -96,8 +136,9 @@ class FusedAdam(torch.optim.Adam):
                                      beta1,
                                      beta2,
                                      group['eps'],
-                                     scale,
+                                     combined_scale,
                                      state['step'],
-                                     self.eps_mode)
+                                     self.eps_mode,
+                                     bias_correction,
+                                     group['weight_decay'])
         return loss
-