polish optimizer docstring (#619)

colossalai/nn/optimizer/cpu_adam.py

@@ -44,8 +44,8 @@ class CPUAdam(torch.optim.Optimizer):
             True for decoupled weight decay(also known as AdamW) (default: True)
         simd_log (boolean, optional): whether to show if you are using SIMD to 
             accelerate. (default: False)
-    
-    .. _Adam: A Method for Stochastic Optimization:
+
+    .. _Adam\: A Method for Stochastic Optimization:
         https://arxiv.org/abs/1412.6980
     .. _On the Convergence of Adam and Beyond:
         https://openreview.net/forum?id=ryQu7f-RZ

colossalai/nn/optimizer/fused_adam.py

@@ -41,7 +41,7 @@ class FusedAdam(torch.optim.Optimizer):
         set_grad_none (bool, optional): whether set grad to None when zero_grad()
             method is called. (default: True)
 
-    .. _Adam: A Method for Stochastic Optimization:
+    .. _Adam\: A Method for Stochastic Optimization:
         https://arxiv.org/abs/1412.6980
     .. _On the Convergence of Adam and Beyond:
         https://openreview.net/forum?id=ryQu7f-RZ
@@ -128,14 +128,14 @@ class FusedAdam(torch.optim.Optimizer):
 
                 if p.dtype not in [torch.float16, torch.float32]:
                     raise RuntimeError('FusedAdam only support fp16 and fp32.')
-                
+
                 g_l.append(p.grad.data)
                 p_l.append(p.data)
                 m_l.append(state['exp_avg'])
                 v_l.append(state['exp_avg_sq'])
 
-            multi_tensor_applier(self.multi_tensor_adam, self._dummy_overflow_buf, [g_l, p_l, m_l, v_l],
-                                    group['lr'], beta1, beta2, group['eps'], group['step'], self.adamw_mode,
-                                    bias_correction, group['weight_decay'])
+            multi_tensor_applier(self.multi_tensor_adam, self._dummy_overflow_buf, [g_l, p_l, m_l, v_l], group['lr'],
+                                 beta1, beta2, group['eps'], group['step'], self.adamw_mode, bias_correction,
+                                 group['weight_decay'])
 
         return loss

colossalai/nn/optimizer/fused_lamb.py

@@ -10,7 +10,7 @@ class FusedLAMB(torch.optim.Optimizer):
     """Implements LAMB algorithm.
 
     Currently GPU-only.  Requires ColossalAI to be installed via
-    ``pip install -v --no-cache-dir --global-option="--cuda_ext" ./``.
+    ``pip install .``.
 
     This version of fused LAMB implements 2 fusions.
 

colossalai/nn/optimizer/fused_sgd.py

@@ -11,7 +11,7 @@ class FusedSGD(Optimizer):
     r"""Implements stochastic gradient descent (optionally with momentum).
 
     Currently GPU-only.  Requires ColossalAI to be installed via
-    ``pip install -v --no-cache-dir --global-option="--cuda_ext" ./``.
+    ``pip install .``.
 
     This version of fused SGD implements 2 fusions.
 
@@ -57,8 +57,13 @@ class FusedSGD(Optimizer):
         The Nesterov version is analogously modified.
     """
 
-    def __init__(self, params, lr=required, momentum=0, dampening=0,
-                 weight_decay=0, nesterov=False,
+    def __init__(self,
+                 params,
+                 lr=required,
+                 momentum=0,
+                 dampening=0,
+                 weight_decay=0,
+                 nesterov=False,
                  wd_after_momentum=False,
                  materialize_master_grads=True,
                  set_grad_none=False):
@@ -67,14 +72,11 @@ class FusedSGD(Optimizer):
         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))
+            raise ValueError("Invalid weight_decay value: {}".format(weight_decay))
 
-        defaults = dict(lr=lr, momentum=momentum, dampening=dampening,
-                        weight_decay=weight_decay, nesterov=nesterov)
+        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")
+            raise ValueError("Nesterov momentum requires a momentum and zero dampening")
         super(FusedSGD, self).__init__(params, defaults)
 
         self.wd_after_momentum = wd_after_momentum
@@ -86,8 +88,9 @@ class FusedSGD(Optimizer):
         if multi_tensor_applier.available:
             import colossal_C
             # Skip buffer
-            self._dummy_overflow_buf = torch.tensor(
-                [0], dtype=torch.int, device=self.param_groups[0]["params"][0].device)
+            self._dummy_overflow_buf = torch.tensor([0],
+                                                    dtype=torch.int,
+                                                    device=self.param_groups[0]["params"][0].device)
             self.multi_tensor_sgd = colossal_C.multi_tensor_sgd
         else:
             raise RuntimeError('FusedSGD requires cuda extensions')
@@ -133,8 +136,7 @@ class FusedSGD(Optimizer):
         if closure is not None:
             loss = closure()
 
-        explicit_master_params = (hasattr(self, "_amp_stash") and
-                                  hasattr(self._amp_stash, "fp32_from_fp16_groups"))
+        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']
@@ -154,71 +156,52 @@ class FusedSGD(Optimizer):
             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_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]
+                    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 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)
+                        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 = [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]]
+                    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_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_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]]
+                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)
+                    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

colossalai/nn/optimizer/hybrid_adam.py

 import torch
 
-
 from colossalai.utils import multi_tensor_applier
 from colossalai.registry import OPTIMIZERS
 
@@ -14,13 +13,14 @@ class HybridAdam(torch.optim.Optimizer):
       * Parameters on CPU and gradients on CPU is allowed.
       * Parameters on GPU and gradients on GPU is allowed.
       * Parameters on GPU and gradients on CPU is **not** allowed.
-    
+
     Requires ColossalAI to be installed via ``pip install .``
 
     This version of Hybrid Adam is an hybrid of CPUAdam and FusedAdam.
-      * For parameters updating on CPU, it uses CPUAdam.
-      * For parameters updating on GPU, it uses FusedAdam.
-      * Hybird precision calculation of fp16 and fp32 is supported, eg fp32 parameters and fp16 gradients.
+
+    * For parameters updating on CPU, it uses CPUAdam.
+    * For parameters updating on GPU, it uses FusedAdam.
+    * Hybird precision calculation of fp16 and fp32 is supported, eg fp32 parameters and fp16 gradients.
 
     :class:`colossalai.nn.optimizer.HybridAdam` may be used as a drop-in replacement for ``torch.optim.AdamW``,
     or ``torch.optim.Adam`` with ``adamw_mode=False``
@@ -43,8 +43,8 @@ class HybridAdam(torch.optim.Optimizer):
             True for decoupled weight decay(also known as AdamW) (default: True)
         simd_log (boolean, optional): whether to show if you are using SIMD to 
             accelerate. (default: False)
-    
-    .. _Adam: A Method for Stochastic Optimization:
+
+    .. _Adam\: A Method for Stochastic Optimization:
         https://arxiv.org/abs/1412.6980
     .. _On the Convergence of Adam and Beyond:
         https://openreview.net/forum?id=ryQu7f-RZ
@@ -75,7 +75,7 @@ class HybridAdam(torch.optim.Optimizer):
             import colossal_C
         except ImportError:
             raise ImportError('Please install colossalai from source code to use HybridAdam')
-        
+
         self.cpu_adam_op = cpu_adam
         self.cpu_adam_op.create_adam(self.opt_id, lr, betas[0], betas[1], eps, weight_decay, adamw_mode, simd_log)
 
@@ -131,14 +131,14 @@ class HybridAdam(torch.optim.Optimizer):
                     g_l.append(p.grad.data)
                     p_l.append(p.data)
                     m_l.append(state['exp_avg'])
-                    v_l.append(state['exp_avg_sq']) 
+                    v_l.append(state['exp_avg_sq'])
 
                 else:
                     raise RuntimeError
             if len(g_l) > 0:
                 adamw_mode = 1 if self.adamw_mode else 0
                 bias_correction = 1 if group['bias_correction'] else 0
-                multi_tensor_applier(self.gpu_adam_op, self._dummy_overflow_buf, [g_l, p_l,m_l, v_l],
-                                     group['lr'], group['betas'][0], group['betas'][1], group['eps'], group_step,
-                                     adamw_mode, bias_correction, group['weight_decay'])
+                multi_tensor_applier(self.gpu_adam_op, self._dummy_overflow_buf, [g_l, p_l, m_l, v_l], group['lr'],
+                                     group['betas'][0], group['betas'][1], group['eps'], group_step, adamw_mode,
+                                     bias_correction, group['weight_decay'])
         return loss