Distributed LAMB: Clip grads before reduce_scatter/all_reduce (#1099)

* clip before reduce scatter

* provide clip before/after RS option

* change to clip after ar (avoid confusion)

* fix comments

apex/contrib/optimizers/distributed_fused_lamb.py

@@ -87,7 +87,7 @@ class DistributedFusedLAMB(torch.optim.Optimizer):
                  step_supports_amp_scaling=True, overlap_reductions=True,
                  dwu_group_size=0, dwu_num_blocks=4, dwu_num_chunks=4,
                  dwu_num_rs_pg=1, dwu_num_ar_pg=4, dwu_num_ag_pg=0, 
-                 e5m2_allgather=False, verbose=False):
+                 e5m2_allgather=False, verbose=False, clip_after_ar=True):
         defaults = dict(lr=lr, bias_correction=bias_correction,
                         betas=betas, eps=eps, weight_decay=weight_decay,
                         grad_averaging=grad_averaging,
@@ -118,6 +118,7 @@ class DistributedFusedLAMB(torch.optim.Optimizer):
         self._num_chunks = dwu_num_chunks
         self._e5m2_allgather = e5m2_allgather
         self._verbose = verbose
+        self._clip_after_ar = clip_after_ar
         self._L2_grad_norm = None
         
         self._current_process_group = c10d._get_default_group()
@@ -470,40 +471,87 @@ class DistributedFusedLAMB(torch.optim.Optimizer):
         return flush_block
 
     def _pipeline_block_reductions(self, block_id):
-        self._flatten_grad_mt(1.0/self._world_size)
-
-        # Reduction within each node
-        # Changes gradient format from [block * chunk * shard] to [shard * block * chunk]
-        # The output format is the same as the fp32 master parameters
-        works = [None]*self._num_chunks
-        for chunk_id in range(self._num_chunks):
-            glob_chunk_id = block_id * self._num_chunks + chunk_id
-            rs_stream = self._rs_st[glob_chunk_id%self._num_rs_pg]
-            rs_stream.wait_stream(torch.cuda.current_stream())
-            with torch.cuda.stream(rs_stream):
-                works[chunk_id] = torch.distributed.reduce_scatter(self._fp16_g_chunks[block_id][chunk_id],self._flat_grads_shards[block_id][chunk_id],group=self._rs_pg[glob_chunk_id%self._num_rs_pg],async_op=True,no_copy=True)
-
-        # Reduction across nodes for each rank
-        if self._num_groups > 1:
+        if self._clip_after_ar:
+            self._flatten_grad_mt(1.0/self._world_size)
+
+            # Reduction within each node
+            # Changes gradient format from [block * chunk * shard] to [shard * block * chunk]
+            # The output format is the same as the fp32 master parameters
+            works = [None]*self._num_chunks
             for chunk_id in range(self._num_chunks):
                 glob_chunk_id = block_id * self._num_chunks + chunk_id
-                ar_stream = self._ar_st[glob_chunk_id%self._num_ar_pg]
-                with torch.cuda.stream(ar_stream):
-                    works[chunk_id].wait()
-                    works[chunk_id] = torch.distributed.all_reduce(self._fp16_g_chunks[block_id][chunk_id],group=self._ar_pg[glob_chunk_id%self._num_ar_pg],async_op=True)
-        self._reductions_works[block_id] = works
-
-        # Compute L2 grad norm
-        if block_id == 0:
+                rs_stream = self._rs_st[glob_chunk_id%self._num_rs_pg]
+                rs_stream.wait_stream(torch.cuda.current_stream())
+                with torch.cuda.stream(rs_stream):
+                    works[chunk_id] = torch.distributed.reduce_scatter(self._fp16_g_chunks[block_id][chunk_id],self._flat_grads_shards[block_id][chunk_id],group=self._rs_pg[glob_chunk_id%self._num_rs_pg],async_op=True,no_copy=True)
+
+            # Reduction across nodes for each rank
+            if self._num_groups > 1:
+                for chunk_id in range(self._num_chunks):
+                    glob_chunk_id = block_id * self._num_chunks + chunk_id
+                    ar_stream = self._ar_st[glob_chunk_id%self._num_ar_pg]
+                    with torch.cuda.stream(ar_stream):
+                        works[chunk_id].wait()
+                        works[chunk_id] = torch.distributed.all_reduce(self._fp16_g_chunks[block_id][chunk_id],group=self._ar_pg[glob_chunk_id%self._num_ar_pg],async_op=True)
+            self._reductions_works[block_id] = works
+
+            # Compute L2 grad norm
+            if block_id == 0:
+                with torch.cuda.stream(self._l2_grad_norm_st):
+                    for block_id in range(self._num_blocks):
+                        for chunk_id in range(self._num_chunks):
+                            self._reductions_works[block_id][chunk_id].wait()
+                    # Since the packed format is contiguous after reductions, only one norm is needed
+                    l2_grad_norm_sq = torch.empty([1], device='cuda')
+                    l2_grad_norm_sq = self._fp16_g.norm(dtype=torch.float32, p=2)**2
+                    torch.distributed.all_reduce(l2_grad_norm_sq, group=self._l2_grad_norm_pg)
+                    self._L2_grad_norm = l2_grad_norm_sq.sqrt()
+        else:
+            # Copy model grads to flat grads buffer
+            self._flatten_grad_mt(1.0)
+
+            # Compute L2 grad norm
+            self._l2_grad_norm_st.wait_stream(torch.cuda.current_stream())
             with torch.cuda.stream(self._l2_grad_norm_st):
+                self._L2_grad_norm = self._flat_grads.norm(dtype=torch.float16, p=2).float()
+            torch.cuda.current_stream().wait_stream(self._l2_grad_norm_st)
+
+            # Apply clipping & pre-reduction scaling on grads
+            loss_scale = self.global_scale
+            max_grad_norm = loss_scale*self.defaults['max_grad_norm']
+            coeff = max_grad_norm /(1e-6+self.L2_grad_norm)
+            coeff = (coeff>1) * self._one + (coeff<=1) * coeff
+            tmp = torch.cat(((self._one), (coeff)))
+            index = (coeff+1>coeff).int()
+            scale = tmp.index_select(0, index).half()/self._world_size
+            self._flat_grads.mul_(scale)
+
+            # Reduction within each node
+            # Changes gradient format from [block * chunk * shard] to [shard * block * chunk]
+            # The output format is the same as the fp32 master parameters
+            works = [None]*self._num_chunks
+            for chunk_id in range(self._num_chunks):
+                glob_chunk_id = block_id * self._num_chunks + chunk_id
+                rs_stream = self._rs_st[glob_chunk_id%self._num_rs_pg]
+                rs_stream.wait_stream(torch.cuda.current_stream())
+                rs_stream.wait_stream(self._l2_grad_norm_st)
+                with torch.cuda.stream(rs_stream):
+                    works[chunk_id] = torch.distributed.reduce_scatter(self._fp16_g_chunks[block_id][chunk_id],self._flat_grads_shards[block_id][chunk_id],group=self._rs_pg[glob_chunk_id%self._num_rs_pg],async_op=True,no_copy=True)
+
+            # Reduction across nodes for each rank
+            if self._num_groups > 1:
+                for chunk_id in range(self._num_chunks):
+                    glob_chunk_id = block_id * self._num_chunks + chunk_id
+                    ar_stream = self._ar_st[glob_chunk_id%self._num_ar_pg]
+                    with torch.cuda.stream(ar_stream):
+                        works[chunk_id].wait()
+                        works[chunk_id] = torch.distributed.all_reduce(self._fp16_g_chunks[block_id][chunk_id],group=self._ar_pg[glob_chunk_id%self._num_ar_pg],async_op=True)
+            self._reductions_works[block_id] = works
+
+            if block_id == 0:
                 for block_id in range(self._num_blocks):
                     for chunk_id in range(self._num_chunks):
                         self._reductions_works[block_id][chunk_id].wait()
-                # Since the packed format is contiguous after reductions, only one norm is needed
-                l2_grad_norm_sq = torch.empty([1], device='cuda')
-                l2_grad_norm_sq = self._fp16_g.norm(dtype=torch.float32, p=2)**2
-                torch.distributed.all_reduce(l2_grad_norm_sq, group=self._l2_grad_norm_pg)
-                self._L2_grad_norm = l2_grad_norm_sq.sqrt()
 
     def __compute_contrib_param_norm(self):
         if self._contrib_model_param_for_norm_fp16 is not None and self._contrib_model_param_for_norm_fp32 is not None:
@@ -528,12 +576,20 @@ class DistributedFusedLAMB(torch.optim.Optimizer):
 
     def _pipeline_step(self):
         global_scale = self.global_scale
-        max_grad_norm = self.defaults['max_grad_norm']
+        # if clip before ar, set max_grad_norm to 0
+        max_grad_norm = self.defaults['max_grad_norm'] * self._clip_after_ar
+        self._completion_st.wait_stream(self._l2_grad_norm_st)
         global_grad_norm = self.L2_grad_norm
 
         # check global_grad_norm and fill overflow_buf
         is_finite = (global_grad_norm + 1 > global_grad_norm).int()
         self._overflow_buf = self._one * (is_finite ^ self._one) # toggle between 0 and 1
+        torch.distributed.all_reduce(is_finite,
+                                     op=torch.distributed.ReduceOp.MIN,
+                                     group=self._current_process_group)
+        torch.distributed.all_reduce(self._overflow_buf,
+                                     op=torch.distributed.ReduceOp.MAX,
+                                     group=self._current_process_group)
 
         # increment step counter if no overflow
         self._step += is_finite