Merge branch 'feat/fstp' of https://github.com/yingtongxiong/InternLM into feat/fstp

internlm/core/scheduler/no_pipeline_scheduler.py

@@ -194,6 +194,7 @@ class NonPipelineScheduler(BaseScheduler):
             _output, _loss, _moe_loss = self._train_one_batch(
                 _data, _label, engine, forward_only, return_loss, self._grad_accum_size
             )
+            engine.optimizer.reset_reduce_bucket()
 
             if return_loss:
                 loss += _loss

internlm/model/linear.py

@@ -329,6 +329,8 @@ class FSTPAllGatherSyncHandler:
         self.block_module = dict()  # key: transformer block index; value: {name_index: FSTP module}
         self.module_name_index = dict()  # key: FSTP module; value: the name in index in self.module_name
 
+        self.reduce_scatter_handlers = {}
+
         # just want to share same for loop for ModuleList and Module
         if not isinstance(model, nn.ModuleList):
             model = [model]
@@ -337,16 +339,22 @@ class FSTPAllGatherSyncHandler:
             if isinstance(_chunk, NaiveAMPModel):
                 _chunk = _chunk.model
 
-            for _, children in _chunk.named_children():
+            for _chunk_name, children in _chunk.named_children():
                 if isinstance(children, nn.ModuleList):
                     for idx, block in enumerate(children):
                         index = 0
                         self.block_module[idx] = {}
-                        for _, sub in block.named_children():
+                        for _sub_name, sub in block.named_children():
                             sub_modules = list(sub.children())
                             if len(sub_modules) > 0:
                                 for name, child in sub.named_children():
                                     if isinstance(child, FSTPLinear):
+
+                                        _full_name = f"{_chunk_name}.{idx}.{_sub_name}.{name}"
+                                        setattr(child.weight, "_fstp_reduce_scatter_str", f"{_full_name}.weight")
+                                        if child.bias is not None:
+                                            setattr(child.bias, "_fstp_reduce_scatter_str", f"{_full_name}.bias")
+
                                         self.FSTP_modules.append(child)
                                         self.module_block[child] = idx
                                         self.block_module[idx][index] = child
@@ -451,6 +459,8 @@ class CoarseGrainedFSTPAllGatherSyncHandler:
         self.block_module = dict()  # key: transformer block index; value: {name_index: FSTP module}
         self.head = []
 
+        self.reduce_scatter_handlers = {}
+
         # just want to share same for loop for ModuleList and Module
         if not isinstance(model, nn.ModuleList):
             model = [model]
@@ -459,7 +469,7 @@ class CoarseGrainedFSTPAllGatherSyncHandler:
             if isinstance(_chunk, NaiveAMPModel):
                 _chunk = _chunk.model
 
-            for _, children in _chunk.named_children():
+            for _chunk_name, children in _chunk.named_children():
                 if isinstance(children, nn.ModuleList):
                     for idx, block in enumerate(children):
                         index = 0
@@ -468,7 +478,7 @@ class CoarseGrainedFSTPAllGatherSyncHandler:
                         self.block_to_index[block] = idx
                         self.index_to_block[idx] = block
                         self.index_to_fsdp_modules[idx] = []
-                        for _, sub in block.named_children():
+                        for _sub_name, sub in block.named_children():
                             sub_modules = list(sub.children())
                             if len(sub_modules) > 0:
                                 for name, child in sub.named_children():
@@ -486,6 +496,11 @@ class CoarseGrainedFSTPAllGatherSyncHandler:
                                         self.index_to_fsdp_modules[idx].append(child)
                                         self.module_name_index[child] = index
                                         index = index + 1
+                                        
+                                        _full_name = f"{_chunk_name}.{idx}.{_sub_name}.{name}"
+                                        setattr(child.weight, "_fstp_reduce_scatter_str", f"{_full_name}.weight")
+                                        if child.bias is not None:
+                                            setattr(child.bias, "_fstp_reduce_scatter_str", f"{_full_name}.bias")
                             else:
                                 continue
                 elif isinstance(children, ScaleColumnParallelLinear):

internlm/model/utils.py

@@ -324,9 +324,9 @@ class FSTPFusedDenseFunc(torch.autograd.Function):
             raise RuntimeError("fused_dense only supports matrix dims <= 2M")
         output = F.linear(total_x, total_weight, total_bias)
         if ctx.compute_weight_gradient:
-            ctx.save_for_backward(x, weight)
+            ctx.save_for_backward(x, weight, bias)
         else:
-            ctx.save_for_backward(weight)
+            ctx.save_for_backward(weight, bias)
         return output if not return_residual else (output, x)
 
     @staticmethod
@@ -340,10 +340,10 @@ class FSTPFusedDenseFunc(torch.autograd.Function):
         all_gather_handler = ctx.all_gather_handler
         module = ctx.module
         if ctx.compute_weight_gradient:
-            x, weight = ctx.saved_tensors
+            x, weight, bias = ctx.saved_tensors
             total_x = x
         else:
-            (weight,) = ctx.saved_tensors
+            weight, bias = ctx.saved_tensors
             total_x = None
         batch_shape = grad_output.shape[:-1]
         batch_dim = batch_shape.numel()
@@ -368,9 +368,15 @@ class FSTPFusedDenseFunc(torch.autograd.Function):
                 total_x.reshape(batch_dim, total_x.shape[-1]), grad_output, ctx.needs_input_grad[2]
             )
             if world_size > 1:
-                grad_weight, handle_grad_weight = reduce_scatter_raw(grad_weight, process_group, async_op=True)
+                grad_weight_async, handle_grad_weight = reduce_scatter_raw(grad_weight, process_group, async_op=True)
+                assert hasattr(weight, "_fstp_reduce_scatter_str")
+                all_gather_handler.reduce_scatter_handlers[weight._fstp_reduce_scatter_str] = (handle_grad_weight, grad_weight_async)
+                grad_weight = torch.zeros(grad_weight.shape[0]//torch.distributed.get_world_size(process_group), *grad_weight.shape[1:], dtype=grad_weight.dtype, device=grad_weight.device)
                 if grad_bias is not None:
-                    grad_bias, handle_grad_bias = reduce_scatter_raw(grad_bias, process_group, async_op=True)
+                    grad_bias_async, handle_grad_bias = reduce_scatter_raw(grad_bias, process_group, async_op=True)
+                    assert hasattr(bias, "_fstp_reduce_scatter_str")
+                    all_gather_handler.reduce_scatter_handlers[bias._fstp_reduce_scatter_str] = (handle_grad_bias, grad_bias_async)
+                    grad_bias = torch.zeros(grad_bias.shape[0]//torch.distributed.get_world_size(process_group), *grad_bias.shape[1:], dtype=grad_bias.dtype, device=grad_bias.device)
         else:
             grad_weight = None
             grad_bias = grad_output if ctx.needs_input_grad[2] else None
@@ -384,11 +390,11 @@ class FSTPFusedDenseFunc(torch.autograd.Function):
         else:
             grad_input = None
 
-        if ctx.needs_input_grad[1]:
+        # if ctx.needs_input_grad[1]:
-            if world_size > 1:
+        #     if world_size > 1:
-                handle_grad_weight.wait()
+        #         handle_grad_weight.wait()
-                if grad_bias is not None:
+        #         if grad_bias is not None:
-                    handle_grad_bias.wait()
+        #             handle_grad_bias.wait()
         return grad_input, grad_weight, grad_bias, None, None, None, None
 
 

internlm/solver/optimizer/hybrid_zero_optim.py

@@ -65,6 +65,8 @@ class HybridZeroOptimizer(BaseOptimizer):
         hysteresis = grad_scal_cfg.hysteresis
         max_scale = grad_scal_cfg.max_scale
 
+        self._fstp_handler = gpc.config.fstp_handler
+
         # Zero related args
         reduce_bucket_size = zero_cfg.reduce_bucket_size
         clip_grad_norm = zero_cfg.clip_grad_norm
@@ -301,8 +303,7 @@ class HybridZeroOptimizer(BaseOptimizer):
                         # NOT IMPORTANT BUT GOOD TO KNOW:
                         # args here is not grad, but allow_unreacable and accumulate_grad
                         def reduce_grad_hook(*args):  # pylint: disable=W0613
-                            if self.skip_grad_reduce is False:
+                            reduction_func()
-                                reduction_func()
 
                         accum_grad_obj.register_hook(reduce_grad_hook)
 
@@ -322,6 +323,20 @@ class HybridZeroOptimizer(BaseOptimizer):
         group_id = getattr(param, "group_id")
         return tensor_rank == gpc.get_local_rank(self._broadcast_parallel_mode[group_id])
 
+    def reset_reduce_bucket(self) -> None:
+        for bucket in self._bucket_store:
+            for rank, params in bucket._params.items():
+                for _param in params:
+                    if not hasattr(_param, "_fstp_reduce_scatter_str"):
+                        continue
+
+                    key = getattr(_param, "_fstp_reduce_scatter_str")
+                    comm_handle, _grad = self._fstp_handler.reduce_scatter_handlers[key]
+                    comm_handle.wait()
+                    _param.grad += _grad
+
+                bucket.reset_by_rank(rank)
+
     def _store_and_try_reduce_grads_by_bucket(self, param, reduce_rank=None):
         param_size = param.numel()
 
@@ -332,11 +347,26 @@ class HybridZeroOptimizer(BaseOptimizer):
         current_bucket = self._bucket_store[group_id]
 
         if current_bucket.num_elements_in_bucket(reduce_rank) + param_size > self._reduce_bucket_size:
-            self._reduce_grads_stored_in_bucket(current_bucket, reduce_rank, last_bucket=False)
+            # wait reduce scatter communication
+            params = current_bucket.get_param(reduce_rank)
+            for _param in params:
+                if not hasattr(_param, "_fstp_reduce_scatter_str"):
+                    continue
+
+                key = getattr(_param, "_fstp_reduce_scatter_str")
+                comm_handle, _grad = self._fstp_handler.reduce_scatter_handlers[key]
+                comm_handle.wait()
+                _param.grad += _grad
+
+            # reduce grad
+            if self.skip_grad_reduce is False:
+                self._reduce_grads_stored_in_bucket(current_bucket, reduce_rank, last_bucket=False)
+            else:
+                current_bucket.reset_by_rank(reduce_rank)
 
         # the param must not be reduced to ensure correctness
         is_param_reduced = self._param_store.is_param_reduced(param)
-        if is_param_reduced:
+        if is_param_reduced and self.skip_grad_reduce is False:
             msg = (
                 f"Parameter of size ({param.size()}) has already been reduced, "
                 + "duplicate reduction will lead to arithmetic incorrectness"

internlm/train/training_internlm.py

@@ -576,4 +576,8 @@ def record_current_batch_training_metrics(
             tgs_list.append(tgs_origin)
         if batch_count == gpc.config.data.total_steps - 1:
             print(tgs_list, flush=True)
+            avg_tgs = sum(tgs_list) / len(tgs_list)
+            for tgs in tgs_list.copy():
+                if abs(tgs - avg_tgs) > 1000:
+                    tgs_list.remove(tgs)
             print(f"avg_tgs: {sum(tgs_list)/len(tgs_list)}", flush=True)