[feature] Add clip_grad_norm for hybrid_parallel_plugin (#4837)

* Add clip_grad_norm for hibrid_parallel_plugin

* polish code

* add unittests

* Move tp to a higher-level optimizer interface.

* bug fix

* polish code

colossalai/amp/naive_amp/mixed_precision_optimizer.py

@@ -1,7 +1,7 @@
-from typing import Dict, List
+from typing import Dict, List, Tuple
 
 import torch
-from torch import Tensor
+from torch import Tensor, inf
 from torch.nn import Module, Parameter
 from torch.optim import Optimizer
 
@@ -68,8 +68,6 @@ class MixedPrecisionOptimizer(OptimizerWrapper):
             self.mixed_precision = BF16MixedPrecisionMixin()
         else:
             raise ValueError(f"Unsupported precision: {precision}")
-        if max_norm > 0.0:
-            raise NotImplementedError("max_norm is not supported yet.")
         self.max_norm = max_norm
         self.working_to_master_map: Dict[Parameter, Tensor] = {}
         self.master_to_working_map: Dict[Tensor, Parameter] = {}
@@ -102,32 +100,65 @@ class MixedPrecisionOptimizer(OptimizerWrapper):
         return super().zero_grad(*args, **kwargs)
 
     def _unscale_and_clip_grads(self, total_norm: float) -> None:
+        """
+        Unscale and clip gradients before performing the optimization step.
+
+        Args:
+            total_norm (float): The computed total gradient norm.
+
+        Returns:
+            None
+        """
         div_scale = 1.0
+
+        # If mixed-precision training is used, get the gradient division scale from the mixed-precision handler.
         if self.mixed_precision is not None:
             div_scale = self.mixed_precision.get_grad_div_scale()
 
         if self.max_norm > 0.0:
-            # norm is in fact norm*scale
+            # Calculate the scaling factor for gradient clipping
+            # The gradient norm is scaled by 'div_scale' and then clipped to 'max_norm'
             clip = ((total_norm / div_scale) + 1e-6) / self.max_norm
+
+            # If the clip factor exceeds 1, adjust 'div_scale' accordingly to ensure clipping
             if clip > 1:
                 div_scale = clip * div_scale
 
+        # Apply the scaling factor to gradients
         for group in self.param_groups:
             for p in group["params"]:
                 if p.grad is None:
                     continue
                 p.grad.data.mul_(1.0 / div_scale)
 
-    def _compute_grad_norm(self) -> float:
+    def _compute_grad_norm(self, param_gradient_pairs: List[Tuple[Tensor]], norm_type: int = 2) -> int:
-        if self.max_norm <= 0.0:
+        r"""
+        Compute and return the gradient norm for gradient clipping.
+
+        Args:
+            param_gradient_pairs (List[Tuple[Tensor]]): List of (parameter, gradient) pairs; gradients are used for norm calculation.
+            norm_type (int, optional): Type of the norm used (e.g., 2 for L2 norm). Defaults to 2.
+
+        Returns:
+            float: The total norm of the given gradients.
+        """
+
+        if len(param_gradient_pairs) == 0:
             return 0.0
-        grads = [p.grad for group in self.param_groups for p in group["params"] if p.grad is not None]
+
-        if len(grads) == 0:
+        # gradients used for norm calculation.
-            return 0.0
+        gradients = [grad for param, grad in param_gradient_pairs]
-        device = grads[0].device
+
-        # TODO(ver217): support tp
+        if norm_type == inf:
-        total_norm = torch.norm(torch.stack([torch.norm(g.detach(), 2).to(device) for g in grads]), 2)
+            total_norm = max(grad.data.abs().max() for grad in gradients)
-        return total_norm.item()
+
+        else:
+            total_norm_exponentiated = 0.0
+            for grad in gradients:
+                total_norm_exponentiated += grad.data.double().norm(norm_type) ** norm_type
+            total_norm = total_norm_exponentiated ** (1.0 / norm_type)
+
+        return total_norm
 
     def step(self, *args, **kwargs):
         if self.mixed_precision.should_skip_step():
@@ -142,8 +173,22 @@ class MixedPrecisionOptimizer(OptimizerWrapper):
                 if working_param.grad is not None:
                     p.grad = working_param.grad.data.float()
                     working_param.grad = None
-        total_norm = self._compute_grad_norm()
+
+        # gradient unscale and clip.
+        if self.max_norm <= 0:
+            # no need to compute gradient norm.
+            total_norm = 0.0
+        else:
+            # compute the total norm.
+            param_gradient_pairs = [
+                (self.master_to_working_map[p], p.grad)
+                for group in self.param_groups
+                for p in group["params"]
+                if p.grad is not None
+            ]
+            total_norm = self._compute_grad_norm(param_gradient_pairs)
         self._unscale_and_clip_grads(total_norm)
+
         self.optim.step(*args, **kwargs)
         # update working params
         for group in self.optim.param_groups:

colossalai/booster/plugin/hybrid_parallel_plugin.py

@@ -1,3 +1,4 @@
+import ctypes
 import random
 from contextlib import nullcontext
 from functools import partial
@@ -7,7 +8,8 @@ from typing import Any, Callable, Iterator, List, Optional, OrderedDict, Tuple,
 import numpy as np
 import torch
 import torch.distributed as dist
-from torch.distributed import ProcessGroup
+from torch import Tensor, inf
+from torch.distributed import ProcessGroup, get_world_size
 from torch.nn import Module, SyncBatchNorm
 from torch.nn.parallel import DistributedDataParallel as DDP
 from torch.optim import Optimizer
@@ -24,6 +26,7 @@ from colossalai.pipeline.schedule import OneForwardOneBackwardSchedule
 from colossalai.pipeline.stage_manager import PipelineStageManager
 from colossalai.shardformer import ShardConfig, ShardFormer
 from colossalai.shardformer.policies.base_policy import Policy
+from colossalai.tensor.d_tensor.api import is_distributed_tensor
 from colossalai.zero.low_level import LowLevelZeroOptimizer
 
 from .pp_plugin_base import PipelinePluginBase
@@ -160,12 +163,143 @@ def init_pipeline_optimizer(optim: Optimizer, model: Module):
 
 
 class HybridParallelNaiveOptimizer(OptimizerWrapper):
-    def __init__(self, optim: Optimizer, model: Module, use_pipeline: bool, param_info: OrderedDict):
+    def __init__(
+        self,
+        optim: Optimizer,
+        model: Module,
+        use_pipeline: bool,
+        param_info: OrderedDict,
+        max_norm: float = 0,
+        tp_process_group: Optional[ProcessGroup] = None,  # if using tp
+        pp_process_group: Optional[ProcessGroup] = None,  # if using pp
+    ):
         self.param_info = param_info
         if use_pipeline:
             init_pipeline_optimizer(optim, model)
+        self.stage_manager = model.stage_manager
+        self.shared_params = model.shared_params
+        self.max_norm = max_norm
+        self.tp_pg = tp_process_group
+        self.pp_pg = pp_process_group
         super().__init__(optim)
 
+    def step(self, *args, **kwargs):
+        r"""
+        Perform an optimization step.
+
+        Args:
+            *args: Variable-length positional arguments to be passed to the optimizer's step function.
+            **kwargs: Keyword arguments to be passed to the optimizer's step function.
+        """
+
+        if self.max_norm > 0:
+            # Compute the total gradient norm.
+            param_gradient_pairs = [
+                (p, p.grad) for group in self.optim.param_groups for p in group["params"] if p.grad is not None
+            ]
+            total_norm = self._compute_grad_norm(param_gradient_pairs)
+
+            # Clip the gradients to prevent exploding gradients.
+            self._clip_grad_norm(total_norm)
+
+        # Perform the optimization step using the underlying optimizer.
+        self.optim.step(*args, **kwargs)
+
+    def _compute_grad_norm(self, param_gradient_pairs: List[Tuple[Tensor]], norm_type: int = 2) -> int:
+        r"""
+        Compute and return the gradient norm for gradient clipping.
+
+        Args:
+            param_gradient_pairs (List[Tuple[Tensor]]): List of (parameter, gradient) pairs; gradients are used for norm calculation.
+            norm_type (int, optional): Type of the norm used (e.g., 2 for L2 norm). Defaults to 2.
+
+        Returns:
+            float: The total norm of the given gradients.
+        """
+
+        if len(param_gradient_pairs) == 0:
+            return 0.0
+
+        tp_size = get_world_size(self.tp_pg) if self.tp_pg is not None else 1
+        pp_size = get_world_size(self.pp_pg) if self.pp_pg is not None else 1
+        norm_type = float(norm_type)
+
+        # gradients used for norm calculation.
+        gradients = [grad for param, grad in param_gradient_pairs]
+
+        if norm_type == inf:
+            total_norm = max(grad.data.abs().max() for grad in gradients)
+            total_norm_cuda = torch.cuda.FloatTensor([float(total_norm)])
+            if tp_size > 1:
+                dist.all_reduce(tensor=total_norm_cuda, op=dist.ReduceOp.MAX, group=self.tp_pg)
+            if pp_size > 1:
+                dist.all_reduce(tensor=total_norm_cuda, op=dist.ReduceOp.MAX, group=self.pp_pg)
+            total_norm = total_norm_cuda.item()
+        else:
+            # gradients used for norm calculation.
+            gradients = [grad for param, grad in param_gradient_pairs]
+            # grad_to_param_mapping is used to check which gradients are not distributed across devices of the 'tp_group'.
+            grad_to_param_mapping = {id(grad): param for param, grad in param_gradient_pairs}
+
+            total_norm_exponentiated = 0.0
+            for grad in gradients:
+                grad_norm_exponentiated = grad.data.double().norm(norm_type) ** norm_type
+
+                # If 'tp_size' is greater than 1 and the parameter for the gradient is not a distributed tensor,
+                # it indicates that the parameter is not distributed across devices of the 'tp_group'.
+                # Consequently, there is no need to perform an 'all_reduce' operation for 'grad_norm'.
+                # However, we still perform the 'all_reduce' operation for the sake of good coding practices.
+                # To ensure mathematical equivalence, we divide the 'grad_norm' by 'tp_size.'
+                if tp_size > 1:
+                    param_for_grad = grad_to_param_mapping[id(grad)]
+                    if not is_distributed_tensor(param_for_grad):
+                        grad_norm_exponentiated /= tp_size
+
+                # If 'pp_size' is greater than 1 and the gradient belongs to shared parameters,
+                # it means that this parameter is used in two different pipeline stages.
+                # To avoid redundant norm calculations, we divide the exponent of this norm by
+                # the number of shared stages.
+                if pp_size > 1:
+                    for shared_param in self.shared_params:
+                        if self.stage_manager.stage in shared_param:
+                            stage_shared_param = shared_param[self.stage_manager.stage]
+                            if grad is stage_shared_param.grad:
+                                grad_norm_exponentiated /= len(shared_param)
+
+                total_norm_exponentiated += grad_norm_exponentiated
+
+            total_norm_exponentiated_cuda = torch.cuda.FloatTensor([float(total_norm_exponentiated)])
+            if tp_size > 1:
+                # compute norm in tp process group
+                dist.all_reduce(tensor=total_norm_exponentiated_cuda, op=dist.ReduceOp.SUM, group=self.tp_pg)
+            if pp_size > 1:
+                # compute norm in pp process group
+                dist.all_reduce(tensor=total_norm_exponentiated_cuda, op=dist.ReduceOp.SUM, group=self.pp_pg)
+
+            # compute the total_norm
+            total_norm = total_norm_exponentiated_cuda.item() ** (1.0 / norm_type)
+
+        return total_norm
+
+    def _clip_grad_norm(self, total_norm: float) -> None:
+        r"""
+        Clips the gradients of the model's parameters to prevent exploding gradients.
+
+        Args:
+            total_norm (float): The computed total gradient norm.
+
+        Returns:
+            None
+        """
+        clip_coef = torch.tensor(self.max_norm / (total_norm + 1e-6))
+        clip_coef_clamped = torch.clamp(clip_coef, max=1.0)
+
+        for group in self.optim.param_groups:
+            for p in group["params"]:
+                if p.grad is None:
+                    continue
+                p.grad.data.mul_(clip_coef_clamped)
+
     def update_master_params(self, model: Module):
         pass
 
@@ -192,23 +326,108 @@ class HybridParallelAMPOptimizer(MixedPrecisionOptimizer):
         hysteresis: int = 2,
         max_scale: float = 2**32,
         max_norm: float = 0,
+        tp_process_group: Optional[ProcessGroup] = None,  # if using tp
+        pp_process_group: Optional[ProcessGroup] = None,  # if using pp
     ):
         self.param_info = param_info
+        self.stage_manager = model.stage_manager
+        self.shared_params = model.shared_params
+        self.tp_pg = tp_process_group
+        self.pp_pg = pp_process_group
         if use_pipeline:
             init_pipeline_optimizer(optim, model)
         super().__init__(
             optim,
-            precision,
+            precision=precision,
-            initial_scale,
+            initial_scale=initial_scale,
-            min_scale,
+            min_scale=min_scale,
-            growth_factor,
+            growth_factor=growth_factor,
-            backoff_factor,
+            backoff_factor=backoff_factor,
-            growth_interval,
+            growth_interval=growth_interval,
-            hysteresis,
+            hysteresis=hysteresis,
-            max_scale,
+            max_scale=max_scale,
-            max_norm,
+            max_norm=max_norm,
         )
 
+    def _compute_grad_norm(self, param_gradient_pairs: List[Tuple[Tensor]], norm_type: int = 2) -> int:
+        r"""
+        Compute and return the gradient norm for gradient clipping.
+
+        Args:
+            param_gradient_pairs (List[Tuple[Tensor]]): List of (parameter, gradient) pairs; gradients are used for norm calculation.
+            norm_type (int, optional): Type of the norm used (e.g., 2 for L2 norm). Defaults to 2.
+
+        Returns:
+            float: The total norm of the given gradients.
+        """
+        if len(param_gradient_pairs) == 0:
+            return 0.0
+
+        tp_size = get_world_size(self.tp_pg) if self.tp_pg is not None else 1
+        pp_size = get_world_size(self.pp_pg) if self.pp_pg is not None else 1
+        norm_type = float(norm_type)
+
+        if norm_type == inf:
+            # The parent class calculates the norm of 'dp' gradients,
+            # so we need to calculate the norm of 'tp' and 'pp' gradients.
+            total_norm = super()._compute_grad_norm(param_gradient_pairs, norm_type)
+
+            total_norm_cuda = torch.cuda.FloatTensor([float(total_norm)])
+
+            if tp_size > 1:
+                dist.all_reduce(tensor=total_norm_cuda, op=dist.ReduceOp.MAX, group=self.tp_pg)
+            if pp_size > 1:
+                dist.all_reduce(tensor=total_norm_cuda, op=dist.ReduceOp.MAX, group=self.pp_pg)
+
+            total_norm = total_norm_cuda.item()
+
+        else:
+            # gradients used for norm calculation.
+            gradients = [grad for param, grad in param_gradient_pairs]
+            # grad_to_param_mapping is used to check which gradients are not distributed in tensor parallelism.
+            grad_to_param_mapping = {id(grad): param for param, grad in param_gradient_pairs}
+
+            total_norm_exponentiated = 0.0
+            for grad in gradients:
+                grad_norm_exponentiated = grad.data.double().norm(norm_type) ** norm_type
+
+                # If 'tp_size' is greater than 1 and the parameter for the gradient is not a distributed tensor,
+                # it indicates that the parameter is not distributed across devices of the 'tp_group'.
+                # Consequently, there is no need to perform an 'all_reduce' operation for 'grad_norm'.
+                # However, we still perform the 'all_reduce' operation for the sake of good coding practices.
+                # To ensure mathematical equivalence, we divide the 'grad_norm' by 'tp_size.'
+                if tp_size > 1:
+                    param_for_grad = grad_to_param_mapping[id(grad)]
+                    if not is_distributed_tensor(param_for_grad):
+                        grad_norm_exponentiated /= tp_size
+
+                # If 'pp_size' is greater than 1 and the gradient belongs to shared parameters,
+                # it means that this parameter is used in two different pipeline stages.
+                # To avoid redundant norm calculations, we divide the exponent of this norm by
+                # the number of shared stages.
+                if pp_size > 1:
+                    for shared_param in self.shared_params:
+                        if self.stage_manager.stage in shared_param:
+                            stage_working_shared_param = shared_param[self.stage_manager.stage]
+                            stage_master_shared_param = self.working_to_master_map[stage_working_shared_param]
+                            if grad is stage_master_shared_param.grad:
+                                grad_norm_exponentiated /= len(shared_param)
+
+                total_norm_exponentiated += grad_norm_exponentiated
+
+            total_norm_exponentiated_cuda = torch.cuda.FloatTensor([float(total_norm_exponentiated)])
+            if tp_size > 1:
+                # compute norm in tp process group
+                dist.all_reduce(tensor=total_norm_exponentiated_cuda, op=dist.ReduceOp.SUM, group=self.tp_pg)
+            if pp_size > 1:
+                # compute norm in pp process group
+                dist.all_reduce(tensor=total_norm_exponentiated_cuda, op=dist.ReduceOp.SUM, group=self.pp_pg)
+
+            # compute the total_norm
+            total_norm = total_norm_exponentiated_cuda.item() ** (1.0 / norm_type)
+
+        return total_norm
+
 
 class HybridParallelZeroOptimizer(LowLevelZeroOptimizer):
     def __init__(
@@ -233,9 +452,15 @@ class HybridParallelZeroOptimizer(LowLevelZeroOptimizer):
         cpu_offload: bool = False,  # cpu offload
         dp_process_group: Optional[ProcessGroup] = None,  # the dp pg for comm
         tp_process_group: Optional[ProcessGroup] = None,  # if using tp
+        pp_process_group: Optional[ProcessGroup] = None,  # if using pp
         forced_dtype: Optional[torch.dtype] = None,
     ):
         self.param_info = param_info
+        self.stage_manager = model.stage_manager
+        self.shared_params = model.shared_params
+        self.dp_pg = dp_process_group
+        self.tp_pg = tp_process_group
+        self.pp_pg = pp_process_group
         if use_pipeline:
             init_pipeline_optimizer(optimizer, model)
         super().__init__(
@@ -255,10 +480,90 @@ class HybridParallelZeroOptimizer(LowLevelZeroOptimizer):
             partition_grad,
             cpu_offload,
             dp_process_group,
-            tp_process_group,
             forced_dtype,
         )
 
+    def _compute_grad_norm(self, gradients: List[Tensor], norm_type: int = 2) -> float:
+        r"""
+        Compute and return the gradient norm for gradient clipping.
+
+        Args:
+            gradients (List[Tensor]): A list of tensors containing gradients.
+            norm_type (int, optional): Type of the p-norm to be computed. Defaults to 2.
+
+        Returns:
+            float: The computed gradient norm.
+        """
+
+        # Check if the list of gradients is empty
+        if len(gradients) == 0:
+            return 0.0
+
+        dp_size = get_world_size(self.dp_pg) if self.dp_pg is not None else 1
+        tp_size = get_world_size(self.tp_pg) if self.tp_pg is not None else 1
+        pp_size = get_world_size(self.pp_pg) if self.pp_pg is not None else 1
+        norm_type = float(norm_type)
+
+        if norm_type == inf:
+            # The parent class calculates the norm of 'dp' gradients,
+            # so we only need to calculate the norm 'tp' of 'pp' gradients.
+            total_norm = super()._compute_grad_norm(gradients, norm_type)
+
+            total_norm_cuda = torch.cuda.FloatTensor([float(total_norm)])
+
+            if tp_size > 1:
+                dist.all_reduce(tensor=total_norm_cuda, op=dist.ReduceOp.MAX, group=self.tp_pg)
+            if pp_size > 1:
+                dist.all_reduce(tensor=total_norm_cuda, op=dist.ReduceOp.MAX, group=self.pp_pg)
+
+            total_norm = total_norm_cuda.item()
+        else:
+            total_norm_exponentiated = 0.0
+            for grad in gradients:
+                grad_norm_exponentiated = grad.data.double().norm(norm_type) ** norm_type
+
+                # If 'tp_size' is greater than 1 and the parameter for the gradient is not a distributed tensor,
+                # it indicates that the parameter is not distributed across devices of the 'tp_group'.
+                # Consequently, there is no need to perform an 'all_reduce' operation for 'grad_norm'.
+                # However, we still perform the 'all_reduce' operation for the sake of good coding practices.
+                # To ensure mathematical equivalence, we divide the 'grad_norm' by 'tp_size.'
+                if tp_size > 1:
+                    param_id_for_grad = self._grad_store.get_param_id_for_grad(grad)
+                    param_for_grad = ctypes.cast(param_id_for_grad, ctypes.py_object).value
+
+                    if not is_distributed_tensor(param_for_grad):
+                        grad_norm_exponentiated /= tp_size
+
+                # If 'pp_size' is greater than 1 and the gradient belongs to shared parameters,
+                # it means that this parameter is used in two different pipeline stages.
+                # To avoid redundant norm calculations, we divide the exponent of this norm by
+                # the number of shared stages.
+                if pp_size > 1:
+                    for shared_param in self.shared_params:
+                        if self.stage_manager.stage in shared_param:
+                            stage_shared_param = shared_param[self.stage_manager.stage]
+                            working_grad = self._grad_store.get_working_grad_by_param_id(id(stage_shared_param))
+                            if grad is working_grad:
+                                grad_norm_exponentiated /= len(shared_param)
+
+                total_norm_exponentiated += grad_norm_exponentiated
+
+            total_norm_exponentiated_cuda = torch.cuda.FloatTensor([float(total_norm_exponentiated)])
+            if dp_size > 1:
+                # compute norm in dp process group
+                dist.all_reduce(tensor=total_norm_exponentiated_cuda, op=dist.ReduceOp.SUM, group=self.dp_pg)
+            if tp_size > 1:
+                # compute norm in tp process group
+                dist.all_reduce(tensor=total_norm_exponentiated_cuda, op=dist.ReduceOp.SUM, group=self.tp_pg)
+            if pp_size > 1:
+                # compute norm in pp process group
+                dist.all_reduce(tensor=total_norm_exponentiated_cuda, op=dist.ReduceOp.SUM, group=self.pp_pg)
+
+            # Compute the 'total_norm' from 'total_norm_exponentiated'
+            total_norm = total_norm_exponentiated_cuda.item() ** (1.0 / norm_type)
+
+        return total_norm
+
 
 class HybridParallelPlugin(PipelinePluginBase):
     """
@@ -475,11 +780,19 @@ class HybridParallelPlugin(PipelinePluginBase):
                         param_info=param_info,
                         precision=self.precision,
                         max_norm=self.max_norm,
+                        pp_process_group=self.pp_group,
+                        tp_process_group=self.tp_group,
                         **self.amp_config,
                     )
                 else:
                     optimizer = HybridParallelNaiveOptimizer(
-                        optimizer, model, use_pipeline=self.enable_pipeline_parallelism, param_info=param_info
+                        optimizer,
+                        model,
+                        use_pipeline=self.enable_pipeline_parallelism,
+                        param_info=param_info,
+                        max_norm=self.max_norm,
+                        pp_process_group=self.pp_group,
+                        tp_process_group=self.tp_group,
                     )
             else:
                 assert self.dp_size > 1, "Please use Zero when data parallel size is greater than 1."
@@ -491,6 +804,7 @@ class HybridParallelPlugin(PipelinePluginBase):
                     param_info=param_info,
                     dp_process_group=self.dp_group,
                     tp_process_group=self.tp_group,
+                    pp_process_group=self.pp_group,
                     verbose=True,
                     clip_grad_norm=self.max_norm,
                     **self.zero_config,

colossalai/zero/low_level/_utils.py

@@ -3,9 +3,7 @@ from typing import Optional
 
 import torch
 import torch.distributed as dist
-from torch import Tensor, inf
 from torch._utils import _flatten_dense_tensors, _unflatten_dense_tensors
-from torch.distributed import ProcessGroup
 
 
 def flatten(input_):
@@ -192,53 +190,6 @@ def calculate_global_norm_from_list(norm_list):
         total_norm += norm**2.0
     return math.sqrt(total_norm)
 
-
-def compute_norm(gradients: Tensor, dp_group: ProcessGroup, tp_group: ProcessGroup, norm_type: int = 2) -> int:
-    """Clips gradient norm of an iterable of parameters.
-    This is adapted from torch.nn.utils.clip_grad.clip_grad_norm_ and
-    added functionality to handle model parallel parameters.
-
-    Args:
-        gradients (Tensor): The gradients to compute norm
-        dp_group (ProcessGroup): The process group of ZeRO Data Parallelism
-        tp_group (ProcessGroup): The process group of Tensor Parallelism
-        norm_type (int, optional): type of the used p-norm, Can be ``'inf'`` for infinity norm. Defaults to 2.
-
-    Returns:
-        int: The total norm of given gradients
-    """
-
-    norm_type = float(norm_type)
-    if norm_type == inf:
-        total_norm = max(g.data.abs().max() for g in gradients)
-        total_norm_cuda = torch.cuda.FloatTensor([float(total_norm)])
-        dist.all_reduce(total_norm_cuda, op=torch.distributed.ReduceOp.MAX, group=dp_group)
-
-        # Take max across all GPUs.
-        if tp_group is not None:
-            dist.all_reduce(tensor=total_norm_cuda, op=torch.distributed.ReduceOp.MAX)
-        total_norm = total_norm_cuda[0].item()
-    else:
-        total_norm = 0.0
-        for g in gradients:
-            param_norm = g.data.double().norm(norm_type)
-            total_norm += param_norm.item() ** norm_type
-
-        # Sum across all model parallel GPUs.
-        total_norm_cuda = torch.cuda.FloatTensor([float(total_norm)])
-        torch.distributed.all_reduce(total_norm_cuda, op=torch.distributed.ReduceOp.SUM, group=dp_group)
-
-        if tp_group is not None:
-            dist.all_reduce(tensor=total_norm_cuda, op=torch.distributed.ReduceOp.SUM, group=tp_group)
-
-        total_norm = total_norm_cuda[0].item() ** (1.0 / norm_type)
-
-    if total_norm == float("inf") or total_norm == -float("inf") or total_norm != total_norm:
-        total_norm = -1
-
-    return total_norm
-
-
 def sync_tensor(flat_tensor, tensor_list):
     """
     Synchronize the flattened tensor and unflattened tensor list. When

colossalai/zero/low_level/bookkeeping/gradient_store.py

@@ -21,6 +21,8 @@ class GradientStore(BaseStore):
         # for zero2, it's `param_id: [grad_local_rank]`
         self._working_index = 0 if partition_grad else self._local_rank
 
+        self.grad_to_param_mapping = dict()
+
     def get_partitioned_gradients_by_param_id(self, group_id: int, param_id: int) -> List:
         """Return list of gradient slices of a specific parameter
 
@@ -54,6 +56,8 @@ class GradientStore(BaseStore):
         else:
             self._grads_of_params[group_id][param_id].append(grad)
 
+        self.grad_to_param_mapping[id(grad)] = param_id
+
     def add_gradients_by_param_id(self, grad: Tensor, grad_idx: int, group_id: int, param_id: int):
         """Add a gradient slice on an existing slice of the parameter's gradient
         Used when no_sync is not activated.
@@ -83,8 +87,37 @@ class GradientStore(BaseStore):
 
         return grad_list
 
+    def get_working_grad_by_param_id(self, param_id) -> Tensor:
+        """
+        Return the working gradient for the specified parameter.
+
+        Args:
+            param_id (int): The index of the parameter.
+
+        Returns:
+            Tensor: The the working gradient slices for the specified param_id.
+        """
+
+        for group in self._grads_of_params.values():
+            if param_id in group.keys():
+                return group[param_id][self._working_index]
+
+        raise KeyError(f"Working gradient for param_id {param_id} not found.")
+
     def reset_grads_by_group_id(self, group_id: int):
         self._grads_of_params[group_id] = dict()
 
     def reset_all_gradients(self):
         self._grads_of_params = dict()
+
+    def get_param_id_for_grad(self, grad: Tensor) -> int:
+        """Return the id of a parameter which the gradient slice belongs to
+
+        Args:
+            grad (Tensor): the gradient slice
+
+        Returns:
+            int: the id of a parameter which the gradient slice belongs to
+        """
+
+        return self.grad_to_param_mapping[id(grad)]

colossalai/zero/low_level/low_level_optim.py

@@ -2,11 +2,12 @@
 import copy
 from contextlib import contextmanager
 from functools import partial
-from typing import Dict, Iterator, Optional, Tuple
+from typing import Dict, Iterator, List, Optional, Tuple
 
 import torch
 import torch.distributed as dist
 import torch.nn as nn
+from torch import Tensor, inf
 from torch.distributed import ProcessGroup
 from torch.optim import Optimizer
 
@@ -21,14 +22,7 @@ from colossalai.logging import get_dist_logger
 # from colossalai.tensor import ColoParameter, ProcessGroup
 from colossalai.utils.cuda import get_current_device
 
-from ._utils import (
+from ._utils import calculate_global_norm_from_list, flatten, has_inf_or_nan, release_param_grad, sync_tensor
-    calculate_global_norm_from_list,
-    compute_norm,
-    flatten,
-    has_inf_or_nan,
-    release_param_grad,
-    sync_tensor,
-)
 from .bookkeeping import BucketStore, GradientStore, ParameterStore
 
 
@@ -80,7 +74,6 @@ class LowLevelZeroOptimizer(OptimizerWrapper):
         partition_grad: bool = False,  # stage 2 flag
         cpu_offload: bool = False,  # cpu offload
         dp_process_group: Optional[ProcessGroup] = None,  # the dp pg for comm
-        tp_process_group: Optional[ProcessGroup] = None,  # if using tp
         forced_dtype: Optional[torch.dtype] = None,
     ):
         super(LowLevelZeroOptimizer, self).__init__(optim=optimizer)
@@ -101,8 +94,6 @@ class LowLevelZeroOptimizer(OptimizerWrapper):
         self._local_rank = dist.get_rank(group=self.dp_pg)
         self._world_size = dist.get_world_size(group=self.dp_pg)
 
-        self.tp_pg = tp_process_group
-
         # working and master params for mixed precision training
         self._working_param_groups = dict()
         self._master_param_groups_of_current_rank = dict()
@@ -433,7 +424,7 @@ class LowLevelZeroOptimizer(OptimizerWrapper):
 
             # compute norm
             working_grads = self._grad_store.get_working_grads_by_group_id(group_id)
-            norm_group = compute_norm(gradients=working_grads, dp_group=self.dp_pg, tp_group=self.tp_pg)
+            norm_group = self._compute_grad_norm(gradients=working_grads)
             norm_groups.append(norm_group)
 
             self._grad_store.reset_grads_by_group_id(group_id)
@@ -467,6 +458,44 @@ class LowLevelZeroOptimizer(OptimizerWrapper):
 
             self.optim.param_groups[group_id]["params"] = self._master_param_groups_of_current_rank[group_id]
 
+    def _compute_grad_norm(self, gradients: List[Tensor], norm_type: int = 2) -> float:
+        r"""
+        Compute and return the gradient norm for gradient clipping.
+
+        Args:
+            gradients (List[Tensor]): The gradients to compute norm
+            norm_type (int, optional): type of the used p-norm, Can be ``'inf'`` for infinity norm. Defaults to 2.
+
+        Returns:
+            float: The total norm of given gradients
+        """
+
+        if len(gradients) == 0:
+            return 0.0
+
+        norm_type = float(norm_type)
+        if norm_type == inf:
+            total_norm = max(grad.data.abs().max() for grad in gradients)
+
+            total_norm_cuda = torch.cuda.FloatTensor([float(total_norm)])
+            dist.all_reduce(total_norm_cuda, op=torch.distributed.ReduceOp.MAX, group=self.dp_pg)
+            total_norm = total_norm_cuda.item()
+
+        else:
+            total_norm_exponentiated = 0.0
+            for grad in gradients:
+                grad_norm_exponentiated = grad.data.double().norm(norm_type) ** norm_type
+                total_norm_exponentiated += grad_norm_exponentiated
+
+            # Sum across all model parallel GPUs.
+            total_norm_exponentiated_cuda = torch.cuda.FloatTensor([float(total_norm_exponentiated)])
+            torch.distributed.all_reduce(
+                total_norm_exponentiated_cuda, op=torch.distributed.ReduceOp.SUM, group=self.dp_pg
+            )
+            total_norm = total_norm_exponentiated_cuda.item() ** (1.0 / norm_type)
+
+        return total_norm
+
     #############################
     # Mixed Precision Utilities #
     #############################

tests/test_shardformer/test_hybrid_parallel_grad_clip_norm/test_amp_optimizer.py

@@ -0,0 +1,258 @@
+import pytest
+import torch
+from torch.nn.utils.clip_grad import clip_grad_norm_
+
+import colossalai
+from colossalai.logging import disable_existing_loggers
+from colossalai.shardformer.layer.utils import Randomizer
+from colossalai.tensor.d_tensor.api import clear_layout_converter
+from colossalai.testing import clear_cache_before_run, parameterize, rerun_if_address_is_in_use, spawn
+from tests.kit.model_zoo import model_zoo
+from tests.test_shardformer.test_model._utils import (
+    build_model_from_hybrid_plugin,
+    check_all_grad_tensors,
+    check_loss,
+    check_output_hidden_state,
+    check_weight,
+    get_grad_tensors_for_check,
+    run_forward_backward_with_hybrid_plugin,
+    unwrap_model,
+)
+
+
+def check_forward_backward(model_fn, data_gen_fn, output_transform_fn, loss_fn, test_config):
+    org_model, org_optimizer, sharded_model, sharded_optimizer, criterion, booster = build_model_from_hybrid_plugin(
+        model_fn, loss_fn, test_config
+    )
+
+    org_loss, org_output, sharded_loss, sharded_output = run_forward_backward_with_hybrid_plugin(
+        org_model, sharded_model, sharded_optimizer, data_gen_fn, output_transform_fn, criterion, booster
+    )
+
+    stage_manager = booster.plugin.stage_manager
+    tp_group = booster.plugin.tp_group
+
+    bert = unwrap_model(org_model, "BertModel", "bert")
+    sharded_bert = unwrap_model(sharded_model, "BertModel", "bert")
+
+    col_layer_for_check = ["encoder.layer[0].output.dense"]
+    row_layer_for_check = ["embeddings.word_embeddings", "encoder.layer[0].intermediate.dense"]
+
+    if test_config["precision"] == "fp32":
+        atol, rtol = 1e-4, 1e-3
+    elif test_config["precision"] == "fp16":
+        atol, rtol = 5e-3, 5e-3
+    else:
+        atol, rtol = 2e-2, 2e-2
+
+    # Check grads
+    # Save gradient tensors for comparison between the original model and the sharded model.
+    grads_to_check = {}
+    if (stage_manager is None or stage_manager.is_first_stage()) and booster.plugin.zero_stage == 0:
+        col_layer_grads = get_grad_tensors_for_check(
+            bert, sharded_bert, col_layer_for_check, tp_group, atol=atol, rtol=rtol, dim=1, verbose=False
+        )
+        row_layer_grads = get_grad_tensors_for_check(
+            bert, sharded_bert, row_layer_for_check, tp_group, atol=atol, rtol=rtol, dim=0, verbose=False
+        )
+        grads_to_check.update(col_layer_grads)
+        grads_to_check.update(row_layer_grads)
+    check_all_grad_tensors(grads_to_check)
+
+    # Check gradient norm
+    # Convert the gradient data of the working parameter to float and assign it to the master parameter's gradient
+    # Note that this operation should have been done in the 'step' function, but it is performed here in advance for gradient norm calculation purposes.
+    # Although it will be done again in the 'step' function, it does not affect correctness.
+    for group in sharded_optimizer.optim.param_groups:
+        for p in group["params"]:
+            working_param = sharded_optimizer.master_to_working_map[p]
+            if p is working_param:
+                continue
+            if working_param.grad is not None:
+                p.grad = working_param.grad.data.float()
+                working_param.grad = None
+    # Create a list of parameter-gradient pairs containing working parameters and their gradients
+    param_gradient_pairs = [
+        (sharded_optimizer.master_to_working_map[p], p.grad)
+        for group in sharded_optimizer.param_groups
+        for p in group["params"]
+        if p.grad is not None
+    ]
+
+    origin_norm = clip_grad_norm_(org_model.parameters(), test_config["max_norm"])
+    # Calculate the gradient norm of the sharded optimizer
+    device = origin_norm.device
+    hybrid_norm = torch.tensor(sharded_optimizer._compute_grad_norm(param_gradient_pairs)).to(device)
+
+    # If using fp16 precision, divide by the initial scale
+    if test_config["precision"] == "fp16":
+        hybrid_norm /= test_config["initial_scale"]
+
+    # Assert that the gradient norm of the original model is close to the gradient norm of the hybrid model
+    assert torch.allclose(
+        origin_norm, hybrid_norm, atol=atol, rtol=rtol
+    ), f"Original model grad norm is not equal to sharded model grad norm\n{origin_norm}\n{hybrid_norm}"
+
+    # Optimizer executes step
+    org_optimizer.step()
+    sharded_optimizer.step()
+
+    # Check last hidden state & loss
+    if stage_manager is None or stage_manager.is_last_stage():
+        if test_config["precision"] == "fp32":
+            atol, rtol = 1e-5, 1e-3
+        elif test_config["precision"] == "fp16":
+            atol, rtol = 5e-3, 5e-3
+        else:
+            atol, rtol = 2e-2, 2e-2
+        if org_model.__class__.__name__ == "BertModel":
+            check_output_hidden_state(org_output, sharded_output, stage_manager, atol=atol, rtol=rtol)
+
+        check_loss(org_loss, sharded_loss, atol=atol, rtol=rtol)
+
+    # Check weights
+    if test_config["precision"] == "fp32":
+        atol, rtol = 5e-3, 1e-3
+    else:
+        atol, rtol = 5e-3, 5e-3
+    if stage_manager is None or stage_manager.is_first_stage():
+        check_weight(bert, sharded_bert, col_layer_for_check, tp_group, atol=atol, rtol=rtol, dim=1, verbose=False)
+
+    torch.cuda.empty_cache()
+
+
+@parameterize(
+    "test_config",
+    [
+        {
+            "tp_size": 1,
+            "pp_size": 2,
+            "num_microbatches": 4,
+            "enable_all_optimization": False,
+            "use_lazy_init": True,
+            "precision": "fp16",
+            "max_norm": 5,
+            "initial_scale": 1,
+        },
+        {
+            "tp_size": 2,
+            "pp_size": 1,
+            "enable_all_optimization": False,
+            "use_lazy_init": False,
+            "precision": "fp16",
+            "max_norm": 5,
+            "initial_scale": 1,
+        },
+        {
+            "tp_size": 2,
+            "pp_size": 2,
+            "num_microbatches": 4,
+            "enable_all_optimization": False,
+            "use_lazy_init": False,
+            "precision": "fp16",
+            "max_norm": 5,
+            "initial_scale": 1,
+        },
+        {
+            "tp_size": 1,
+            "pp_size": 2,
+            "num_microbatches": 4,
+            "enable_all_optimization": False,
+            "use_lazy_init": True,
+            "precision": "bf16",
+            "max_norm": 5,
+        },
+        {
+            "tp_size": 2,
+            "pp_size": 1,
+            "enable_all_optimization": False,
+            "use_lazy_init": False,
+            "precision": "bf16",
+            "max_norm": 5,
+        },
+        {
+            "tp_size": 2,
+            "pp_size": 2,
+            "num_microbatches": 4,
+            "enable_all_optimization": False,
+            "use_lazy_init": False,
+            "precision": "bf16",
+            "max_norm": 5,
+        },
+    ],
+)
+def run_test(test_config):
+    sub_model_zoo = model_zoo.get_sub_registry("transformers_bert")
+
+    for name, (model_fn, data_gen_fn, output_transform_fn, loss_fn, _) in sub_model_zoo.items():
+        check_forward_backward(model_fn, data_gen_fn, output_transform_fn, loss_fn, test_config)
+
+    clear_layout_converter()
+    Randomizer.reset_index()
+    torch.cuda.empty_cache()
+
+
+@parameterize(
+    "test_config",
+    [
+        {
+            "tp_size": 2,
+            "pp_size": 2,
+            "num_microbatches": 4,
+            "enable_all_optimization": False,
+            "use_lazy_init": False,
+            "precision": "bf16",
+            "max_norm": 5,
+        },
+        {
+            "tp_size": 2,
+            "pp_size": 2,
+            "num_microbatches": 4,
+            "enable_all_optimization": False,
+            "use_lazy_init": False,
+            "precision": "fp16",
+            "max_norm": 5,
+            "initial_scale": 1,
+        },
+    ],
+)
+def run_3d_test(test_config):
+    sub_model_zoo = model_zoo.get_sub_registry("transformers_bert")
+
+    for name, (model_fn, data_gen_fn, output_transform_fn, loss_fn, _) in sub_model_zoo.items():
+        check_forward_backward(model_fn, data_gen_fn, output_transform_fn, loss_fn, test_config)
+
+    clear_layout_converter()
+    Randomizer.reset_index()
+    torch.cuda.empty_cache()
+
+
+def check_grad_clip_norm(rank, world_size, port):
+    disable_existing_loggers()
+    colossalai.launch(config={}, rank=rank, world_size=world_size, host="localhost", port=port, backend="nccl")
+    run_test()
+
+
+def check_grad_clip_norm_3d(rank, world_size, port):
+    disable_existing_loggers()
+    colossalai.launch(config={}, rank=rank, world_size=world_size, host="localhost", port=port, backend="nccl")
+    run_3d_test()
+
+
+@pytest.mark.dist
+@rerun_if_address_is_in_use()
+@clear_cache_before_run()
+def test_grad_clip_norm():
+    spawn(check_grad_clip_norm, 4)
+
+
+@pytest.mark.largedist
+@rerun_if_address_is_in_use()
+@clear_cache_before_run()
+def test_grad_clip_norm_3d():
+    spawn(check_grad_clip_norm_3d, 8)
+
+
+if __name__ == "__main__":
+    test_grad_clip_norm()
+    test_grad_clip_norm_3d()

tests/test_shardformer/test_hybrid_parallel_grad_clip_norm/test_naive_optimizer.py

@@ -0,0 +1,197 @@
+import pytest
+import torch
+from torch.nn.utils.clip_grad import clip_grad_norm_
+
+import colossalai
+from colossalai.logging import disable_existing_loggers
+from colossalai.shardformer.layer.utils import Randomizer
+from colossalai.tensor.d_tensor.api import clear_layout_converter
+from colossalai.testing import clear_cache_before_run, parameterize, rerun_if_address_is_in_use, spawn
+from tests.kit.model_zoo import model_zoo
+from tests.test_shardformer.test_model._utils import (
+    build_model_from_hybrid_plugin,
+    check_all_grad_tensors,
+    check_loss,
+    check_output_hidden_state,
+    check_weight,
+    get_grad_tensors_for_check,
+    run_forward_backward_with_hybrid_plugin,
+    unwrap_model,
+)
+
+
+def check_forward_backward(model_fn, data_gen_fn, output_transform_fn, loss_fn, test_config):
+    org_model, org_optimizer, sharded_model, sharded_optimizer, criterion, booster = build_model_from_hybrid_plugin(
+        model_fn, loss_fn, test_config
+    )
+
+    org_loss, org_output, sharded_loss, sharded_output = run_forward_backward_with_hybrid_plugin(
+        org_model, sharded_model, sharded_optimizer, data_gen_fn, output_transform_fn, criterion, booster
+    )
+
+    stage_manager = booster.plugin.stage_manager
+    tp_group = booster.plugin.tp_group
+
+    bert = unwrap_model(org_model, "BertModel", "bert")
+    sharded_bert = unwrap_model(sharded_model, "BertModel", "bert")
+
+    col_layer_for_check = ["encoder.layer[0].output.dense"]
+    row_layer_for_check = ["embeddings.word_embeddings", "encoder.layer[0].intermediate.dense"]
+
+    if test_config["precision"] == "fp32":
+        atol, rtol = 1e-4, 1e-3
+    elif test_config["precision"] == "fp16":
+        atol, rtol = 5e-3, 5e-3
+    else:
+        atol, rtol = 2e-2, 2e-2
+
+    # Check grads
+    # Save gradient tensors for comparison between the original model and the sharded model.
+    grads_to_check = {}
+    if (stage_manager is None or stage_manager.is_first_stage()) and booster.plugin.zero_stage == 0:
+        col_layer_grads = get_grad_tensors_for_check(
+            bert, sharded_bert, col_layer_for_check, tp_group, atol=atol, rtol=rtol, dim=1, verbose=False
+        )
+        row_layer_grads = get_grad_tensors_for_check(
+            bert, sharded_bert, row_layer_for_check, tp_group, atol=atol, rtol=rtol, dim=0, verbose=False
+        )
+        grads_to_check.update(col_layer_grads)
+        grads_to_check.update(row_layer_grads)
+    check_all_grad_tensors(grads_to_check)
+
+    # Check grad norm
+    param_gradient_pairs = [
+        (p, p.grad) for group in sharded_optimizer.param_groups for p in group["params"] if p.grad is not None
+    ]
+    origin_norm = clip_grad_norm_(org_model.parameters(), test_config["max_norm"])
+    device = origin_norm.device
+    hybrid_norm = torch.tensor(sharded_optimizer._compute_grad_norm(param_gradient_pairs)).to(device)
+    assert torch.allclose(
+        origin_norm, hybrid_norm, atol=atol, rtol=rtol
+    ), f"orgin origin model grad norm is not equal to shard model grad norm\n{origin_norm}\n{hybrid_norm}"
+
+    # optimizer executes step
+    org_optimizer.step()
+    sharded_optimizer.step()
+
+    # check last hidden state & loss
+    if stage_manager is None or stage_manager.is_last_stage():
+        if test_config["precision"] == "fp32":
+            atol, rtol = 1e-5, 1e-3
+        elif test_config["precision"] == "fp16":
+            atol, rtol = 5e-3, 5e-3
+        else:
+            atol, rtol = 2e-2, 2e-2
+
+        if org_model.__class__.__name__ == "BertModel":
+            check_output_hidden_state(org_output, sharded_output, stage_manager, atol=atol, rtol=rtol)
+
+        check_loss(org_loss, sharded_loss, atol=atol, rtol=rtol)
+
+    # check weights
+    if test_config["precision"] == "fp32":
+        atol, rtol = 5e-3, 1e-3
+    else:
+        atol, rtol = 5e-3, 5e-3
+    if stage_manager is None or stage_manager.is_first_stage():
+        check_weight(bert, sharded_bert, col_layer_for_check, tp_group, atol=atol, rtol=rtol, dim=1, verbose=False)
+
+    torch.cuda.empty_cache()
+
+
+@parameterize(
+    "test_config",
+    [
+        {
+            "tp_size": 1,
+            "pp_size": 2,
+            "num_microbatches": 4,
+            "enable_all_optimization": False,
+            "use_lazy_init": True,
+            "precision": "fp32",
+            "max_norm": 5,
+        },
+        {
+            "tp_size": 2,
+            "pp_size": 1,
+            "enable_all_optimization": False,
+            "use_lazy_init": False,
+            "precision": "fp32",
+            "max_norm": 5,
+        },
+        {
+            "tp_size": 2,
+            "pp_size": 2,
+            "num_microbatches": 4,
+            "enable_all_optimization": False,
+            "use_lazy_init": False,
+            "precision": "fp32",
+            "max_norm": 5,
+        },
+    ],
+)
+def run_test(test_config):
+    sub_model_zoo = model_zoo.get_sub_registry("transformers_bert")
+
+    for name, (model_fn, data_gen_fn, output_transform_fn, loss_fn, _) in sub_model_zoo.items():
+        check_forward_backward(model_fn, data_gen_fn, output_transform_fn, loss_fn, test_config)
+
+    clear_layout_converter()
+    Randomizer.reset_index()
+    torch.cuda.empty_cache()
+
+
+@parameterize(
+    "test_config",
+    [
+        {
+            "tp_size": 2,
+            "pp_size": 2,
+            "num_microbatches": 4,
+            "enable_all_optimization": False,
+            "use_lazy_init": False,
+            "precision": "fp32",
+            "max_norm": 5,
+        },
+    ],
+)
+def run_3d_test(test_config):
+    sub_model_zoo = model_zoo.get_sub_registry("transformers_bert")
+
+    for name, (model_fn, data_gen_fn, output_transform_fn, loss_fn, _) in sub_model_zoo.items():
+        check_forward_backward(model_fn, data_gen_fn, output_transform_fn, loss_fn, test_config)
+
+    clear_layout_converter()
+    Randomizer.reset_index()
+    torch.cuda.empty_cache()
+
+
+def check_grad_clip_norm(rank, world_size, port):
+    disable_existing_loggers()
+    colossalai.launch(config={}, rank=rank, world_size=world_size, host="localhost", port=port, backend="nccl")
+    run_test()
+
+
+def check_grad_clip_norm_3d(rank, world_size, port):
+    disable_existing_loggers()
+    colossalai.launch(config={}, rank=rank, world_size=world_size, host="localhost", port=port, backend="nccl")
+    run_3d_test()
+
+
+@pytest.mark.dist
+@rerun_if_address_is_in_use()
+@clear_cache_before_run()
+def test_grad_clip_norm():
+    spawn(check_grad_clip_norm, 4)
+
+
+@pytest.mark.largedist
+@rerun_if_address_is_in_use()
+@clear_cache_before_run()
+def test_grad_clip_norm_3d():
+    spawn(check_grad_clip_norm_3d, 8)
+
+
+if __name__ == "__main__":
+    test_grad_clip_norm()
+    test_grad_clip_norm_3d()

tests/test_shardformer/test_hybrid_parallel_grad_clip_norm/test_zero_optimizer.py

@@ -0,0 +1,241 @@
+import math
+
+import pytest
+import torch
+import torch.distributed as dist
+from torch.nn.utils.clip_grad import clip_grad_norm_
+
+import colossalai
+from colossalai.logging import disable_existing_loggers
+from colossalai.shardformer.layer.utils import Randomizer
+from colossalai.tensor.d_tensor.api import clear_layout_converter
+from colossalai.testing import clear_cache_before_run, parameterize, rerun_if_address_is_in_use, spawn
+from tests.kit.model_zoo import model_zoo
+from tests.test_shardformer.test_model._utils import (
+    build_model_from_hybrid_plugin,
+    check_loss,
+    check_output_hidden_state,
+    check_weight,
+    run_forward_backward_with_hybrid_plugin,
+    unwrap_model,
+)
+
+
+def check_forward_backward(model_fn, data_gen_fn, output_transform_fn, loss_fn, test_config):
+    org_model, org_optimizer, sharded_model, sharded_optimizer, criterion, booster = build_model_from_hybrid_plugin(
+        model_fn, loss_fn, test_config
+    )
+
+    org_loss, org_output, sharded_loss, sharded_output = run_forward_backward_with_hybrid_plugin(
+        org_model, sharded_model, sharded_optimizer, data_gen_fn, output_transform_fn, criterion, booster
+    )
+
+    stage_manager = booster.plugin.stage_manager
+    tp_group = booster.plugin.tp_group
+
+    bert = unwrap_model(org_model, "BertModel", "bert")
+    sharded_bert = unwrap_model(sharded_model, "BertModel", "bert")
+
+    col_layer_for_check = ["encoder.layer[0].output.dense"]
+
+    if test_config["precision"] == "fp32":
+        atol, rtol = 1e-4, 1e-3
+    elif test_config["precision"] == "fp16":
+        atol, rtol = 5e-3, 5e-3
+    else:
+        atol, rtol = 2e-2, 2e-2
+
+    dist.barrier()
+    # Check gradient norm
+    origin_norm = clip_grad_norm_(org_model.parameters(), test_config["max_norm"])
+
+    # Calculate the gradient norm of the sharded optimizer
+    device = origin_norm.device
+    norm_groups = []
+    for group_id in range(sharded_optimizer.num_param_groups):
+        working_grads = sharded_optimizer._grad_store.get_working_grads_by_group_id(group_id)
+        norm_group = sharded_optimizer._compute_grad_norm(gradients=working_grads)
+        norm_groups.append(norm_group)
+    total_norm = 0.0
+    for norm in norm_groups:
+        total_norm += norm**2.0
+    hybrid_norm = torch.tensor(math.sqrt(total_norm)).to(device)
+
+    # If using fp16 precision, divide by the initial scale
+    if test_config["precision"] == "fp16":
+        hybrid_norm /= test_config["initial_scale"]
+
+    # Assert that the gradient norm of the original model is close to the gradient norm of the hybrid model
+    assert torch.allclose(
+        origin_norm, hybrid_norm, atol=atol, rtol=rtol
+    ), f"Original model grad norm is not equal to sharded model grad norm\n{origin_norm}\n{hybrid_norm}"
+
+    # optimizer executes step
+    org_optimizer.step()
+    sharded_optimizer.step()
+
+    # check last hidden state & loss
+    if stage_manager is None or stage_manager.is_last_stage():
+        if test_config["precision"] == "fp32":
+            atol, rtol = 1e-5, 1e-3
+        elif test_config["precision"] == "fp16":
+            atol, rtol = 5e-3, 5e-3
+        else:
+            atol, rtol = 2e-2, 2e-2
+        if org_model.__class__.__name__ == "BertModel":
+            check_output_hidden_state(org_output, sharded_output, stage_manager, atol=atol, rtol=rtol)
+
+        check_loss(org_loss, sharded_loss, atol=atol, rtol=rtol)
+
+    # check weights
+    if test_config["precision"] == "fp32":
+        atol, rtol = 5e-3, 1e-3
+    else:
+        atol, rtol = 5e-3, 5e-3
+    if stage_manager is None or stage_manager.is_first_stage():
+        check_weight(bert, sharded_bert, col_layer_for_check, tp_group, atol=atol, rtol=rtol, dim=1, verbose=False)
+
+    torch.cuda.empty_cache()
+
+
+@parameterize(
+    "test_config",
+    [
+        {
+            "tp_size": 1,
+            "pp_size": 2,
+            "num_microbatches": 4,
+            "zero_stage": 1,
+            "enable_all_optimization": False,
+            "use_lazy_init": True,
+            "precision": "fp16",
+            "max_norm": 5,
+            "initial_scale": 1,
+        },
+        {
+            "tp_size": 2,
+            "pp_size": 1,
+            "zero_stage": 1,
+            "enable_all_optimization": False,
+            "use_lazy_init": False,
+            "precision": "fp16",
+            "max_norm": 5,
+            "initial_scale": 1,
+        },
+        {
+            "tp_size": 2,
+            "pp_size": 1,
+            "zero_stage": 2,
+            "enable_all_optimization": False,
+            "use_lazy_init": False,
+            "precision": "fp16",
+            "max_norm": 5,
+            "initial_scale": 1,
+        },
+        {
+            "tp_size": 1,
+            "pp_size": 2,
+            "num_microbatches": 4,
+            "zero_stage": 1,
+            "enable_all_optimization": False,
+            "use_lazy_init": True,
+            "precision": "bf16",
+            "max_norm": 5,
+        },
+        {
+            "tp_size": 2,
+            "pp_size": 1,
+            "zero_stage": 1,
+            "enable_all_optimization": False,
+            "use_lazy_init": False,
+            "precision": "bf16",
+            "max_norm": 5,
+        },
+        {
+            "tp_size": 2,
+            "pp_size": 1,
+            "zero_stage": 2,
+            "enable_all_optimization": False,
+            "use_lazy_init": False,
+            "precision": "bf16",
+            "max_norm": 5,
+        },
+    ],
+)
+def run_test(test_config):
+    sub_model_zoo = model_zoo.get_sub_registry("transformers_bert")
+
+    for name, (model_fn, data_gen_fn, output_transform_fn, loss_fn, _) in sub_model_zoo.items():
+        check_forward_backward(model_fn, data_gen_fn, output_transform_fn, loss_fn, test_config)
+
+    clear_layout_converter()
+    Randomizer.reset_index()
+    torch.cuda.empty_cache()
+
+
+@parameterize(
+    "test_config",
+    [
+        {
+            "tp_size": 2,
+            "pp_size": 2,
+            "num_microbatches": 4,
+            "zero_stage": 1,
+            "enable_all_optimization": False,
+            "use_lazy_init": False,
+            "precision": "bf16",
+            "max_norm": 5,
+        },
+        {
+            "tp_size": 2,
+            "pp_size": 2,
+            "num_microbatches": 4,
+            "zero_stage": 1,
+            "enable_all_optimization": False,
+            "use_lazy_init": False,
+            "precision": "fp16",
+            "max_norm": 5,
+            "initial_scale": 1,
+        },
+    ],
+)
+def run_3d_test(test_config):
+    sub_model_zoo = model_zoo.get_sub_registry("transformers_bert")
+
+    for name, (model_fn, data_gen_fn, output_transform_fn, loss_fn, _) in sub_model_zoo.items():
+        check_forward_backward(model_fn, data_gen_fn, output_transform_fn, loss_fn, test_config)
+
+    clear_layout_converter()
+    Randomizer.reset_index()
+    torch.cuda.empty_cache()
+
+
+def check_grad_clip_norm(rank, world_size, port):
+    disable_existing_loggers()
+    colossalai.launch(config={}, rank=rank, world_size=world_size, host="localhost", port=port, backend="nccl")
+    run_test()
+
+
+def check_grad_clip_norm_3d(rank, world_size, port):
+    disable_existing_loggers()
+    colossalai.launch(config={}, rank=rank, world_size=world_size, host="localhost", port=port, backend="nccl")
+    run_3d_test()
+
+
+@pytest.mark.dist
+@rerun_if_address_is_in_use()
+@clear_cache_before_run()
+def test_grad_clip_norm():
+    spawn(check_grad_clip_norm, 4)
+
+
+@pytest.mark.largedist
+@rerun_if_address_is_in_use()
+@clear_cache_before_run()
+def test_grad_clip_norm_3d():
+    spawn(check_grad_clip_norm_3d, 8)
+
+
+if __name__ == "__main__":
+    test_grad_clip_norm()
+    test_grad_clip_norm_3d()