[zero] polish sharded param name (#484)

* [zero] polish sharded param name

* polish code

* polish

* polish code

* polish

* polsih

* polish

colossalai/engine/ophooks/zero_hook.py

@@ -34,13 +34,13 @@ class ZeroHook(BaseOpHook):
         tensor_list = []
         for param in module.parameters():
             assert hasattr(param, 'col_attr')
-            tensor_list.append(param.col_attr.data)
+            tensor_list.append(param.col_attr.sharded_data_tensor)
         self.shard_strategy.gather(tensor_list, self.process_group)
         for param in module.parameters():
-            if param.col_attr.data.device != self.computing_device:
-                param.col_attr.data.to(self.computing_device)
-                GLOBAL_MODEL_DATA_TRACER.add_tensor(param.col_attr.data.payload)
-            param.data = param.col_attr.data.payload
+            if param.col_attr.sharded_data_tensor.device != self.computing_device:
+                param.col_attr.sharded_data_tensor.to(self.computing_device)
+                GLOBAL_MODEL_DATA_TRACER.add_tensor(param.col_attr.sharded_data_tensor.payload)
+            param.data = param.col_attr.sharded_data_tensor.payload
 
         if self._memstarts_collector:
             self._memstarts_collector.sample_memstats()
@@ -49,7 +49,7 @@ class ZeroHook(BaseOpHook):
         tensor_list = []
         for param in module.parameters():
             assert hasattr(param, 'col_attr')
-            tensor_list.append(param.col_attr.data)
+            tensor_list.append(param.col_attr.sharded_data_tensor)
         self.shard_strategy.shard(tensor_list, self.process_group)
         for param in module.parameters():
             param.col_attr.remove_torch_payload()
@@ -58,13 +58,13 @@ class ZeroHook(BaseOpHook):
         tensor_list = []
         for param in module.parameters():
             assert hasattr(param, 'col_attr')
-            tensor_list.append(param.col_attr.data)
+            tensor_list.append(param.col_attr.sharded_data_tensor)
         self.shard_strategy.gather(tensor_list, self.process_group)
         for param in module.parameters():
-            if param.col_attr.data.device != self.computing_device:
-                param.col_attr.data.to(self.computing_device)
-                GLOBAL_MODEL_DATA_TRACER.add_tensor(param.col_attr.data.payload)
-            param.data = param.col_attr.data.payload
+            if param.col_attr.sharded_data_tensor.device != self.computing_device:
+                param.col_attr.sharded_data_tensor.to(self.computing_device)
+                GLOBAL_MODEL_DATA_TRACER.add_tensor(param.col_attr.sharded_data_tensor.payload)
+            param.data = param.col_attr.sharded_data_tensor.payload
             # Store local accumulated grad shard
             if param.grad is not None:
                 if param.col_attr.bwd_count == 0:
@@ -75,7 +75,7 @@ class ZeroHook(BaseOpHook):
                 else:
                     # We have stored local accumulated grad
                     # The grad here must be locally computed full grad in this backward pass
-                    assert param.grad.shape == param.col_attr.data.origin_shape
+                    assert param.grad.shape == param.col_attr.sharded_data_tensor.origin_shape
             param.col_attr.bwd_count += 1
         if self._memstarts_collector:
             self._memstarts_collector.sample_memstats()
@@ -84,7 +84,7 @@ class ZeroHook(BaseOpHook):
         tensor_list = []
         for param in module.parameters():
             assert hasattr(param, 'col_attr')
-            tensor_list.append(param.col_attr.data)
+            tensor_list.append(param.col_attr.sharded_data_tensor)
         self.shard_strategy.shard(tensor_list, self.process_group)
         for param in module.parameters():
             param.col_attr.remove_torch_payload()

colossalai/utils/commons/bucket_tensor_copy.py

@@ -50,7 +50,7 @@ class BucketizedTensorCopy(object):
         self._cuda_buffer.copy_(self._cpu_buffer)
         flush_offset = 0
         for sparam, numel in zip(self._buffered_param_list, self._numel_list):
-            sparam.data.copy_payload(self._cpu_buffer.narrow(0, flush_offset, numel))
+            sparam.sharded_data_tensor.copy_payload(self._cpu_buffer.narrow(0, flush_offset, numel))
             flush_offset += numel
 
         self.reset()

colossalai/zero/init_ctx/init_context.py

@@ -160,8 +160,8 @@ class ZeroInitContext(InsertPostInitMethodToModuleSubClasses):
             self.initialized_param_list.append(param)
 
             if self.shard_param:
-                self.shard_strategy.shard([param.col_attr._data_sharded_tensor], self.dp_process_group)
-                GLOBAL_MODEL_DATA_TRACER.add_tensor(param.col_attr._data_sharded_tensor.payload)
+                self.shard_strategy.shard([param.col_attr.sharded_data_tensor], self.dp_process_group)
+                GLOBAL_MODEL_DATA_TRACER.add_tensor(param.col_attr.sharded_data_tensor.payload)
             # if param.col_attr.grad and self.shard_grad:
             #     self.shard_strategy.shard([param.col_attr._grad_sharded_tensor], self.dp_process_group)
             #     GLOBAL_MODEL_DATA_TRACER.add_tensor(param.col_attr._grad_sharded_tensor.payload)

colossalai/zero/sharded_model/sharded_model_v2.py

@@ -165,7 +165,7 @@ class ShardedModelV2(nn.Module):
         if self.shard_param:
             for p in self.module.parameters():
                 if not p.col_attr.param_is_sharded:
-                    self.shard_strategy.shard([p.col_attr.data], self.process_group)
+                    self.shard_strategy.shard([p.col_attr.sharded_data_tensor], self.process_group)
         for p in self.module.parameters():
             p.col_attr.bwd_count = 0
             if not p.requires_grad:
@@ -249,13 +249,15 @@ class ShardedModelV2(nn.Module):
         param.col_attr.fp16_grad = reduced_grad.data
 
     def state_dict(self, destination=None, prefix='', keep_vars=False) -> 'OrderedDict[str, torch.Tensor]':
-        self.shard_strategy.gather([p.col_attr.data for p in self.module.parameters()], self.process_group)
+        self.shard_strategy.gather([p.col_attr.sharded_data_tensor for p in self.module.parameters()],
+                                   self.process_group)
         prev_params = {}
         for p in self.module.parameters():
             prev_params[p] = p.data
-            p.data = p.col_attr.data.payload
+            p.data = p.col_attr.sharded_data_tensor.payload
         gathered_state_dict = self.module.state_dict(destination, prefix, keep_vars)
-        self.shard_strategy.shard([p.col_attr.data for p in self.module.parameters()], self.process_group)
+        self.shard_strategy.shard([p.col_attr.sharded_data_tensor for p in self.module.parameters()],
+                                  self.process_group)
         for p in self.module.parameters():
             p.data = prev_params[p]
         return gathered_state_dict

colossalai/zero/sharded_model/utils.py

@@ -11,9 +11,9 @@ def col_model_deepcopy(sharded_model: ShardedModelV2, other_model: torch.nn.Modu
     """
     for zero_param, param in zip(sharded_model.parameters(), other_model.parameters()):
         assert hasattr(zero_param, 'col_attr')
-        shard_flag = zero_param.col_attr.data.is_sharded
+        shard_flag = zero_param.col_attr.sharded_data_tensor.is_sharded
         if shard_flag:
-            sharded_model.shard_strategy.gather([zero_param.col_attr.data])
-        param.data = copy.deepcopy(zero_param.col_attr.data.payload)
+            sharded_model.shard_strategy.gather([zero_param.col_attr.sharded_data_tensor])
+        param.data = copy.deepcopy(zero_param.col_attr.sharded_data_tensor.payload)
         if shard_flag:
-            sharded_model.shard_strategy.shard([zero_param.col_attr.data])
+            sharded_model.shard_strategy.shard([zero_param.col_attr.sharded_data_tensor])

colossalai/zero/sharded_optim/sharded_optim_v2.py

@@ -109,17 +109,17 @@ class ShardedOptimizerV2(ColossalaiOptimizer):
         for group in self.optim.param_groups:
             for p in group['params']:
                 assert hasattr(p, 'col_attr'), 'The parameter must be wrapped with ShardedParam'
-                is_param_sharded = p.col_attr.data.is_sharded
+                is_param_sharded = p.col_attr.sharded_data_tensor.is_sharded
                 if not is_param_sharded:
                     # TODO (ver217): we may not use shard / gather here
                     # Param is no sharded, which means we use ZeRO-2 here
                     # As we only store param shard, we shard it here
-                    self.shard_strategy.shard([p.col_attr.data], self.dp_process_group)
-                self.master_params[p] = cast_tensor_to_fp32(p.col_attr.data.payload).to(self.device)
+                    self.shard_strategy.shard([p.col_attr.sharded_data_tensor], self.dp_process_group)
+                self.master_params[p] = cast_tensor_to_fp32(p.col_attr.sharded_data_tensor.payload).to(self.device)
                 if not is_param_sharded:
                     # In this branch, there's no need to shard param
                     # So we gather here
-                    self.shard_strategy.gather([p.col_attr.data], self.dp_process_group)
+                    self.shard_strategy.gather([p.col_attr.sharded_data_tensor], self.dp_process_group)
 
     def step(self, *args, **kwargs):
         # unscale grads if scaled
@@ -149,24 +149,24 @@ class ShardedOptimizerV2(ColossalaiOptimizer):
         # a chunk.
         for group in self.optim.param_groups:
             for p in group['params']:
-                is_param_sharded = p.col_attr.data.is_sharded
+                is_param_sharded = p.col_attr.sharded_data_tensor.is_sharded
                 if not is_param_sharded:
                     # We use ZeRO-2 here
-                    # The `p.col_attr.data` saves full fp16 param
+                    # The `p.col_attr.sharded_data_tensor` saves full fp16 param
                     # But we only have updated fp32 param shard here
                     # So we first shard full fp16 param and copy fp32 param shard to it
                     # Then we will gather them
-                    self.shard_strategy.shard([p.col_attr.data], self.dp_process_group)
+                    self.shard_strategy.shard([p.col_attr.sharded_data_tensor], self.dp_process_group)
                 # We have to use `copy_payload` instead of `reset_payload`
-                # Since p.data is fp32 and p.col_attr.data is fp16
+                # Since p.data is fp32 and p.col_attr.sharded_data_tensor is fp16
 
                 # TODO() optimize this line CPU (fp32) -> GPU (fp16)
-                p.col_attr.data.copy_payload(p.data)
+                p.col_attr.sharded_data_tensor.copy_payload(p.data)
 
                 if not is_param_sharded:
                     # We gather full fp16 param here
-                    self.shard_strategy.gather([p.col_attr.data], self.dp_process_group)
-                p.data = p.col_attr.data.payload
+                    self.shard_strategy.gather([p.col_attr.sharded_data_tensor], self.dp_process_group)
+                p.data = p.col_attr.sharded_data_tensor.payload
         return ret
 
     def backward(self, loss: Tensor) -> None:

colossalai/zero/sharded_param/__init__.py

@@ -1,4 +1,4 @@
 from colossalai.zero.sharded_param.sharded_tensor import ShardedTensor
-from colossalai.zero.sharded_param.sharded_param import ShardedParam, ShardedParamV2
+from colossalai.zero.sharded_param.sharded_param import ShardedParamV2
 
-__all__ = ['ShardedParam', 'ShardedTensor', 'ShardedParamV2']
+__all__ = ['ShardedTensor', 'ShardedParamV2']

colossalai/zero/sharded_param/sharded_param.py

@@ -1,12 +1,7 @@
-from typing import Optional, Tuple, Union
-
-import numpy
 import torch
 import torch.distributed as dist
-from colossalai.context.parallel_mode import ParallelMode
-from colossalai.core import global_context as gpc
-from colossalai.zero.sharded_model._zero3_utils import get_shard
 from colossalai.zero.sharded_param import ShardedTensor
+from typing import Optional
 
 
 class ShardedParamV2(object):
@@ -15,7 +10,7 @@ class ShardedParamV2(object):
                  param: torch.nn.Parameter,
                  process_group: Optional[dist.ProcessGroup] = None,
                  rm_torch_payload=False) -> None:
-        self._data_sharded_tensor: ShardedTensor = ShardedTensor(param.data, process_group)
+        self._sharded_data_tensor: ShardedTensor = ShardedTensor(param.data, process_group)
         self.fp16_grad: Optional[torch.Tensor] = None
         self.fp32_grad: Optional[torch.Tensor] = None
 
@@ -37,105 +32,9 @@ class ShardedParamV2(object):
         self.param.data = torch.empty([], dtype=self.param.dtype, device=self.param.device)
 
     @property
-    def data(self):
-        return self._data_sharded_tensor
+    def sharded_data_tensor(self):
+        return self._sharded_data_tensor
 
     @property
     def param_is_sharded(self):
-        return self._data_sharded_tensor.is_sharded
-
-
-class ShardedParam(object):
-    r"""
-    A wrapper to torch.nn.Parameter. Shard a param
-    on memory space of different processes.
-    """
-
-    def __init__(self,
-                 other: Union[torch.nn.Parameter, Tuple[int, ...]],
-                 process_group: Optional[dist.ProcessGroup] = None,
-                 is_sharded: bool = False,
-                 device: Optional[torch.device] = None) -> None:
-        r"""
-        other: either an existing torch parameter or a tuple, indicate allocate a new param with the tuple as shape.
-        process_group: the process group storing the shared data.
-        is_sharded: is shared the param during __init__.
-        device: the device to place param data payload on
-        """
-        self.process_group = process_group or gpc.get_group(ParallelMode.DATA)
-        self.world_size = dist.get_world_size(self.process_group)
-        self.local_rank = dist.get_rank(self.process_group)
-        self.is_sharded = False
-        self.device = device
-
-        # Hijack the data payload of param
-        if isinstance(other, torch.nn.Parameter):
-            self._param_payload = other.data.to(device)
-            self._origin_shape = other.shape
-            self._origin_numel = other.numel()
-            if is_sharded:
-                self.shard()
-        elif isinstance(other, tuple):
-            self._origin_shape = other
-            self._origin_numel = numpy.prod(other)
-
-            # TODO(jiaruifang) can be optimized. Directly allocate payload as the sharded shape.
-            assert device is not None, "You have to assign a device to initialize a ShardParam from a shape tuple"
-            self._param_payload = torch.empty(self._origin_shape, device=device)
-            if is_sharded:
-                self.shard()
-        else:
-            raise RuntimeError(f"Initialize ShardParam failed. The 2nd parameter is wrong type {type(other)}")
-
-        self._payload_numel = None
-
-    def payload(self, target_device: Optional[torch.device] = None):
-        r"""
-        get the payload and move it to target device
-        """
-        if target_device is not None:
-            return self._param_payload.to(target_device)
-        return self._param_payload
-
-    def set_payload(self, data: torch.Tensor):
-        r"""
-        set payload as data
-        """
-        assert self._param_payload.shape == data.shape
-        self._param_payload.copy_(data)
-
-    def shard(self):
-        r"""
-        Distributed the payload of param to all processes.
-        """
-        if self.is_sharded:
-            return
-        self._param_payload, _ = get_shard(self._param_payload, self.local_rank, self.world_size)
-        self.is_sharded = True
-
-    def gather(self):
-        r"""
-        Collect the payload of param from different processes to process of local rank.
-        The payload has to be moved to cuda memory before communication.
-        """
-        if not self.is_sharded:
-            return
-
-        buffer_list = []
-        payload_numel = self._param_payload.numel()
-        for i in range(self.world_size):
-            if i == self.local_rank:
-                buffer_list.append(self._param_payload.cuda())
-            else:
-                buffer_list.append(torch.zeros(payload_numel).cuda())
-
-        torch.distributed.all_gather(buffer_list,
-                                     buffer_list[self.local_rank],
-                                     group=self.process_group,
-                                     async_op=False)
-        self._param_payload = torch.narrow(torch.cat(buffer_list), 0, 0, self._origin_numel).view(self._origin_shape)
-        self.is_sharded = False
-
-    @property
-    def origin_dtype(self):
-        return self._origin_dtype
+        return self._sharded_data_tensor.is_sharded

tests/test_utils/test_bucket_tensor_copy.py

@@ -17,7 +17,6 @@ def test_bucket_copy():
     for shape in shape_list:
         # on CPU
         src_param = torch.nn.Parameter(torch.randn(shape, dtype=torch.float, device=torch.device('cpu')))
-        print(src_param)
         # on GPU
         tgt_param = ShardedParamV2(torch.nn.Parameter(torch.ones(shape, dtype=torch.half, device=torch.device('cuda'))))
 
@@ -29,9 +28,10 @@ def test_bucket_copy():
     copyer.flush()
 
     for src_param, tgt_param in zip(src_param_list, tgt_param_list):
-        print(tgt_param.data.payload)
-        diff = src_param.cpu().float() - tgt_param.data.payload.cpu().float()
-        assert torch.allclose(src_param.cpu().float(), tgt_param.data.payload.cpu().float(), rtol=1e-03,
+        diff = src_param.cpu().float() - tgt_param.sharded_data_tensor.payload.cpu().float()
+        assert torch.allclose(src_param.cpu().float(),
+                              tgt_param.sharded_data_tensor.payload.cpu().float(),
+                              rtol=1e-03,
                               atol=1e-03), f"diff {diff}"
 
 

tests/test_zero_data_parallel/common.py

@@ -119,7 +119,7 @@ def check_params_padding(model, zero_model, loose=False):
 def check_sharded_params_padding(model, zero_model, loose=False):
     rank = dist.get_rank()
     for p, zero_p in zip(model.parameters(), zero_model.parameters()):
-        zero_p = zero_p.col_attr.data.payload.to(p.device).float()
+        zero_p = zero_p.col_attr.sharded_data_tensor.payload.to(p.device).float()
         chunks = torch.flatten(p).chunk(dist.get_world_size())
         if rank >= len(chunks):
             continue

tests/test_zero_data_parallel/test_init_context.py

@@ -34,10 +34,10 @@ def run_model_test(init_device, shard_strategy_class):
 
         for param in model.parameters():
             assert hasattr(param, 'col_attr')
-            assert param.col_attr.data.dtype == torch.half
-            assert param.col_attr.data.is_sharded
-            assert param.col_attr.data.payload.device.type == init_device.type, \
-                f'{param.col_attr.data.payload.device.type} vs. {init_device.type}'
+            assert param.col_attr.sharded_data_tensor.dtype == torch.half
+            assert param.col_attr.sharded_data_tensor.is_sharded
+            assert param.col_attr.sharded_data_tensor.payload.device.type == init_device.type, \
+                f'{param.col_attr.sharded_data_tensor.payload.device.type} vs. {init_device.type}'
 
     print(f'cuda usgae {GLOBAL_MODEL_DATA_TRACER.cuda_usage}')
     print(f'numel {model_numel_tensor}')

tests/test_zero_data_parallel/test_shard_param.py

@@ -1,6 +1,3 @@
-#!/usr/bin/env python
-# -*- encoding: utf-8 -*-
-
 from copy import deepcopy
 from functools import partial
 
@@ -8,13 +5,11 @@ import colossalai
 import pytest
 import torch
 import torch.multiprocessing as mp
-from colossalai.logging import disable_existing_loggers, get_dist_logger
 from colossalai.testing import parameterize
 from colossalai.utils import free_port
 from colossalai.zero.shard_utils import (BucketTensorShardStrategy, TensorShardStrategy)
-from colossalai.zero.sharded_param import ShardedParam, ShardedTensor
+from colossalai.zero.sharded_param import ShardedTensor
 from colossalai.zero.sharded_param.sharded_param import ShardedParamV2
-from tests.components_to_test.registry import non_distributed_component_funcs
 from tests.test_zero_data_parallel.common import CONFIG, allclose
 
 
@@ -52,7 +47,7 @@ def _run_shard_param_v2(rank, world_size, port):
     param_ref = deepcopy(param)
     sparam = ShardedParamV2(param=param, process_group=None)
 
-    allclose(sparam.data.payload, param_ref.data)
+    allclose(sparam.sharded_data_tensor.payload, param_ref.data)
 
     sparam.remove_torch_payload()
     assert (param.data.numel() == 1)
@@ -65,69 +60,6 @@ def test_shard_param_v2(world_size):
     mp.spawn(run_func, nprocs=world_size)
 
 
-def _run_test_shard_param(rank, world_size, port):
-    colossalai.launch(config=CONFIG, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
-
-    param = torch.nn.Parameter(torch.randn(2, 3))
-    param_ref = deepcopy(param)
-    sparam = ShardedParamV2(param=param, process_group=None)
-    print(sparam.data)
-    print(param_ref.data)
-
-    logger = get_dist_logger()
-    for get_components_func in non_distributed_component_funcs:
-        model_builder, *_ = get_components_func()
-        model = model_builder(checkpoint=True)
-        # add an attribute as col_attr to hijack the access to param.data
-        for _, param in model.named_parameters():
-            numel_ref = (param.numel() + world_size - 1) // world_size
-            param.col_attr = ShardedParam(param)
-            param.col_attr.shard()
-            param_data = param.col_attr.payload(torch.device('cpu'))
-            assert (numel_ref == param_data.numel())
-
-        for _, param in model.named_parameters():
-            param.col_attr.gather()
-            param_data = param.col_attr.payload(torch.device('cpu'))
-
-        disable_existing_loggers([logger])
-
-
-@pytest.mark.dist
-@pytest.mark.parametrize("world_size", [1, 2])
-def test_shard_param(world_size):
-    run_func = partial(_run_test_shard_param, world_size=world_size, port=free_port())
-    mp.spawn(run_func, nprocs=world_size)
-
-
-def _run_init_shard_param(rank, world_size, port):
-    colossalai.launch(config=CONFIG, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
-    param = torch.nn.Parameter(data=torch.rand(world_size, 3))
-    sparam = ShardedParam(param, None, True)
-    payload = sparam.payload(torch.device('cuda'))
-    assert (list(payload.shape) == [3])
-    del sparam
-
-    param_shape = (world_size, 3)
-    sparam = ShardedParam(param_shape, process_group=None, is_sharded=True, device=torch.device('cpu'))
-    payload = sparam.payload(torch.device('cuda'))
-    assert (list(payload.shape) == [3])
-
-    param_shape = (world_size, 3)
-    sparam = ShardedParam(param_shape, process_group=None, is_sharded=False, device=torch.device('cpu'))
-    payload = sparam.payload(torch.device('cuda'))
-    assert (list(payload.shape) == [world_size, 3])
-
-
-@pytest.mark.dist
-@pytest.mark.parametrize("world_size", [1, 4])
-def test_init_shard_param(world_size):
-    run_func = partial(_run_init_shard_param, world_size=world_size, port=free_port())
-    mp.spawn(run_func, nprocs=world_size)
-
-
 if __name__ == '__main__':
     test_shard_tensor(2)
-    test_shard_param(2)
     test_shard_param_v2(2)
-    test_init_shard_param(4)