Repository: hpcaitech/ColossalAI

Issue:
[tensor] fix some unittests

[tensor] fix some unittests

[tensor] fix some unittests



Discussion:

Resolving patch:
diff --git a/colossalai/auto_parallel/tensor_shard/node_handler/strategy/matmul_strategy_generator.py b/colossalai/auto_parallel/tensor_shard/node_handler/strategy/matmul_strategy_generator.py
--- a/colossalai/auto_parallel/tensor_shard/node_handler/strategy/matmul_strategy_generator.py
+++ b/colossalai/auto_parallel/tensor_shard/node_handler/strategy/matmul_strategy_generator.py
@@ -247,12 +247,12 @@ def collate_strategies(self) -> List[ShardingStrategy]:
         strategies.append(self.split_rhs_space_both_contract(1, 0))
 
         # RR= RS x SR
-        # strategies.append(self.recompute_split_both_contract(0))
-        # strategies.append(self.recompute_split_both_contract(1))
+        strategies.append(self.recompute_split_both_contract(0))
+        strategies.append(self.recompute_split_both_contract(1))
 
-        # # RS = RR x RS
-        # strategies.append(self.split_rhs_space_only(0))
-        # strategies.append(self.split_rhs_space_only(1))
+        # RS = RR x RS
+        strategies.append(self.split_rhs_space_only(0))
+        strategies.append(self.split_rhs_space_only(1))
 
         # S01R = S01R x RR
         strategies.append(self.split_lhs_1st_dim_1d(0, 1))
@@ -263,8 +263,8 @@ def collate_strategies(self) -> List[ShardingStrategy]:
         # RS01 = RR x RS01
         strategies.append(self.split_rhs_2nd_dim_1d(0, 1))
 
-        # # RR = RR x RR
-        # strategies.append(self.non_split())
+        # RR = RR x RR
+        strategies.append(self.non_split())
 
         return strategies
 
diff --git a/colossalai/auto_parallel/tensor_shard/solver/cost_graph.py b/colossalai/auto_parallel/tensor_shard/solver/cost_graph.py
--- a/colossalai/auto_parallel/tensor_shard/solver/cost_graph.py
+++ b/colossalai/auto_parallel/tensor_shard/solver/cost_graph.py
@@ -62,9 +62,6 @@ def _build_cost_graph(self):
                         else:
                             edge_cost[(j, i)] = resharding_cost_item.total
                 self.edge_costs[node_pair] = edge_cost
-            # add parents and children attribute to node
-            # parent_nodes = [node for node in strategies_vector.predecessor_nodes]
-            # children_nodes = [node for node in strategies_vector.successor_nodes]
             parent_nodes = []
             children_nodes = []
 


Test patch:
diff --git a/tests/test_auto_parallel/test_tensor_shard/test_bias_addition_forward.py b/tests/test_auto_parallel/test_tensor_shard/test_bias_addition_forward.py
--- a/tests/test_auto_parallel/test_tensor_shard/test_bias_addition_forward.py
+++ b/tests/test_auto_parallel/test_tensor_shard/test_bias_addition_forward.py
@@ -4,21 +4,11 @@
 import torch
 import torch.multiprocessing as mp
 
-from colossalai.auto_parallel.passes.runtime_apply_pass import runtime_apply_pass
-from colossalai.auto_parallel.passes.runtime_preparation_pass import runtime_preparation_pass
-from colossalai.auto_parallel.tensor_shard.sharding_strategy import OperationDataType
-from colossalai.auto_parallel.tensor_shard.solver import (
-    CostGraph,
-    GraphAnalyser,
-    Solver,
-    SolverOptions,
-    StrategiesConstructor,
-)
+from colossalai.auto_parallel.tensor_shard.initialize import initialize_model
 from colossalai.device.device_mesh import DeviceMesh
-from colossalai.fx import ColoGraphModule, ColoTracer
 from colossalai.initialize import launch
 from colossalai.logging import disable_existing_loggers
-from colossalai.testing import assert_close, assert_close_loose, rerun_if_address_is_in_use
+from colossalai.testing import assert_close, rerun_if_address_is_in_use
 from colossalai.testing.pytest_wrapper import run_on_environment_flag
 from colossalai.utils import free_port
 
@@ -63,42 +53,9 @@ def check_linear_module(rank, world_size, port):
     # [[0, 1]
     #  [2, 3]]
     device_mesh = DeviceMesh(physical_mesh_id, mesh_shape, init_process_group=True)
-    tracer = ColoTracer()
-    # graph():
-    #     %x : torch.Tensor [#users=1] = placeholder[target=x]
-    #     %linear_weight : [#users=1] = get_attr[target=linear.weight]
-    #     %linear_bias : [#users=1] = get_attr[target=linear.bias]
-    #     %linear : [#users=1] = call_function[target=torch._C._nn.linear](args = (%x, %linear_weight), kwargs = {})
-    #     %add : [#users=1] = call_function[target=operator.add](args = (%linear, %linear_bias), kwargs = {})
-    #     %mul : [#users=1] = call_function[target=operator.mul](args = (%add, 2), kwargs = {})
-    #     return mul
-    graph = tracer.trace(root=model, meta_args={'x': torch.rand(4, 4).to('meta')})
-    # def forward(self, x : torch.Tensor):
-    #     linear_weight = self.linear.weight
-    #     linear_bias = self.linear.bias
-    #     linear = torch._C._nn.linear(x, linear_weight);  x = linear_weight = None
-    #     add = linear + linear_bias;  linear = linear_bias = None
-    #     mul = add * 2;  add = None
-    #     return mul
-    gm = ColoGraphModule(model, graph)
-    gm.recompile()
-    node_list = list(graph.nodes)
-
-    solver_options = SolverOptions()
-    strategies_constructor = StrategiesConstructor(graph, device_mesh, solver_options)
-    strategies_constructor.build_strategies_and_cost()
-    linear_node = node_list[3]
-    cost_graph = CostGraph(strategies_constructor.leaf_strategies)
-    cost_graph.simplify_graph()
-    graph_analyser = GraphAnalyser(gm)
-    solver = Solver(gm.graph, strategies_constructor, cost_graph, graph_analyser)
-    ret = solver.call_solver_serialized_args()
-    solution = list(ret[0])
-    gm, sharding_spec_dict, origin_spec_dict, comm_actions_dict = runtime_preparation_pass(gm, solution, device_mesh)
-
-    gm = runtime_apply_pass(gm)
-    gm.recompile()
-    output = gm(input, sharding_spec_dict, origin_spec_dict, comm_actions_dict)
+    meta_args = {'x': torch.rand(4, 4).to('meta')}
+    gm = initialize_model(model, meta_args=meta_args, device_mesh=device_mesh)
+    output = gm(input)
     assert_close(output, output_compare)
 
 
@@ -113,47 +70,9 @@ def check_conv_module(rank, world_size, port):
     # [[0, 1]
     #  [2, 3]]
     device_mesh = DeviceMesh(physical_mesh_id, mesh_shape, init_process_group=True)
-    tracer = ColoTracer()
-    # graph():
-    #     %x : torch.Tensor [#users=1] = placeholder[target=x]
-    #     %conv_weight : [#users=1] = get_attr[target=conv.weight]
-    #     %conv_bias : [#users=1] = get_attr[target=conv.bias]
-    #     %conv2d : [#users=1] = call_function[target=torch.conv2d](args = (%x, %conv_weight), kwargs = {})
-    #     %view : [#users=1] = call_method[target=view](args = (%conv_bias, [1, -1, 1, 1]), kwargs = {})
-    #     %add : [#users=1] = call_function[target=operator.add](args = (%conv2d, %view), kwargs = {})
-    #     %mul : [#users=1] = call_function[target=operator.mul](args = (%add, 2), kwargs = {})
-    #     return mul
-    graph = tracer.trace(root=model, meta_args={'x': torch.rand(4, 3, 64, 64).to('meta')})
-    # def forward(self, x : torch.Tensor):
-    #     conv_weight = self.conv.weight
-    #     conv_bias = self.conv.bias
-    #     conv2d = torch.conv2d(x, conv_weight);  x = conv_weight = None
-    #     view = conv_bias.view([1, -1, 1, 1]);  conv_bias = None
-    #     add = conv2d + view;  conv2d = view = None
-    #     mul = add * 2;  add = None
-    #     return mul
-    gm = ColoGraphModule(model, graph)
-
-    gm.recompile()
-
-    node_list = list(graph.nodes)
-    conv_node = node_list[3]
-    solver_options = SolverOptions()
-    strategies_constructor = StrategiesConstructor(graph, device_mesh, solver_options)
-    strategies_constructor.build_strategies_and_cost()
-
-    cost_graph = CostGraph(strategies_constructor.leaf_strategies)
-    cost_graph.simplify_graph()
-    graph_analyser = GraphAnalyser(gm)
-    solver = Solver(gm.graph, strategies_constructor, cost_graph, graph_analyser)
-    ret = solver.call_solver_serialized_args()
-    solution = list(ret[0])
-
-    gm, sharding_spec_dict, origin_spec_dict, comm_actions_dict = runtime_preparation_pass(gm, solution, device_mesh)
-
-    gm = runtime_apply_pass(gm)
-    gm.recompile()
-    output = gm(input, sharding_spec_dict, origin_spec_dict, comm_actions_dict)
+    meta_args = {'x': torch.rand(4, 3, 64, 64).to('meta')}
+    gm = initialize_model(model, meta_args=meta_args, device_mesh=device_mesh)
+    output = gm(input)
     assert_close(output, output_compare)
 
 
diff --git a/tests/test_auto_parallel/test_tensor_shard/test_gpt/test_gpt2_performance.py b/tests/test_auto_parallel/test_tensor_shard/test_gpt/test_gpt2_performance.py
deleted file mode 100644
--- a/tests/test_auto_parallel/test_tensor_shard/test_gpt/test_gpt2_performance.py
+++ /dev/null
@@ -1,131 +0,0 @@
-import copy
-import random
-from functools import partial
-from time import time
-from typing import Dict, Optional, Tuple, Union
-
-import numpy as np
-import psutil
-import pytest
-import torch
-import torch.multiprocessing as mp
-import torch.nn as nn
-import transformers
-from torch.fx import GraphModule
-from torch.profiler import ProfilerActivity, profile, record_function, schedule, tensorboard_trace_handler
-
-from colossalai.auto_parallel.passes.runtime_apply_pass import runtime_apply_pass
-from colossalai.auto_parallel.passes.runtime_preparation_pass import runtime_preparation_pass
-from colossalai.auto_parallel.tensor_shard.constants import BATCHNORM_MODULE_OP
-from colossalai.auto_parallel.tensor_shard.initialize import autoparallelize, initialize_model
-from colossalai.auto_parallel.tensor_shard.sharding_strategy import ShardingSpec
-from colossalai.auto_parallel.tensor_shard.solver import (
-    CostGraph,
-    GraphAnalyser,
-    Solver,
-    SolverOptions,
-    StrategiesConstructor,
-)
-from colossalai.device.device_mesh import DeviceMesh
-from colossalai.fx.tracer.tracer import ColoTracer
-from colossalai.initialize import launch, launch_from_torch
-from colossalai.logging import disable_existing_loggers, get_dist_logger
-from colossalai.tensor.shape_consistency import ShapeConsistencyManager, to_global
-from colossalai.testing import assert_close, assert_close_loose, parameterize, rerun_if_address_is_in_use
-from colossalai.testing.pytest_wrapper import run_on_environment_flag
-from colossalai.utils import free_port
-from tests.test_auto_parallel.test_tensor_shard.test_gpt.gpt_modules import GPT2LMHeadModel, GPTLMLoss
-
-BATCH_SIZE = 32
-SEQ_LENGTH = 256
-HIDDEN_DIM = 16384
-NUM_HEADS = 128
-NUM_LAYERS = 4
-VOCAB_SIZE = 50257
-NUM_STEPS = 10
-FP16 = True
-
-
-def get_cpu_mem():
-    return psutil.Process().memory_info().rss / 1024**2
-
-
-def get_gpu_mem():
-    return torch.cuda.memory_allocated() / 1024**2
-
-
-def get_mem_info(prefix=''):
-    return f'{prefix}GPU memory usage: {get_gpu_mem():.2f} MB, CPU memory usage: {get_cpu_mem():.2f} MB'
-
-
-def get_tflops(model_numel, batch_size, seq_len, step_time):
-    # Tflops_per_GPU = global_batch * global_numel * seq_len * 8 / #gpu
-    return model_numel * batch_size * seq_len * 8 / 1e12 / (step_time + 1e-12) / 4
-
-
-# Randomly Generated Data
-def get_data(batch_size, seq_len, vocab_size):
-    input_ids = torch.randint(0, vocab_size, (batch_size, seq_len), device=torch.cuda.current_device())
-    attention_mask = torch.ones_like(input_ids)
-    return input_ids, attention_mask
-
-
-def main():
-    disable_existing_loggers()
-    launch_from_torch(config={})
-    logger = get_dist_logger()
-    config = transformers.GPT2Config(n_position=SEQ_LENGTH, n_layer=NUM_LAYERS, n_head=NUM_HEADS, n_embd=HIDDEN_DIM)
-    if FP16:
-        model = GPT2LMHeadModel(config=config).half().to('cuda')
-    else:
-        model = GPT2LMHeadModel(config=config).to('cuda')
-    global_numel = sum([p.numel() for p in model.parameters()])
-
-    meta_input_sample = {
-        'input_ids': torch.zeros((BATCH_SIZE, SEQ_LENGTH), dtype=torch.int64).to('meta'),
-        'attention_mask': torch.zeros((BATCH_SIZE, SEQ_LENGTH), dtype=torch.int64).to('meta'),
-    }
-
-    physical_mesh_id = torch.arange(0, 4)
-    mesh_shape = (2, 2)
-    # [[0, 1]
-    #  [2, 3]]
-    device_mesh = DeviceMesh(physical_mesh_id, mesh_shape, init_process_group=True)
-
-    gm = initialize_model(model, meta_input_sample, device_mesh)
-
-    # build criterion
-    criterion = GPTLMLoss()
-
-    optimizer = torch.optim.Adam(gm.parameters(), lr=0.01)
-    logger.info(get_mem_info(prefix='After init model, '), ranks=[0])
-    get_tflops_func = partial(get_tflops, global_numel, BATCH_SIZE, SEQ_LENGTH)
-    torch.cuda.synchronize()
-    model.train()
-    # with profile(activities=[ProfilerActivity.CPU, ProfilerActivity.CUDA],
-    #              schedule=schedule(wait=1, warmup=2, active=2),
-    #              on_trace_ready=tensorboard_trace_handler(f'log/dummy_data/bs128_seq128_new'),
-    #              record_shapes=True,
-    #              profile_memory=True) as prof:
-    # with torch.profiler.profile(activities=[torch.profiler.ProfilerActivity.CPU, torch.profiler.ProfilerActivity.CUDA]) as prof:
-    for n in range(10):
-        # we just use randomly generated data here
-        input_ids, attn_mask = get_data(BATCH_SIZE, SEQ_LENGTH, VOCAB_SIZE)
-        optimizer.zero_grad()
-        start = time()
-        outputs = gm(input_ids, attn_mask)
-        loss = criterion(outputs, input_ids)
-        loss.backward()
-        optimizer.step()
-        # prof.step()
-        torch.cuda.synchronize()
-        step_time = time() - start
-        logger.info(
-            f'[{n+1}/{NUM_STEPS}] Loss:{loss.item():.3f}, Step time: {step_time:.3f}s, TFLOPS: {get_tflops_func(step_time):.3f}',
-            ranks=[0])
-    # print(prof.key_averages().table(sort_by="self_cuda_time_total", row_limit=10))
-    torch.cuda.synchronize()
-
-
-if __name__ == '__main__':
-    main()
diff --git a/tests/test_auto_parallel/test_tensor_shard/test_gpt/test_runtime_with_gpt_modules.py b/tests/test_auto_parallel/test_tensor_shard/test_gpt/test_runtime_with_gpt_modules.py
--- a/tests/test_auto_parallel/test_tensor_shard/test_gpt/test_runtime_with_gpt_modules.py
+++ b/tests/test_auto_parallel/test_tensor_shard/test_gpt/test_runtime_with_gpt_modules.py
@@ -1,32 +1,27 @@
 import copy
 import random
 from functools import partial
-from typing import Dict, Optional, Tuple, Union
+from typing import Dict
 
 import numpy as np
 import pytest
 import torch
 import torch.multiprocessing as mp
-import torch.nn as nn
 import transformers
 from torch.fx import GraphModule
 
-from colossalai.auto_parallel.passes.runtime_apply_pass import runtime_apply_pass
-from colossalai.auto_parallel.passes.runtime_preparation_pass import runtime_preparation_pass
-from colossalai.auto_parallel.tensor_shard.constants import BATCHNORM_MODULE_OP
-from colossalai.auto_parallel.tensor_shard.sharding_strategy import ShardingSpec
-from colossalai.auto_parallel.tensor_shard.solver import (
-    CostGraph,
-    GraphAnalyser,
-    Solver,
-    SolverOptions,
-    StrategiesConstructor,
+from colossalai.auto_parallel.tensor_shard.initialize import (
+    ModuleWrapper,
+    build_strategy_constructor,
+    solve_solution,
+    transform_to_sharded_model,
 )
+from colossalai.auto_parallel.tensor_shard.sharding_strategy import ShardingSpec
 from colossalai.device.device_mesh import DeviceMesh
 from colossalai.fx.tracer.tracer import ColoTracer
 from colossalai.initialize import launch
 from colossalai.logging import disable_existing_loggers
-from colossalai.tensor.shape_consistency import ShapeConsistencyManager, to_global
+from colossalai.tensor.shape_consistency import to_global
 from colossalai.testing import assert_close, assert_close_loose, parameterize, rerun_if_address_is_in_use
 from colossalai.testing.pytest_wrapper import run_on_environment_flag
 from colossalai.utils import free_port
@@ -49,6 +44,7 @@ def _check_module_grad(module: torch.nn.Module, origin_param_dict: Dict[str, tor
                        best_sharding_spec_dict: Dict[str, ShardingSpec]):
     for name, param in module.named_parameters():
         param_grad = param.grad
+        name = name.replace('module.', '')
         origin_param_grad = origin_param_dict[name].grad
         atoms = name.split('.')
         new_name = '_'.join(atoms)
@@ -115,30 +111,17 @@ def check_attention_layer(rank, model_cls, world_size, port):
     # [[0, 1]
     #  [2, 3]]
     device_mesh = DeviceMesh(physical_mesh_id, mesh_shape, init_process_group=True)
-    shape_consistency_manager = ShapeConsistencyManager()
-
     tracer = ColoTracer()
 
     graph = tracer.trace(root=model, meta_args=meta_input_sample)
     gm = GraphModule(model, graph, model.__class__.__name__)
     gm.recompile()
 
-    graph_analyser = GraphAnalyser(gm)
-    liveness_list = graph_analyser.liveness_analysis()
-    solver_options = SolverOptions()
-    strategies_constructor = StrategiesConstructor(graph, device_mesh, solver_options)
-    strategies_constructor.build_strategies_and_cost()
-
-    cost_graph = CostGraph(strategies_constructor.leaf_strategies)
-    cost_graph.simplify_graph()
-    solver = Solver(gm.graph, strategies_constructor, cost_graph, graph_analyser, memory_budget=-1)
-    ret = solver.call_solver_serialized_args()
-
-    solution = list(ret[0])
-    gm, sharding_spec_dict, origin_spec_dict, comm_actions_dict = runtime_preparation_pass(
-        gm, solution, device_mesh, strategies_constructor)
-    gm = runtime_apply_pass(gm)
-    gm.recompile()
+    strategies_constructor = build_strategy_constructor(graph, device_mesh)
+    solution = solve_solution(gm, strategies_constructor, memory_budget=-1)
+    gm, sharding_spec_dicts = transform_to_sharded_model(gm, solution, device_mesh, strategies_constructor)
+    gm = ModuleWrapper(gm, *sharding_spec_dicts)
+
     nodes = [strategies_vector.node for strategies_vector in strategies_constructor.leaf_strategies]
     best_sharding_spec_dict = {}
     for index, node in enumerate(nodes):
@@ -149,7 +132,7 @@ def check_attention_layer(rank, model_cls, world_size, port):
     origin_output = test_model(*test_input_sample)
     torch.cuda.set_rng_state(cuda_rng_state)
     torch.set_rng_state(cpu_rng_state)
-    output = gm(*input_sample, sharding_spec_dict, origin_spec_dict, comm_actions_dict)
+    output = gm(*input_sample)
     assert_close(output, origin_output, rtol=1e-03, atol=1e-03)
 
     #*******************backward starting*******************
@@ -174,16 +157,15 @@ def check_attention_layer(rank, model_cls, world_size, port):
     #*******************strategy selected*******************
     if rank == 0:
         print("*******************strategy selected*******************")
-        strategies_list = solver.last_s_val
         nodes = [strategies_vector.node for strategies_vector in strategies_constructor.leaf_strategies]
         computation_cost = 0
         communication_cost = 0
         memory_cost = 0
         for index, node in enumerate(nodes):
-            print(node.name, node.strategies_vector[strategies_list[index]].name)
-            computation_cost += node.strategies_vector[strategies_list[index]].compute_cost.total
-            communication_cost += node.strategies_vector[strategies_list[index]].communication_cost.total
-            node_memory_cost = node.strategies_vector[strategies_list[index]].memory_cost.total
+            print(node.name, node.strategies_vector[solution[index]].name)
+            computation_cost += node.strategies_vector[solution[index]].compute_cost.total
+            communication_cost += node.strategies_vector[solution[index]].communication_cost.total
+            node_memory_cost = node.strategies_vector[solution[index]].memory_cost.total
             if isinstance(node_memory_cost, tuple):
                 node_memory_cost = node_memory_cost[0]
             memory_cost += node_memory_cost.activation + node_memory_cost.parameter
diff --git a/tests/test_auto_parallel/test_tensor_shard/test_metainfo/utils.py b/tests/test_auto_parallel/test_tensor_shard/test_metainfo/utils.py
--- a/tests/test_auto_parallel/test_tensor_shard/test_metainfo/utils.py
+++ b/tests/test_auto_parallel/test_tensor_shard/test_metainfo/utils.py
@@ -57,7 +57,7 @@ def mem_test_for_node_strategy(rank: int,
             output_key]
 
         gm, sharding_spec_dict, origin_spec_dict, comm_actions_dict = runtime_preparation_pass(
-            gm, solution, device_mesh)
+            gm, solution, device_mesh, strategies_constructor)
         gm = runtime_apply_pass(gm)
         gm.recompile()
         gm: GraphModule
diff --git a/tests/test_auto_parallel/test_tensor_shard/test_resnet_block_runtime.py b/tests/test_auto_parallel/test_tensor_shard/test_resnet_block_runtime.py
deleted file mode 100644
--- a/tests/test_auto_parallel/test_tensor_shard/test_resnet_block_runtime.py
+++ /dev/null
@@ -1,270 +0,0 @@
-import copy
-from copy import deepcopy
-from functools import partial
-
-import pytest
-import torch
-import torch.multiprocessing as mp
-import torch.nn as nn
-from torch.fx import GraphModule
-from torchvision.models import resnet34, resnet50
-
-from colossalai import device
-from colossalai.auto_parallel.passes.runtime_apply_pass import runtime_apply_pass
-from colossalai.auto_parallel.passes.runtime_preparation_pass import runtime_preparation_pass
-from colossalai.auto_parallel.tensor_shard.constants import *
-from colossalai.auto_parallel.tensor_shard.solver.cost_graph import CostGraph
-from colossalai.auto_parallel.tensor_shard.solver.graph_analysis import GraphAnalyser
-from colossalai.auto_parallel.tensor_shard.solver.options import SolverOptions
-from colossalai.auto_parallel.tensor_shard.solver.solver import Solver
-from colossalai.auto_parallel.tensor_shard.solver.strategies_constructor import StrategiesConstructor
-from colossalai.device.device_mesh import DeviceMesh
-from colossalai.fx.tracer.tracer import ColoTracer
-from colossalai.initialize import launch
-from colossalai.logging import disable_existing_loggers
-from colossalai.testing import assert_close, assert_close_loose, rerun_if_address_is_in_use
-from colossalai.testing.pytest_wrapper import run_on_environment_flag
-from colossalai.utils import free_port
-
-seed = 128
-cudnn_benchmark = False
-cudnn_deterministic = True
-
-
-def conv3x3(in_planes: int, out_planes: int, stride: int = 1, groups: int = 1, dilation: int = 1) -> nn.Conv2d:
-    """3x3 convolution with padding"""
-    return nn.Conv2d(
-        in_planes,
-        out_planes,
-        kernel_size=3,
-        stride=stride,
-        padding=dilation,
-        groups=groups,
-        bias=False,
-        dilation=dilation,
-    )
-
-
-def conv1x1(in_planes: int, out_planes: int, stride: int = 1) -> nn.Conv2d:
-    """1x1 convolution"""
-    return nn.Conv2d(in_planes, out_planes, kernel_size=1, stride=stride, bias=False)
-
-
-class Bottleneck(nn.Module):
-    # Bottleneck in torchvision places the stride for downsampling at 3x3 convolution(self.conv2)
-    # while original implementation places the stride at the first 1x1 convolution(self.conv1)
-    # according to "Deep residual learning for image recognition"https://arxiv.org/abs/1512.03385.
-    # This variant is also known as ResNet V1.5 and improves accuracy according to
-    # https://ngc.nvidia.com/catalog/model-scripts/nvidia:resnet_50_v1_5_for_pytorch.
-
-    expansion: int = 4
-
-    def __init__(
-        self,
-        inplanes: int,
-        planes: int,
-        stride: int = 1,
-        downsample=None,
-        groups: int = 1,
-        base_width: int = 64,
-        dilation: int = 1,
-        norm_layer=None,
-    ) -> None:
-        super().__init__()
-        if norm_layer is None:
-            norm_layer = nn.BatchNorm2d
-        width = int(planes * (base_width / 64.0)) * groups
-        # Both self.conv2 and self.downsample layers downsample the input when stride != 1
-        self.conv1 = conv1x1(inplanes, width)
-        self.bn1 = norm_layer(width)
-        self.conv2 = conv3x3(width, width, stride, groups, dilation)
-        self.bn2 = norm_layer(width)
-        self.conv3 = conv1x1(width, planes * self.expansion)
-        self.bn3 = norm_layer(planes * self.expansion)
-        self.relu = nn.ReLU(inplace=True)
-        self.downsample = downsample
-        self.stride = stride
-
-    def forward(self, x):
-        identity = x
-
-        out = self.conv1(x)
-        out = self.bn1(out)
-        out = self.relu(out)
-
-        out = self.conv2(out)
-        out = self.bn2(out)
-        out = self.relu(out)
-
-        out = self.conv3(out)
-        out = self.bn3(out)
-
-        if self.downsample is not None:
-            identity = self.downsample(x)
-
-        out = self.relu(out)
-
-        return out
-
-
-def check_apply_bottleneck(rank, world_size, port):
-    disable_existing_loggers()
-    launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
-    input = torch.rand(4, 4, 4, 4).cuda()
-    physical_mesh_id = torch.arange(0, 4)
-    mesh_shape = (2, 2)
-    # [[0, 1]
-    #  [2, 3]]
-    device_mesh = DeviceMesh(physical_mesh_id, mesh_shape, init_process_group=True)
-
-    tracer = ColoTracer()
-    model = Bottleneck(4, 4, 1, norm_layer=torch.nn.modules.batchnorm.BatchNorm2d).cuda()
-    test_model = copy.deepcopy(model)
-    test_input = copy.deepcopy(input)
-    # graph():
-    #     %x : torch.Tensor [#users=1] = placeholder[target=x]
-    #     %conv1 : [#users=1] = call_module[target=conv1](args = (%x,), kwargs = {})
-    #     %bn1 : [#users=1] = call_module[target=bn1](args = (%conv1,), kwargs = {})
-    #     %relu : [#users=1] = call_module[target=relu](args = (%bn1,), kwargs = {})
-    #     %conv2 : [#users=1] = call_module[target=conv2](args = (%relu,), kwargs = {})
-    #     %bn2 : [#users=1] = call_module[target=bn2](args = (%conv2,), kwargs = {})
-    #     %relu_1 : [#users=1] = call_module[target=relu](args = (%bn2,), kwargs = {})
-    #     %conv3 : [#users=1] = call_module[target=conv3](args = (%relu_1,), kwargs = {})
-    #     %bn3 : [#users=1] = call_module[target=bn3](args = (%conv3,), kwargs = {})
-    #     %relu_2 : [#users=1] = call_module[target=relu](args = (%bn3,), kwargs = {})
-    #     return relu_2
-    input_sample = {'x': torch.rand(4, 4, 4, 4).to('meta')}
-
-    graph = tracer.trace(root=model, meta_args=input_sample)
-    gm = GraphModule(model, graph, model.__class__.__name__)
-    gm.recompile()
-    solver_options = SolverOptions()
-    strategies_constructor = StrategiesConstructor(graph, device_mesh, solver_options)
-    strategies_constructor.build_strategies_and_cost()
-
-    cost_graph = CostGraph(strategies_constructor.leaf_strategies)
-    cost_graph.simplify_graph()
-    graph_analyser = GraphAnalyser(gm)
-    solver = Solver(gm.graph, strategies_constructor, cost_graph, graph_analyser)
-    ret = solver.call_solver_serialized_args()
-    solution = list(ret[0])
-    print(solution)
-    for index, node in enumerate(graph.nodes):
-        print(node.name, node.strategies_vector[solution[index]].name)
-    gm, sharding_spec_dict, origin_spec_dict, comm_actions_dict = runtime_preparation_pass(gm, solution, device_mesh)
-    gm = runtime_apply_pass(gm)
-    gm.recompile()
-    nodes = [node for node in gm.graph.nodes]
-    # TODO: wrap the gm to avoid the influence of the user training code
-    cuda_rng_state = torch.cuda.get_rng_state()
-    origin_output = test_model(test_input)
-    torch.cuda.set_rng_state(cuda_rng_state)
-    output = gm(input, sharding_spec_dict, origin_spec_dict, comm_actions_dict)
-
-    assert output.shape == origin_output.shape
-    assert_close(output, origin_output, rtol=1e-03, atol=1e-05)
-    print("*******************backward starting*******************")
-    cuda_rng_state = torch.cuda.get_rng_state()
-    output.sum().backward()
-    torch.cuda.set_rng_state(cuda_rng_state)
-    origin_output.sum().backward()
-    if rank == 0:
-        print(
-            f"bn3 diff sum in rank {rank}: {(gm.bn3.weight.grad - test_model.bn3.weight.grad.narrow(0, 0, 4)).abs().sum()}"
-        )
-        print(
-            f"conv3 diff sum in rank {rank}: {(gm.conv3.weight.grad - test_model.conv3.weight.grad.narrow(0, 0, 8)).abs().sum()}"
-        )
-        print(
-            f"bn2 diff sum in rank {rank}: {(gm.bn2.weight.grad - test_model.bn2.weight.grad.narrow(0, 0, 2)).abs().sum()}"
-        )
-        print(
-            f"conv2 diff sum in rank {rank}: {(gm.conv2.weight.grad - test_model.conv2.weight.grad.narrow(0, 0, 2)).abs().sum()}"
-        )
-        print(
-            f"bn1 diff sum in rank {rank}: {(gm.bn1.weight.grad - test_model.bn1.weight.grad.narrow(0, 0, 1)).abs().sum()}"
-        )
-        print(f"conv1 diff sum in rank {rank}: {(gm.conv1.weight.grad - test_model.conv1.weight.grad).sum()}")
-
-        assert_close_loose(gm.conv3.weight.grad.sum(), test_model.conv3.weight.grad.narrow(0, 0, 8).sum())
-        assert_close_loose(gm.conv2.weight.grad.sum(), test_model.conv2.weight.grad.narrow(0, 0, 2).sum())
-        assert_close_loose(gm.conv1.weight.grad.sum(), test_model.conv1.weight.grad.sum())
-
-    if rank == 1:
-        print(
-            f"bn3 diff sum in rank {rank}: {(gm.bn3.weight.grad - test_model.bn3.weight.grad.narrow(0, 4, 4)).abs().sum()}"
-        )
-        print(
-            f"conv3 diff sum in rank {rank}: {(gm.conv3.weight.grad - test_model.conv3.weight.grad.narrow(0, 0, 8)).abs().sum()}"
-        )
-        print(
-            f"bn2 diff sum in rank {rank}: {(gm.bn2.weight.grad - test_model.bn2.weight.grad.narrow(0, 2, 2)).abs().sum()}"
-        )
-        print(
-            f"conv2 diff sum in rank {rank}: {(gm.conv2.weight.grad - test_model.conv2.weight.grad.narrow(0, 2, 2)).abs().sum()}"
-        )
-        print(
-            f"bn1 diff sum in rank {rank}: {(gm.bn1.weight.grad - test_model.bn1.weight.grad.narrow(0, 1, 1)).abs().sum()}"
-        )
-        print(f"conv1 diff sum in rank {rank}: {(gm.conv1.weight.grad - test_model.conv1.weight.grad).sum()}")
-
-        assert_close_loose(gm.conv3.weight.grad.sum(), test_model.conv3.weight.grad.narrow(0, 0, 8).sum())
-        assert_close_loose(gm.conv2.weight.grad.sum(), test_model.conv2.weight.grad.narrow(0, 2, 2).sum())
-        assert_close_loose(gm.conv1.weight.grad.sum(), test_model.conv1.weight.grad.sum())
-
-    if rank == 2:
-        print(
-            f"bn3 diff sum in rank {rank}: {(gm.bn3.weight.grad - test_model.bn3.weight.grad.narrow(0, 8, 4)).abs().sum()}"
-        )
-        print(
-            f"conv3 diff sum in rank {rank}: {(gm.conv3.weight.grad - test_model.conv3.weight.grad.narrow(0, 8, 8)).abs().sum()}"
-        )
-        print(
-            f"bn2 diff sum in rank {rank}: {(gm.bn2.weight.grad - test_model.bn2.weight.grad.narrow(0, 0, 2)).abs().sum()}"
-        )
-        print(
-            f"conv2 diff sum in rank {rank}: {(gm.conv2.weight.grad - test_model.conv2.weight.grad.narrow(0, 0, 2)).abs().sum()}"
-        )
-        print(
-            f"bn1 diff sum in rank {rank}: {(gm.bn1.weight.grad - test_model.bn1.weight.grad.narrow(0, 2, 1)).abs().sum()}"
-        )
-        print(f"conv1 diff sum in rank {rank}: {(gm.conv1.weight.grad - test_model.conv1.weight.grad).sum()}")
-
-        assert_close_loose(gm.conv3.weight.grad.sum(), test_model.conv3.weight.grad.narrow(0, 8, 8).sum())
-        assert_close_loose(gm.conv2.weight.grad.sum(), test_model.conv2.weight.grad.narrow(0, 0, 2).sum())
-        assert_close_loose(gm.conv1.weight.grad.sum(), test_model.conv1.weight.grad.sum())
-
-    if rank == 3:
-        print(
-            f"bn3 diff sum in rank {rank}: {(gm.bn3.weight.grad - test_model.bn3.weight.grad.narrow(0, 12, 4)).abs().sum()}"
-        )
-        print(
-            f"conv3 diff sum in rank {rank}: {(gm.conv3.weight.grad - test_model.conv3.weight.grad.narrow(0, 8, 8)).abs().sum()}"
-        )
-        print(
-            f"bn2 diff sum in rank {rank}: {(gm.bn2.weight.grad - test_model.bn2.weight.grad.narrow(0, 2, 2)).abs().sum()}"
-        )
-        print(
-            f"conv2 diff sum in rank {rank}: {(gm.conv2.weight.grad - test_model.conv2.weight.grad.narrow(0, 2, 2)).abs().sum()}"
-        )
-        print(
-            f"bn1 diff sum in rank {rank}: {(gm.bn1.weight.grad - test_model.bn1.weight.grad.narrow(0, 3, 1)).abs().sum()}"
-        )
-        print(f"conv1 diff sum in rank {rank}: {(gm.conv1.weight.grad - test_model.conv1.weight.grad).sum()}")
-
-        assert_close_loose(gm.conv3.weight.grad.sum(), test_model.conv3.weight.grad.narrow(0, 8, 8).sum())
-        assert_close_loose(gm.conv2.weight.grad.sum(), test_model.conv2.weight.grad.narrow(0, 2, 2).sum())
-        assert_close_loose(gm.conv1.weight.grad.sum(), test_model.conv1.weight.grad.sum())
-
-
-@run_on_environment_flag(name='AUTO_PARALLEL')
-@pytest.mark.dist
-@rerun_if_address_is_in_use()
-def test_apply():
-    world_size = 4
-    run_func = partial(check_apply_bottleneck, world_size=world_size, port=free_port())
-    mp.spawn(run_func, nprocs=world_size)
-
-
-if __name__ == '__main__':
-    test_apply()
diff --git a/tests/test_auto_parallel/test_tensor_shard/test_shape_consistency_pass.py b/tests/test_auto_parallel/test_tensor_shard/test_shape_consistency_pass.py
--- a/tests/test_auto_parallel/test_tensor_shard/test_shape_consistency_pass.py
+++ b/tests/test_auto_parallel/test_tensor_shard/test_shape_consistency_pass.py
@@ -5,19 +5,9 @@
 import torch
 import torch.multiprocessing as mp
 import torch.nn as nn
-from torch.fx import GraphModule
-
-from colossalai.auto_parallel.passes.runtime_apply_pass import runtime_apply_pass
-from colossalai.auto_parallel.passes.runtime_preparation_pass import runtime_preparation_pass
-from colossalai.auto_parallel.tensor_shard.solver import (
-    CostGraph,
-    GraphAnalyser,
-    Solver,
-    SolverOptions,
-    StrategiesConstructor,
-)
+
+from colossalai.auto_parallel.tensor_shard.initialize import initialize_model
 from colossalai.device.device_mesh import DeviceMesh
-from colossalai.fx.tracer.tracer import ColoTracer
 from colossalai.initialize import launch
 from colossalai.logging import disable_existing_loggers
 from colossalai.testing import assert_close, rerun_if_address_is_in_use
@@ -41,41 +31,22 @@ def check_apply(rank, world_size, port):
     disable_existing_loggers()
     launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
     input = torch.rand(4, 4, 4, 4).cuda()
+    test_input = copy.deepcopy(input)
+    # graph():
+    #     %x : torch.Tensor [#users=1] = placeholder[target=x]
+    #     %conv : [#users=1] = call_module[target=conv](args = (%mul,), kwargs = {})
+    #     return conv
+    model = ConvModel(4, 4).cuda()
+    test_model = copy.deepcopy(model)
     physical_mesh_id = torch.arange(0, 4)
     mesh_shape = (2, 2)
     # [[0, 1]
     #  [2, 3]]
     device_mesh = DeviceMesh(physical_mesh_id, mesh_shape, init_process_group=True)
+    meta_args = {'x': torch.rand(4, 4, 4, 4).to('meta')}
+    gm = initialize_model(model, meta_args, device_mesh)
 
-    tracer = ColoTracer()
-    model = ConvModel(4, 4).cuda()
-    test_model = copy.deepcopy(model)
-    test_input = copy.deepcopy(input)
-
-    input_sample = {'x': torch.rand(4, 4, 4, 4).to('meta')}
-    # graph():
-    #     %x : torch.Tensor [#users=1] = placeholder[target=x]
-    #     %conv : [#users=1] = call_module[target=conv](args = (%mul,), kwargs = {})
-    #     return conv
-    graph = tracer.trace(root=model, meta_args=input_sample)
-    gm = GraphModule(model, graph, model.__class__.__name__)
-    gm.recompile()
-    solver_options = SolverOptions()
-    strategies_constructor = StrategiesConstructor(graph, device_mesh, solver_options)
-    strategies_constructor.build_strategies_and_cost()
-
-    cost_graph = CostGraph(strategies_constructor.leaf_strategies)
-    cost_graph.simplify_graph()
-    graph_analyser = GraphAnalyser(gm)
-    solver = Solver(gm.graph, strategies_constructor, cost_graph, graph_analyser)
-    ret = solver.call_solver_serialized_args()
-    solution = list(ret[0])
-    gm, sharding_spec_dict, origin_spec_dict, comm_actions_dict = runtime_preparation_pass(gm, solution, device_mesh)
-    gm = runtime_apply_pass(gm)
-    gm.recompile()
-    nodes = [node for node in gm.graph.nodes]
-    # TODO: wrap the gm to avoid the influence of the user training code
-    output = gm(input, sharding_spec_dict, origin_spec_dict, comm_actions_dict)
+    output = gm(input)
     origin_output = test_model(test_input)
     assert output.equal(origin_output)
     origin_loss = origin_output.sum()
@@ -84,13 +55,21 @@ def check_apply(rank, world_size, port):
     origin_loss.backward()
     loss.backward()
 
-    grad_0 = test_model.conv.weight.grad.narrow(0, 0, 2)
-    grad_1 = test_model.conv.weight.grad.narrow(0, 2, 2)
-
-    if rank in (0, 1):
-        assert_close(gm.conv.weight.grad.data, grad_0.data)
-    elif rank in (2, 3):
-        assert_close(gm.conv.weight.grad.data, grad_1.data)
+    grad_0 = test_model.conv.weight.grad.narrow(0, 0, 1)
+    grad_1 = test_model.conv.weight.grad.narrow(0, 1, 1)
+    grad_2 = test_model.conv.weight.grad.narrow(0, 2, 1)
+    grad_3 = test_model.conv.weight.grad.narrow(0, 3, 1)
+
+    if rank == 0:
+        assert_close(gm.module.conv.weight.grad.data, grad_0.data)
+    elif rank == 1:
+        assert_close(gm.module.conv.weight.grad.data, grad_1.data)
+    elif rank == 2:
+        assert_close(gm.module.conv.weight.grad.data, grad_2.data)
+    elif rank == 3:
+        assert_close(gm.module.conv.weight.grad.data, grad_3.data)
+    else:
+        raise ValueError(f'rank {rank} does not exist.')
 
 
 # skip this test due to pulp not installed in CI environment