[autoparallel] support linear function bias addition (#2104)

colossalai/fx/tracer/bias_addition_patch/patched_bias_addition_function/__init__.py

 from .addbmm import Addbmm
 from .addmm import Addmm
 from .bias_addition_function import BiasAdditionFunc, LinearBasedBiasFunc, func_to_func_dict, method_to_func_dict
+from .linear import Linear

colossalai/fx/tracer/bias_addition_patch/patched_bias_addition_function/bias_addition_function.py

@@ -106,6 +106,7 @@ class LinearBasedBiasFunc(BiasAdditionFunc):
 func_to_func_dict = {
     torch.addmm: F.linear,
     torch.addbmm: torch.bmm,
+    F.linear: F.linear,
 }
 
 method_to_func_dict = {

colossalai/fx/tracer/bias_addition_patch/patched_bias_addition_function/linear.py

+import operator
+
+import torch
+import torch.nn.functional as F
+
+from ...registry import bias_addition_function
+from .bias_addition_function import LinearBasedBiasFunc
+
+
+@bias_addition_function.register(F.linear)
+class Linear(LinearBasedBiasFunc):
+
+    def extract_kwargs_from_origin_func(self):
+        assert 'bias' in self.kwargs
+        kwargs = {}
+        if 'bias' in self.kwargs:
+            kwargs['bias'] = self.kwargs['bias']
+        return kwargs
+
+    def generate(self):
+        non_bias_linear_func_proxy = self.create_non_bias_func_proxy(self.args[0], self.args[1])
+        kwargs = self.extract_kwargs_from_origin_func()
+        bias_addition_proxy = self.create_bias_addition_proxy(non_bias_linear_func_proxy, kwargs['bias'])
+
+        return bias_addition_proxy

colossalai/fx/tracer/tracer.py

@@ -102,6 +102,11 @@ class ColoTracer(Tracer):
         handle = None
         if kind == "call_function":
             if bias_addition_function.has(target):
+                if target == torch.nn.functional.linear:
+                    if 'bias' in kwargs and kwargs['bias'] is not None:
+                        function_to_substitute = func_to_func_dict[target]
+                        handle = bias_addition_function.get(target)(self, target, args, kwargs, function_to_substitute)
+                else:
                     function_to_substitute = func_to_func_dict[target]
                     handle = bias_addition_function.get(target)(self, target, args, kwargs, function_to_substitute)
             elif bias_addition_function.has(target.__name__):

tests/test_auto_parallel/test_tensor_shard/test_node_handler/test_bias_linear_function_node.py

+from faulthandler import disable
+from functools import partial
+from xml.dom import WrongDocumentErr
+
+import pytest
+import torch
+import torch.multiprocessing as mp
+import torch.nn as nn
+import torch.nn.functional as F
+from typing_extensions import Self
+
+from colossalai.auto_parallel.tensor_shard.node_handler import LinearFunctionHandler, LinearModuleHandler
+from colossalai.auto_parallel.tensor_shard.sharding_strategy import (
+    OperationData,
+    OperationDataType,
+    ShardingStrategy,
+    StrategiesVector,
+)
+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, parameterize, rerun_if_address_is_in_use
+from colossalai.testing.pytest_wrapper import run_on_environment_flag
+from colossalai.testing.utils import parameterize
+from colossalai.utils import free_port
+from tests.test_auto_parallel.test_tensor_shard.test_node_handler.utils import numerical_test_for_node_strategy
+
+WEIGHT_SHAPE = (32, 16)
+
+
+class LinearModule(torch.nn.Module):
+
+    def __init__(self, weight_shape):
+        super().__init__()
+        self.weight = torch.nn.Parameter(torch.rand(*weight_shape))
+        self.bias = torch.nn.Parameter(torch.rand(weight_shape[0]))
+
+    def forward(self, x):
+        x = F.linear(x, self.weight, bias=self.bias)
+        return x
+
+
+def check_linear_module_handler(rank, world_size, port):
+    disable_existing_loggers()
+    launch(config={}, rank=rank, world_size=world_size, host='localhost', port=port, backend='nccl')
+    model = LinearModule(weight_shape=WEIGHT_SHAPE).cuda()
+
+    physical_mesh_id = torch.arange(0, 4)
+    mesh_shape = (2, 2)
+    device_mesh = DeviceMesh(physical_mesh_id, mesh_shape, init_process_group=True)
+    input = torch.rand(4, 4, 4, 16).cuda()
+    # the index of linear node in computation graph
+    node_index = 3
+    # strategy number of linear node
+    strategy_number = 24
+    # construct input args
+    input_args = [input]
+    # construct meta arg names
+    meta_arg_names = ['x']
+    numerical_test_for_node_strategy(model=model,
+                                     device_mesh=device_mesh,
+                                     node_index=node_index,
+                                     strategy_number=strategy_number,
+                                     input_args=input_args,
+                                     meta_arg_names=meta_arg_names,
+                                     node_type='bias_module')
+
+    tracer = ColoTracer()
+    # graph():
+    #     %x : torch.Tensor [#users=1] = placeholder[target=x]
+    #     %weight : [#users=1] = get_attr[target=weight]
+    #     %bias : [#users=1] = get_attr[target=bias]
+    #     %linear : [#users=1] = call_function[target=torch._C._nn.linear](args = (%x, %weight), kwargs = {})
+    #     %add : [#users=1] = call_function[target=operator.add](args = (%linear, %bias), kwargs = {})
+    #     return add
+    graph = tracer.trace(model, meta_args={"x": torch.rand(4, 4, 4, 16).to('meta')})
+    gm = ColoGraphModule(model, graph)
+
+    linear_mod_node = list(graph.nodes)[3]
+    strategies_vector = StrategiesVector(linear_mod_node)
+
+    # build handler
+    handler = LinearFunctionHandler(node=linear_mod_node, device_mesh=device_mesh, strategies_vector=strategies_vector)
+    # check operation data mapping
+    mapping = handler.get_operation_data_mapping()
+
+    for name, op_data in mapping.items():
+        op_data: OperationData
+        # make sure they have valid values
+        assert op_data.logical_shape is not None
+        assert op_data.data is not None
+
+    assert mapping['input'].name == "x"
+    assert mapping['input'].data.shape == torch.Size([4, 4, 4, 16])
+    assert mapping['input'].type == OperationDataType.ARG
+    assert mapping['input'].logical_shape == torch.Size([64, 16])
+
+    assert mapping['other'].name == "weight"
+    assert mapping['other'].data.shape == torch.Size([32, 16])
+    assert mapping['other'].type == OperationDataType.PARAM
+    assert mapping['other'].logical_shape == torch.Size([16, 32])
+
+    assert 'bias' not in mapping
+
+    assert mapping['output'].name == "linear"
+    assert mapping['output'].data.shape == torch.Size([4, 4, 4, 32])
+    assert mapping['output'].type == OperationDataType.OUTPUT
+
+    strategies_vector = handler.register_strategy(compute_resharding_cost=False)
+    strategy_name_list = [val.name for val in strategies_vector]
+
+    # SS = SR x RS
+    assert 'S0S1 = S0R x RS1_0' in strategy_name_list
+    assert 'S0S1 = S0R x RS1_1' in strategy_name_list
+    assert 'S0S1 = S0R x RS1_2' in strategy_name_list
+    assert 'S1S0 = S1R x RS0_0' in strategy_name_list
+    assert 'S1S0 = S1R x RS0_1' in strategy_name_list
+    assert 'S1S0 = S1R x RS0_2' in strategy_name_list
+
+    # SR = SS x SR
+    assert 'S0R = S0S1 x S1R_0' in strategy_name_list
+    assert 'S0R = S0S1 x S1R_1' in strategy_name_list
+    assert 'S0R = S0S1 x S1R_2' in strategy_name_list
+    assert 'S1R = S1S0 x S0R_0' in strategy_name_list
+    assert 'S1R = S1S0 x S0R_1' in strategy_name_list
+    assert 'S1R = S1S0 x S0R_2' in strategy_name_list
+
+    # RS = RS x SS
+    assert 'RS0 = RS1 x S1S0' in strategy_name_list
+    assert 'RS1 = RS0 x S0S1' in strategy_name_list
+
+    # RR = RS x SR
+    assert 'RR = RS0 x S0R' in strategy_name_list
+    assert 'RR = RS1 x S1R' in strategy_name_list
+
+    # RS= RR x RS
+    assert 'RS0 = RR x RS0' in strategy_name_list
+    assert 'RS1 = RR x RS1' in strategy_name_list
+
+    # S01R = S01R x RR
+    assert 'S01R = S01R x RR_0' in strategy_name_list
+    assert 'S01R = S01R x RR_1' in strategy_name_list
+    assert 'S01R = S01R x RR_2' in strategy_name_list
+
+    # RR = RS01 x S01R
+    assert 'RR = RS01 x S01R' in strategy_name_list
+
+    # RS01 = RR x RS01
+    assert 'RS01 = RR x RS01' in strategy_name_list
+
+    # RR = RR x RR
+    assert 'RR = RR x RR' in strategy_name_list
+
+    for strategy in strategies_vector:
+        strategy: ShardingStrategy
+        input_sharding_spec = strategy.get_sharding_spec_by_name('x')
+        weight_sharding_spec = strategy.get_sharding_spec_by_name('weight')
+        output_sharding_spec = strategy.get_sharding_spec_by_name('linear')
+
+        # make sure the sharding matches across different operation data
+        assert input_sharding_spec.sharding_sequence[:-1] == output_sharding_spec.sharding_sequence[:-1]
+        assert weight_sharding_spec.sharding_sequence[1] == input_sharding_spec.sharding_sequence[-1]
+        assert weight_sharding_spec.sharding_sequence[0] == output_sharding_spec.sharding_sequence[-1]
+
+
+@run_on_environment_flag(name='AUTO_PARALLEL')
+@pytest.mark.dist
+@rerun_if_address_is_in_use()
+def test_linear_handler():
+    world_size = 4
+    run_func_module = partial(check_linear_module_handler, world_size=world_size, port=free_port())
+    mp.spawn(run_func_module, nprocs=world_size)
+
+
+if __name__ == '__main__':
+    test_linear_handler()