[fx] add a symbolic_trace api. (#1812)

* [fx] add a symbolic_trace api.

* [fx] fix import errors.

colossalai/fx/__init__.py

 from ._compatibility import compatibility, is_compatible_with_meta
 from .graph_module import ColoGraphModule
 from .passes import MetaInfoProp
-from .tracer import ColoTracer, meta_trace
+from .tracer import ColoTracer, meta_trace, symbolic_trace

colossalai/fx/tracer/__init__.py

 from colossalai.fx.tracer.meta_patch.patched_function.python_ops import operator_getitem
 
 from ._meta_trace import meta_trace
+from ._symbolic_trace import symbolic_trace
 from .tracer import ColoTracer

colossalai/fx/tracer/_symbolic_trace.py

+from typing import Any, Callable, Dict, Optional, Union
+
+import torch
+
+from colossalai.fx import ColoGraphModule
+from colossalai.fx._compatibility import compatibility
+
+from .tracer import ColoTracer
+
+
+@compatibility(is_backward_compatible=True)
+def symbolic_trace(
+    root: Union[torch.nn.Module, Callable[..., Any]],
+    concrete_args: Optional[Dict[str, Any]] = None,
+    meta_args: Optional[Dict[str, Any]] = None,
+) -> ColoGraphModule:
+    """
+    Symbolic tracing API
+
+    Given an ``nn.Module`` or function instance ``root``, this function will return a ``ColoGraphModule``
+    constructed by recording operations seen while tracing through ``root``.
+
+    With ``meta_args`` and ``concrete_args``, we can trace the model that are untraceable subject to control flow.
+    If specified using ``meta_args`` only, the tracing can be done ahead of time.
+
+    Note that both ``meta_args`` and ``concrete_args`` are kwargs, which contains the key of the argument's names
+    and the value of the argument's values.
+
+    Uses:
+        >>> model = ...
+
+        # if this works
+        >>> gm = symbolic_trace(model)
+
+        # else try this
+        >>> gm = symbolic_trace(model, meta_args={'x': torch.rand(1, 3, 224, 224, device='meta')})
+
+        # else try this
+        >>> gm = symbolic_trace(model, concrete_args={'x': torch.rand(1, 3, 224, 224)})
+
+    Args:
+        root (Union[torch.nn.Module, Callable[..., Any]]): Module or function to be traced and converted
+            into a Graph representation.
+        concrete_args (Optional[Dict[str, Any]], optional): Inputs to be partially specialized. Defaults to None.
+        meta_args (Optional[Dict[str, Any]], optional): Inputs to be partially specialized, special for ``ColoTracer``.
+            Defaults to None.
+
+    Returns:
+        ColoGraphModule: A ``ColoGraphModule`` created from the recorded operations from ``root``.
+
+    Warnings:
+        This API is still under development and can incur some bugs. Feel free to report any bugs to the Colossal-AI team.
+
+    """
+    tracer = ColoTracer()
+    graph = tracer.trace(root, concrete_args, meta_args)
+    name = (root.__class__.__name__ if isinstance(root, torch.nn.Module) else root.__name__)
+    return ColoGraphModule(tracer.root, graph, name)

tests/test_fx/test_tracer/test_hf_model/utils.py → tests/test_fx/test_tracer/test_hf_model/hf_tracer_utils.py

@@ -3,24 +3,19 @@ from numpy import isin
 from torch.fx import GraphModule
 from torch.utils._pytree import tree_flatten
 
-from colossalai.fx import ColoTracer
+from colossalai.fx import symbolic_trace
 
 
 def trace_model_and_compare_output(model, data_gen):
     # must turn on eval mode to ensure the output is consistent
     model.eval()
 
-    # make sure that the model is traceable
-    tracer = ColoTracer()
-
     try:
         kwargs = data_gen()
         meta_args = {k: v.to('meta') for k, v in kwargs.items()}
-        graph = tracer.trace(root=model, meta_args=meta_args)
+        gm = symbolic_trace(model, meta_args=meta_args)
     except Exception as e:
         raise RuntimeError(f"Failed to trace {model.__class__.__name__}, error: {e}")
-    gm = GraphModule(model, graph, model.__class__.__name__)
-    gm.recompile()
 
     # run forward
     inputs = data_gen()

tests/test_fx/test_tracer/test_hf_model/test_hf_albert.py

 import pytest
 import torch
 import transformers
-from utils import trace_model_and_compare_output
+from hf_tracer_utils import trace_model_and_compare_output
 
 BATCH_SIZE = 2
 SEQ_LENGTH = 16

tests/test_fx/test_tracer/test_hf_model/test_hf_bert.py

 import pytest
 import torch
 import transformers
-from utils import trace_model_and_compare_output
+from hf_tracer_utils import trace_model_and_compare_output
 
 BATCH_SIZE = 2
 SEQ_LENGTH = 16

tests/test_fx/test_tracer/test_hf_model/test_hf_diffuser.py

 import pytest
 import torch
-from torch.fx import GraphModule
-from utils import trace_model_and_compare_output
-
 import transformers
-from colossalai.fx import ColoTracer
+from hf_tracer_utils import trace_model_and_compare_output
+
+from colossalai.fx import symbolic_trace
 
 try:
     import diffusers
@@ -32,11 +31,7 @@ def test_vae():
         model = model_cls()
         sample = torch.zeros(LATENTS_SHAPE)
 
-        tracer = ColoTracer()
-        graph = tracer.trace(root=model)
-
-        gm = GraphModule(model, graph, model.__class__.__name__)
-        gm.recompile()
+        gm = symbolic_trace(model)
 
         model.eval()
         gm.eval()
@@ -98,11 +93,7 @@ def test_unet():
         model = model_cls()
         sample = torch.zeros(LATENTS_SHAPE)
 
-        tracer = ColoTracer()
-        graph = tracer.trace(root=model)
-
-        gm = GraphModule(model, graph, model.__class__.__name__)
-        gm.recompile()
+        gm = symbolic_trace(model)
 
         model.eval()
         gm.eval()

tests/test_fx/test_tracer/test_hf_model/test_hf_gpt.py

 import pytest
 import torch
 import transformers
-from utils import trace_model_and_compare_output
+from hf_tracer_utils import trace_model_and_compare_output
 
 BATCH_SIZE = 1
 SEQ_LENGTH = 16

tests/test_fx/test_tracer/test_hf_model/test_hf_opt.py

 import pytest
 import torch
 import transformers
-from utils import trace_model_and_compare_output
+from hf_tracer_utils import trace_model_and_compare_output
 
 BATCH_SIZE = 1
 SEQ_LENGTH = 16

tests/test_fx/test_tracer/test_hf_model/test_hf_t5.py

 import pytest
 import torch
 import transformers
-from utils import trace_model_and_compare_output
+from hf_tracer_utils import trace_model_and_compare_output
 
 BATCH_SIZE = 1
 SEQ_LENGTH = 16

tests/test_fx/test_tracer/test_timm_model/test_timm_model.py

 import pytest
 import timm.models as tm
 import torch
-from torch.fx import GraphModule
 
-from colossalai.fx import ColoTracer
+from colossalai.fx import symbolic_trace
 
 
-def trace_and_compare(model_cls, tracer, data, meta_args=None):
+def trace_and_compare(model_cls, data, meta_args=None):
     # trace
     model = model_cls()
 
@@ -15,9 +14,7 @@ def trace_and_compare(model_cls, tracer, data, meta_args=None):
     # without this statement, the torch.nn.functional.batch_norm will always be in training mode
     model.eval()
 
-    graph = tracer.trace(root=model, meta_args=meta_args)
-    gm = GraphModule(model, graph, model.__class__.__name__)
-    gm.recompile()
+    gm = symbolic_trace(model, meta_args=meta_args)
 
     # run forward
     with torch.no_grad():
@@ -49,11 +46,10 @@ def test_timm_models_without_control_flow():
         tm.deit_base_distilled_patch16_224,
     ]
 
-    tracer = ColoTracer()
     data = torch.rand(2, 3, 224, 224)
 
     for model_cls in MODEL_LIST:
-        trace_and_compare(model_cls, tracer, data)
+        trace_and_compare(model_cls, data)
 
 
 def test_timm_models_with_control_flow():
@@ -64,13 +60,12 @@ def test_timm_models_with_control_flow():
         tm.swin_transformer.swin_base_patch4_window7_224
     ]
 
-    tracer = ColoTracer()
     data = torch.rand(2, 3, 224, 224)
 
     meta_args = {'x': data.to('meta')}
 
     for model_cls in MODEL_LIST_WITH_CONTROL_FLOW:
-        trace_and_compare(model_cls, tracer, data, meta_args)
+        trace_and_compare(model_cls, data, meta_args)
 
 
 if __name__ == '__main__':

tests/test_fx/test_tracer/test_torchaudio_model/torchaudio_utils.py

 import torch
-from torch.fx import GraphModule, Tracer
 
-from colossalai.fx import ColoTracer
+from colossalai.fx import symbolic_trace
 
 
 def trace_and_compare(model, data_gen, need_meta=False, need_concrete=False, kwargs_transform=False):
     data = data_gen()
     concrete_args = data if need_concrete else {}
     meta_args = {k: v.to('meta') for k, v in data.items()} if need_meta else {}
-    tracer = ColoTracer()
 
     model.eval()
 
-    graph = tracer.trace(root=model, concrete_args=concrete_args, meta_args=meta_args)
-    gm = GraphModule(model, graph, model.__class__.__name__)
-    gm.recompile()
+    gm = symbolic_trace(model, concrete_args=concrete_args, meta_args=meta_args)
 
     with torch.no_grad():
         non_fx_out = model(**data)

tests/test_fx/test_tracer/test_torchrec_model/test_deepfm_model.py

 import pytest
 import torch
 
-from colossalai.fx.tracer import meta_patch
-from colossalai.fx.tracer.meta_patch.patched_function import python_ops
-from colossalai.fx.tracer.tracer import ColoTracer
+from colossalai.fx import symbolic_trace
 
 try:
     from torchrec.models import deepfm
@@ -14,8 +12,6 @@ try:
 except ImportError:
     NOT_TORCHREC = True
 
-from torch.fx import GraphModule
-
 BATCH = 2
 SHAPE = 10
 
@@ -43,9 +39,6 @@ def test_torchrec_deepfm_models():
     # Dense Features
     features = torch.rand((BATCH, SHAPE))
 
-    # Tracer
-    tracer = ColoTracer()
-
     for model_cls in MODEL_LIST:
         # Initializing model
         if model_cls == deepfm.DenseArch:
@@ -60,9 +53,7 @@ def test_torchrec_deepfm_models():
             model = model_cls(ebc)
 
         # Setup GraphModule
-        graph = tracer.trace(model)
-        gm = GraphModule(model, graph, model.__class__.__name__)
-        gm.recompile()
+        gm = symbolic_trace(model)
 
         model.eval()
         gm.eval()

tests/test_fx/test_tracer/test_torchrec_model/test_dlrm_model.py

 import torch
 
-from colossalai.fx.tracer.tracer import ColoTracer
+from colossalai.fx import symbolic_trace
 
 try:
     from torchrec.models import dlrm
@@ -12,7 +12,6 @@ except ImportError:
     NOT_TORCHREC = True
 
 import pytest
-from torch.fx import GraphModule
 
 BATCH = 2
 SHAPE = 10
@@ -51,8 +50,6 @@ def test_torchrec_dlrm_models():
 
     # Sparse Features
     sparse_features = torch.rand((BATCH, len(keys), SHAPE))
-    # Tracer
-    tracer = ColoTracer()
 
     for model_cls in MODEL_LIST:
         # Initializing model
@@ -77,12 +74,9 @@ def test_torchrec_dlrm_models():
         # Setup GraphModule
         if model_cls == dlrm.InteractionV2Arch:
             concrete_args = {"dense_features": dense_features, "sparse_features": sparse_features}
-            graph = tracer.trace(model, concrete_args=concrete_args)
+            gm = symbolic_trace(model, concrete_args=concrete_args)
         else:
-            graph = tracer.trace(model)
-
-        gm = GraphModule(model, graph, model.__class__.__name__)
-        gm.recompile()
+            gm = symbolic_trace(model)
 
         model.eval()
         gm.eval()

tests/test_fx/test_tracer/test_torchvision_model/test_torchvision_model.py

@@ -2,8 +2,8 @@ import torch
 import torchvision
 import torchvision.models as tm
 from packaging import version
-from colossalai.fx import ColoTracer
-from torch.fx import GraphModule
+
+from colossalai.fx import symbolic_trace
 
 
 def test_torchvision_models():
@@ -20,7 +20,6 @@ def test_torchvision_models():
 
     torch.backends.cudnn.deterministic = True
 
-    tracer = ColoTracer()
     data = torch.rand(2, 3, 224, 224)
 
     for model_cls in MODEL_LIST:
@@ -30,10 +29,7 @@ def test_torchvision_models():
         else:
             model = model_cls()
 
-        graph = tracer.trace(root=model)
-
-        gm = GraphModule(model, graph, model.__class__.__name__)
-        gm.recompile()
+        gm = symbolic_trace(model)
 
         model.eval()
         gm.eval()