refactor structure

chunk_codegen.py → autochunk/chunk_codegen.py

@@ -1967,13 +1967,11 @@ def _replace_reshape_size(context, node_name, reshape_size_dict):
 
 def emit_code_with_chunk(
     body,
-    ckpt_func,
     nodes,
     emit_node_func,
     delete_unused_value_func,
-    meta_nodes,
-    meta_graph,
-    max_memory=None,
+    chunk_region_search,
+    chunk_infos
 ):
     """Emit code with nested activation checkpoint
     When we detect some of the node.activation_checkpoint is a List, we will use
@@ -1988,23 +1986,19 @@ def emit_code_with_chunk(
     """
     node_list = list(nodes)
 
-    # find the chunk regions
-    chunk_region_search = ChunkRegionSearch(meta_graph, max_memory)
-    chunk_search = chunk_region_search.search_region()
-
-    chunk_regions = [i["region"] for i in chunk_search]
+    chunk_regions = [i["region"] for i in chunk_infos]
     chunk_starts = [i[0] for i in chunk_regions]
     chunk_ends = [i[1] for i in chunk_regions]
 
-    chunk_inputs = [i["inputs"] for i in chunk_search]
-    chunk_inputs_non_chunk = [i["inputs_non_chunk"] for i in chunk_search]
-    chunk_inputs_dim = [i["inputs_dim"] for i in chunk_search]
+    chunk_inputs = [i["inputs"] for i in chunk_infos]
+    chunk_inputs_non_chunk = [i["inputs_non_chunk"] for i in chunk_infos]
+    chunk_inputs_dim = [i["inputs_dim"] for i in chunk_infos]
     chunk_inputs_names = [j.name for i in chunk_inputs for j in i] + [
         j.name for i in chunk_inputs_non_chunk for j in i
     ]
 
-    chunk_outputs = [i["outputs"][0] for i in chunk_search]
-    chunk_outputs_dim = [i["outputs_dim"] for i in chunk_search]
+    chunk_outputs = [i["outputs"][0] for i in chunk_infos]
+    chunk_outputs_dim = [i["outputs_dim"] for i in chunk_infos]
 
     node_list = chunk_region_search.index_tracer.reorder_node_list(node_list)
     node_idx = 0
@@ -2022,7 +2016,7 @@ def emit_code_with_chunk(
                     chunk_inputs[region_idx],
                     chunk_outputs[region_idx],
                     chunk_outputs_dim[region_idx],
-                    chunk_search[region_idx]["chunk_size"],
+                    chunk_infos[region_idx]["chunk_size"],
                 )
             )
 
@@ -2041,14 +2035,14 @@ def emit_code_with_chunk(
             # ones like
             if "ones_like" in node.name:
                 meta_node = chunk_region_search.index_tracer.node_list[node_idx]
-                chunk_dim = chunk_search[region_idx]["node_chunk_dim"][meta_node][
+                chunk_dim = chunk_infos[region_idx]["node_chunk_dim"][meta_node][
                     "chunk_dim"
                 ]
                 if _get_node_shape(meta_node)[chunk_dim] != 1:
                     source_node = meta_node.args[0].args[0]
                     if (
-                        source_node not in chunk_search[region_idx]["node_chunk_dim"]
-                        or chunk_search[region_idx]["node_chunk_dim"][source_node][
+                        source_node not in chunk_infos[region_idx]["node_chunk_dim"]
+                        or chunk_infos[region_idx]["node_chunk_dim"][source_node][
                             "chunk_dim"
                         ]
                         is None
@@ -2060,7 +2054,7 @@ def emit_code_with_chunk(
                             body[-1], node.args[0].name, node.args[0].name + chunk_slice
                         )
             body[-1] = _replace_reshape_size(
-                body[-1], node.name, chunk_search[region_idx]["reshape_size"]
+                body[-1], node.name, chunk_infos[region_idx]["reshape_size"]
             )
             body[-1] = "    " + body[-1]
             delete_unused_value_func(node, body, chunk_inputs_names)
@@ -2092,6 +2086,9 @@ if CODEGEN_AVAILABLE:
             self.meta_graph = meta_graph
             self.max_memory = max_memory
             self.meta_node = list(meta_graph.graph.nodes)
+            # find the chunk regions
+            self.chunk_region_search = ChunkRegionSearch(meta_graph, max_memory)
+            self.chunk_infos = self.chunk_region_search.search_region()
 
         def _gen_python_code(
             self, nodes, root_module: str, namespace: _Namespace
@@ -2323,13 +2320,11 @@ if CODEGEN_AVAILABLE:
             # will use nested type of activation checkpoint codegen
             emit_code_with_chunk(
                 body,
-                ckpt_func,
                 nodes,
                 emit_node,
                 delete_unused_values,
-                self.meta_node,
-                self.meta_graph,
-                self.max_memory,
+                self.chunk_region_search,
+                self.chunk_infos
             )
 
             if len(body) == 0:

autochunk_benchmark.py

@@ -3,13 +3,13 @@ import time
 import torch
 import torch.fx
 
-from chunk_codegen import ChunkCodeGen
+from autochunk.chunk_codegen import ChunkCodeGen
 from colossalai.fx import ColoTracer
 from colossalai.fx.graph_module import ColoGraphModule
 from colossalai.fx.passes.meta_info_prop import MetaInfoProp
 from colossalai.fx.profiler import MetaTensor
-from evoformer.evoformer import evoformer_base
-from openfold.evoformer import EvoformerBlock
+from autochunk.evoformer.evoformer import evoformer_base
+from autochunk.openfold.evoformer import EvoformerBlock
 
 
 def _benchmark_evoformer(model: torch.nn.Module, node, pair, title, chunk_size=None):
@@ -94,23 +94,23 @@ def _build_openfold():
 def benchmark_evoformer():
     # init data and model
     msa_len = 256
-    pair_len = 2048
+    pair_len = 1024
     node = torch.randn(1, msa_len, pair_len, 256).cuda()
     pair = torch.randn(1, pair_len, pair_len, 128).cuda()
     model = evoformer_base().cuda()
 
     # build autochunk model
-    max_memory = 10000  # MB fit memory mode
-    # max_memory = None  # min memory mode
+    # max_memory = 10000  # MB fit memory mode
+    max_memory = None  # min memory mode
     autochunk = _build_autochunk(evoformer_base().cuda(), max_memory, node, pair)
 
     # build openfold
     chunk_size = 64
-    openfold = _build_openfold()
+    # openfold = _build_openfold()
 
     # benchmark
-    _benchmark_evoformer(model, node, pair, "base")
-    _benchmark_evoformer(openfold, node, pair, "openfold", chunk_size=chunk_size)
+    # _benchmark_evoformer(model, node, pair, "base")
+    # _benchmark_evoformer(openfold, node, pair, "openfold", chunk_size=chunk_size)
     _benchmark_evoformer(autochunk, node, pair, "autochunk")
 
 

chunk_codegen_run.py → autochunk_test.py

@@ -12,8 +12,8 @@ from colossalai.core import global_context as gpc
 from colossalai.fx.graph_module import ColoGraphModule
 from colossalai.fx.passes.meta_info_prop import MetaInfoProp, TensorMetadata
 from colossalai.fx.profiler import MetaTensor
-from evoformer.evoformer import evoformer_base
-from chunk_codegen import ChunkCodeGen
+from autochunk.evoformer.evoformer import evoformer_base
+from autochunk.chunk_codegen import ChunkCodeGen
 with_codegen = True