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new bf071dea54 [Relay][Frontend] Preserve Pytorch Span Names (#16171)
bf071dea54 is described below
commit bf071dea54ceb2705c3819909723d8eb8c048dcb
Author: Navya Mehta <[email protected]>
AuthorDate: Thu Dec 7 19:00:35 2023 -0500
[Relay][Frontend] Preserve Pytorch Span Names (#16171)
* Preserve Pytorch Span Names
* Update pytorch.py
* Add tests
* WIP
* Changes and tests
* Michael Klaiber feedback
* Linting fix
* Linting Feedback Pt.2
* Test changes
* Modify to Pytorch 2.0
---
python/tvm/relay/frontend/pytorch.py | 119 ++++++++++++++++++----
tests/python/frontend/pytorch/test_span_naming.py | 106 +++++++++++++++++++
2 files changed, 206 insertions(+), 19 deletions(-)
diff --git a/python/tvm/relay/frontend/pytorch.py
b/python/tvm/relay/frontend/pytorch.py
index 1a481e9300..9583575bfc 100644
--- a/python/tvm/relay/frontend/pytorch.py
+++ b/python/tvm/relay/frontend/pytorch.py
@@ -21,6 +21,8 @@
"""PT: PyTorch frontend."""
import functools
import itertools
+from abc import ABC
+from typing import Dict
import math
import re
import sys
@@ -137,7 +139,9 @@ def _is_int_seq(seq):
class PyTorchOpConverter:
"""A helper class for holding PyTorch op converters."""
- def __init__(self, prelude, default_dtype, use_parser_friendly_name=False):
+ def __init__(
+ self, prelude, default_dtype, use_parser_friendly_name=False,
preserve_pytorch_scopes=False
+ ):
self.prelude = prelude
self.default_dtype = default_dtype
self.create_convert_map()
@@ -146,6 +150,7 @@ class PyTorchOpConverter:
self.op_type_dict = {} # map from op type to its presenting order
self.current_op = [] # stack for recording current processing op
self.use_parser_friendly_name = use_parser_friendly_name
+ self.preserve_pytorch_scopes = preserve_pytorch_scopes
# this incrementally infers the type, see the comments on the type visitor
# above.
@@ -4314,7 +4319,11 @@ class PyTorchOpConverter:
def convert_block(self, block, outputs):
"""Translate Torch "Block", used for prim::If and prim::Loop"""
ops = _get_operator_nodes(
- block.nodes(), self.source_map, self.op_type_dict,
self.use_parser_friendly_name
+ block.nodes(),
+ self.source_map,
+ self.op_type_dict,
+ self.use_parser_friendly_name,
+ self.preserve_pytorch_scopes,
)
ret_names = _get_input_names(block.returnNode())
return self.convert_operators(ops, outputs, ret_names)
@@ -4881,25 +4890,76 @@ def _get_constant(node):
return None
-def _rename_outputs(node, source_map, op_type_dict, use_parser_friendly_name):
- """Rewrite debug name of node outputs with its operator type"""
+class NodeNamer(ABC):
+ """Name each node and output edge in the relay graph"""
- def _get_source_name(op_type):
+ def __init__(self, op_counter_dict: Dict[str, int]):
+ self.op_counter_dict = op_counter_dict
+
+ def increment_counter(self, identifier: str) -> int:
op_idx = 0
- if op_type in op_type_dict:
- op_idx = op_type_dict[op_type] + 1
- op_type_dict[op_type] = op_idx
- return "_".join([op_type, str(op_idx)])
+ if identifier in self.op_counter_dict:
+ op_idx = self.op_counter_dict[identifier] + 1
+ self.op_counter_dict[identifier] = op_idx
+ return op_idx
- # get source name of operator and rename all of its outputs
+ def get_node_source_name(self, node) -> str:
+ raise NotImplementedError()
+
+ def get_node_output_name(self, node_src_name: str, index: int) -> str:
+ raise NotImplementedError()
+
+
+class DefaultNodeKindNamer(NodeNamer):
+ """
+ Namer that uses a default naming based on the "type"/kind of node
# e.g. node.kind(): aten::adaptive_max_pool2d
# node_src_name -> aten::adaptive_max_pool2d_x
# output_1 -> aten::adaptive_max_pool2d_x_0
# output_2 -> aten::adaptive_max_pool2d_x_1
+ """
+
+ def get_node_source_name(self, node) -> str:
+ op_idx = self.increment_counter(node.kind())
+ return "_".join([node.kind(), str(op_idx)])
+
+ def get_node_output_name(self, node_src_name: str, index: int) -> str:
+ return "_".join([node_src_name, str(index)])
+
+
+class PytorchScopePreservingNamer(NodeNamer):
+ """
+ Namer that uses the Pytorch scope to name nodes.
+ eg. node could be called "bert.encoder.layer.11.output.dense"
+ """
+
+ def get_node_source_name(self, node) -> str:
+ # This works per the scope naming in Pytorch 2.0 and beyond.
+ scope_name_parts = node.scopeName().split("/")
+ imp_parts = [part.split("::")[-1] for part in scope_name_parts]
+ node_src_name = ".".join([part for part in imp_parts if part])
+ return node_src_name
+
+ def get_node_output_name(self, node_src_name: str, index: int) -> str:
+ op_idx = self.increment_counter(node_src_name)
+ return "_".join([node_src_name, str(op_idx), str(index)])
+
+
+def _rename_outputs(
+ node, source_map, op_type_dict, use_parser_friendly_name,
preserve_pytorch_scopes
+):
+ """Rewrite debug name of node outputs with its operator type"""
+ namer = (
+ PytorchScopePreservingNamer(op_type_dict)
+ if preserve_pytorch_scopes
+ else DefaultNodeKindNamer(op_type_dict)
+ )
+ # get source name of operator and rename all of its outputs
if node.kind() != "prim::GetAttr":
- node_src_name = _get_source_name(node.kind())
+ node_src_name = namer.get_node_source_name(node)
for index, output in enumerate(node.outputs()):
- output.setDebugName("_".join([node_src_name, str(index)]))
+ name = namer.get_node_output_name(node_src_name, index)
+ output.setDebugName(name)
# update source map
# if use_parser_friendly_name is True: e.g. prim::Constant_0 ->
prim__Constant_0
if use_parser_friendly_name:
@@ -4907,7 +4967,7 @@ def _rename_outputs(node, source_map, op_type_dict,
use_parser_friendly_name):
source_map[node] = node_src_name
-def _debug_rename(graph, use_parser_friendly_name):
+def _debug_rename(graph, use_parser_friendly_name, preserve_pytorch_scopes):
"""Returns map between node and source name"""
source_map, op_type_dict = {}, {}
prim_with_blocks = ["prim::If", "prim::Loop"]
@@ -4919,13 +4979,21 @@ def _debug_rename(graph, use_parser_friendly_name):
if node.kind() in prim_with_blocks:
for block in node.blocks():
_traverse_graph(block.nodes())
- _rename_outputs(node, source_map, op_type_dict,
use_parser_friendly_name)
+ _rename_outputs(
+ node, source_map, op_type_dict, use_parser_friendly_name,
preserve_pytorch_scopes
+ )
_traverse_graph(graph.nodes())
return source_map
-def _get_operator_nodes(nodes, source_map=None, op_type_dict=None,
use_parser_friendly_name=False):
+def _get_operator_nodes(
+ nodes,
+ source_map=None,
+ op_type_dict=None,
+ use_parser_friendly_name=False,
+ preserve_pytorch_scopes=False,
+):
"""Returns torch IR nodes that need conversion to Relay"""
ops, should_rename_graph = [], all([source_map, op_type_dict]) is not None
@@ -4935,7 +5003,9 @@ def _get_operator_nodes(nodes, source_map=None,
op_type_dict=None, use_parser_fr
continue
if should_rename_graph:
- _rename_outputs(node, source_map, op_type_dict,
use_parser_friendly_name)
+ _rename_outputs(
+ node, source_map, op_type_dict, use_parser_friendly_name,
preserve_pytorch_scopes
+ )
if node.outputsSize() > 1:
node_name = "_".join(_get_output_names(node))
@@ -5197,6 +5267,7 @@ def from_pytorch(
use_parser_friendly_name=False,
keep_quantized_weight=False,
export_renamed_c_graph_path=None,
+ preserve_pytorch_scopes=False,
):
"""Load PyTorch model in the form of a scripted PyTorch model and convert
into relay.
The companion parameters will be handled automatically.
@@ -5244,6 +5315,10 @@ def from_pytorch(
During the conversion, variable names in torch._C.Graph will be
assigned based on their op
types. The exported text file can be the reference to spans.
+ preserve_pytorch_scopes : bool
+ When naming the nodes in the Relay graph, use the "scope name" from
the Pytorch model.
+ If false, a default namer is used that does not preserve the Pytorch
scope names.
+
Returns
-------
mod : tvm.IRModule
@@ -5258,7 +5333,9 @@ def from_pytorch(
prelude = Prelude(mod)
enable_lower_all_tuples = True
- converter = PyTorchOpConverter(prelude, default_dtype,
use_parser_friendly_name)
+ converter = PyTorchOpConverter(
+ prelude, default_dtype, use_parser_friendly_name,
preserve_pytorch_scopes
+ )
graph = script_module.graph.copy()
@@ -5290,7 +5367,7 @@ def from_pytorch(
# rename _C.Graph here for constructing meaningful source name of graph
nodes
# by doing so, we could Use source_map as the reference to rename model
parameters
- source_map = _debug_rename(graph, use_parser_friendly_name)
+ source_map = _debug_rename(graph, use_parser_friendly_name,
preserve_pytorch_scopes)
param_vars, tensors, packed_param_map, param_debug_name_map =
convert_params(
graph, params, source_map, use_parser_friendly_name
)
@@ -5318,7 +5395,11 @@ def from_pytorch(
converter.update_convert_map(qnn_torch.convert_map)
operator_nodes = _get_operator_nodes(
- graph.nodes(), converter.source_map, converter.op_type_dict,
use_parser_friendly_name
+ graph.nodes(),
+ converter.source_map,
+ converter.op_type_dict,
+ use_parser_friendly_name,
+ preserve_pytorch_scopes,
)
ret_name = _get_input_names(graph.return_node())
outputs = converter.convert_operators(operator_nodes, outputs, ret_name)
diff --git a/tests/python/frontend/pytorch/test_span_naming.py
b/tests/python/frontend/pytorch/test_span_naming.py
new file mode 100644
index 0000000000..fb39ddf4f0
--- /dev/null
+++ b/tests/python/frontend/pytorch/test_span_naming.py
@@ -0,0 +1,106 @@
+# Licensed to the Apache Software Foundation (ASF) under one
+# or more contributor license agreements. See the NOTICE file
+# distributed with this work for additional information
+# regarding copyright ownership. The ASF licenses this file
+# to you under the Apache License, Version 2.0 (the
+# "License"); you may not use this file except in compliance
+# with the License. You may obtain a copy of the License at
+#
+# http://www.apache.org/licenses/LICENSE-2.0
+#
+# Unless required by applicable law or agreed to in writing,
+# software distributed under the License is distributed on an
+# "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
+# KIND, either express or implied. See the License for the
+# specific language governing permissions and limitations
+# under the License.
+# pylint: disable=import-self, too-many-lines, len-as-condition,
no-else-return, unused-variable, too-many-nested-blocks
+# pylint: disable=consider-iterating-dictionary, invalid-name,
unused-argument, unused-variable, broad-except
+# pylint: disable=import-outside-toplevel, simplifiable-if-expression,
cell-var-from-loop, unnecessary-lambda
+# pylint: disable=missing-function-docstring, redefined-builtin,
use-implicit-booleaness-not-comparison
+"""Tests to ensure span names are correctly populated when importing Pytorch"""
+from torch import nn
+import torch
+import tvm
+
+
+class NestedConvModule(nn.Module):
+ """Module that performs Conv2d and relu activation"""
+
+ def __init__(self, in_channels, out_channels):
+ super().__init__()
+ self.conv = nn.Conv2d(in_channels, out_channels, kernel_size=3,
stride=1, padding=1)
+ self.relu = nn.ReLU()
+
+ def forward(self, x):
+ x = self.relu(self.conv(x))
+ return x
+
+
+class NestedFinalModule(nn.Module):
+ """Simple module that adds 2 inputs"""
+
+ def forward(self, x, y):
+ return x + y
+
+
+class SimpleTwoConvModule(nn.Module):
+ """
+ ML model that performs 2 convolutions and adds them together.
+ All operations are inside nested modules to make scope names interesting.
+ """
+
+ def __init__(self):
+ super().__init__()
+ # First convolutional module
+ self.image_block1 = NestedConvModule(in_channels=3, out_channels=64)
+ # Second convolutional module
+ self.image_block2 = NestedConvModule(in_channels=64, out_channels=64)
+ self.final_block = NestedFinalModule()
+
+ def forward(self, x):
+ # Forward pass through the first convolutional module
+ x1 = self.image_block1(x)
+ # Forward pass through the second convolutional module
+ x2 = self.image_block2(x1)
+ # Add the outputs of the two convolutional modules
+ return self.final_block(x1, x2)
+
+
+def test_pytorch_scope_based_span_names():
+ model = SimpleTwoConvModule()
+ sample_input = torch.zeros((1, 3, 64, 64), dtype=torch.float32)
+ with torch.no_grad():
+ traced_torch_model = torch.jit.trace(model, sample_input)
+ import_input = [("model_input", (1, 3, 64, 64))]
+ relay_model_ir, relay_model_params = tvm.relay.frontend.from_pytorch(
+ traced_torch_model, import_input, preserve_pytorch_scopes=True
+ )
+ # If specified, we are preserving the pytorch named spans
+ for block in [1, 2]:
+ for key in ["weight", "bias"]:
+ assert f"image_block{block}.conv.{key}" in
relay_model_params.keys()
+ # Manually check all span names since asserting structural equality is not
sufficient
+ current_call = relay_model_ir["main"].body
+ assert current_call.op.name == "add"
+ assert current_call.span is not None and
current_call.span.source_name.name == "final_block"
+ current_call = current_call.args[1]
+ for block in [2, 1]:
+ assert current_call.op.name == "nn.relu"
+ assert (
+ current_call.span is not None
+ and current_call.span.source_name.name ==
f"image_block{block}.relu"
+ )
+ current_call = current_call.args[0]
+ assert current_call.op.name == "nn.bias_add"
+ assert (
+ current_call.span is not None
+ and current_call.span.source_name.name ==
f"image_block{block}.conv"
+ )
+ current_call = current_call.args[0]
+ assert current_call.op.name == "nn.conv2d"
+ assert (
+ current_call.span is not None
+ and current_call.span.source_name.name ==
f"image_block{block}.conv"
+ )
+ current_call = current_call.args[0]
