Debugger#
TVM Debugger is an interface for debugging TVM's computation graph execution. It helps to provide access to graph structures and tensor values at the TVM runtime.
Debug Exchange Format#
1. Computational Graph#
The optimized graph build by relay in json serialized format is dumped as it is. This contains the whole information about the graph. The UX can either use this graph directly or transform this graph to the format UX can understand.
The Graph JSON format is explained below
1. nodes
Nodes are either placeholders or computational nodes in json. The nodes are stored
as a list. A node contains the below information
op- operation type,nullmeans it is a placeholder/variable/input node and``tvm_op`` means this node can be executedname- Name of the nodeinputs- Position of the inputs for this operation, Inputs is a list of tuples with (nodeid, index, version). (Optional)attrs- Attributes of the node which contains the following information
flatten_data- Whether this data need to be flattened before execution
func_name- Fused function name, corresponds to the symbol in the lib generated by relay compilation process.
num_inputs- Number of inputs for this node
num_outputs- Number of outputs this node produces
2. arg_nodes
arg_nodes is a list of indices of nodes which is placeholder/variable/input or constant/param to the graph.
3. heads
heads is a list of entries as the output of the graph.
4. node_row_ptr
node_row_ptr stores the history of forward path, so you can skip constructing the entire graph in inference tasks.
5. attrs
attrs can contain version numbers or similar helpful information.
storage_id- Memory slot id for each node in the storage layout.dtype- Datatype of each node (enum value).dltype- Datatype of each node in order.shape- Shape of each node k order.device_index- Device assignment for each entry in the graph.
Example of dumped graph:
{
"nodes": [ # List of nodes
{
"op": "null", # operation type = null, this is a placeholder/variable/input or constant/param node
"name": "x", # Name of the argument node
"inputs": [] # inputs for this node, its none since this is an argument node
},
{
"op": "tvm_op", # operation type = tvm_op, this node can be executed
"name": "relu0", # Name of the node
"attrs": { # Attributes of the node
"flatten_data": "0", # Whether this data need to be flattened
"func_name": "fuse_l2_normalize_relu", # Fused function name, corresponds to the symbol in the lib generated by compilation process
"num_inputs": "1", # Number of inputs for this node
"num_outputs": "1" # Number of outputs this node produces
},
"inputs": [[0, 0, 0]] # Position of the inputs for this operation
}
],
"arg_nodes": [0], # Which all nodes in this are argument nodes
"node_row_ptr": [0, 1, 2], # Row indices for faster depth first search
"heads": [[1, 0, 0]], # Position of the output nodes for this operation
"attrs": { # Attributes for the graph
"storage_id": ["list_int", [1, 0]], # memory slot id for each node in the storage layout
"dtype": ["list_int", [0, 0]], # Datatype of each node (enum value)
"dltype": ["list_str", [ # Datatype of each node in order
"float32",
"float32"]],
"shape": ["list_shape", [ # Shape of each node k order
[1, 3, 20, 20],
[1, 3, 20, 20]]],
"device_index": ["list_int", [1, 1]], # Device assignment for each node in order
}
}
2. Tensor dumping#
The tensor received after execution is in tvm.ndarray type. All the tensors will
be saved as binary bytes in serialized format. The result binary bytes can be loaded by the
API "load_params".
- Example of loading the parameters
- ::
- with open(path_params, "rb") as fi:
loaded_params = bytearray(fi.read())
module.load_params(loaded_params)
How to use Debugger?#
In
config.cmakeset theUSE_PROFILERflag toON# Whether enable additional graph debug functions set(USE_PROFILER ON)
Do 'make' tvm, so that it will make the
libtvm_runtime.soIn frontend script file instead of
from tvm.contrib import graph_executorimport theGraphModuleDebugfrom tvm.contrib.debugger.debug_executor import GraphModuleDebug
from tvm.contrib.debugger.debug_executor import GraphModuleDebug
m = GraphModuleDebug(
lib["debug_create"]("default", dev),
[dev],
lib.graph_json,
dump_root="/tmp/tvmdbg",
)
# set inputs
m.set_input('data', tvm.nd.array(data.astype(dtype)))
m.set_input(**params)
# execute
m.run()
tvm_out = m.get_output(0, tvm.nd.empty(out_shape, dtype)).numpy()
- If network previously was exported to external library using
lib.export_library("network.so") like shared object file/dynamic linked library, the initialization of debug runtime will be slightly different
- If network previously was exported to external library using
lib = tvm.runtime.load_module("network.so")
m = graph_executor.create(lib["get_graph_json"](), lib, dev, dump_root="/tmp/tvmdbg")
# set inputs
m.set_input('data', tvm.nd.array(data.astype(dtype)))
m.set_input(**params)
# execute
m.run()
tvm_out = m.get_output(0, tvm.nd.empty(out_shape, dtype)).numpy()
The outputs are dumped to a temporary folder in /tmp folder or the
folder specified while creating the runtime.
Sample Output#
The below is the an example output of the debugger.
Node Name Ops Time(us) Time(%) Start Time End Time Shape Inputs Outputs
--------- --- -------- ------- ---------- -------- ----- ------ -------
1_NCHW1c fuse___layout_transform___4 56.52 0.02 15:24:44.177475 15:24:44.177534 (1, 1, 224, 224) 1 1
_contrib_conv2d_nchwc0 fuse__contrib_conv2d_NCHWc 12436.11 3.4 15:24:44.177549 15:24:44.189993 (1, 1, 224, 224, 1) 2 1
relu0_NCHW8c fuse___layout_transform___broadcast_add_relu___layout_transform__ 4375.43 1.2 15:24:44.190027 15:24:44.194410 (8, 1, 5, 5, 1, 8) 2 1
_contrib_conv2d_nchwc1 fuse__contrib_conv2d_NCHWc_1 213108.6 58.28 15:24:44.194440 15:24:44.407558 (1, 8, 224, 224, 8) 2 1
relu1_NCHW8c fuse___layout_transform___broadcast_add_relu___layout_transform__ 2265.57 0.62 15:24:44.407600 15:24:44.409874 (64, 1, 1) 2 1
_contrib_conv2d_nchwc2 fuse__contrib_conv2d_NCHWc_2 104623.15 28.61 15:24:44.409905 15:24:44.514535 (1, 8, 224, 224, 8) 2 1
relu2_NCHW2c fuse___layout_transform___broadcast_add_relu___layout_transform___1 2004.77 0.55 15:24:44.514567 15:24:44.516582 (8, 8, 3, 3, 8, 8) 2 1
_contrib_conv2d_nchwc3 fuse__contrib_conv2d_NCHWc_3 25218.4 6.9 15:24:44.516628 15:24:44.541856 (1, 8, 224, 224, 8) 2 1
reshape1 fuse___layout_transform___broadcast_add_reshape_transpose_reshape 1554.25 0.43 15:24:44.541893 15:24:44.543452 (64, 1, 1) 2 1