def display_debug_result(self):
"""Displays the debugger result"
"""
- header = ["Node Name", "Ops", "Time(us)", "Time(%)", "Start Time", \
- "End Time", "Shape", "Inputs", "Outputs"]
- lines = ["---------", "---", "--------", "-------", "----------", \
- "--------", "-----", "------", "-------"]
+ header = ["Node Name", "Ops", "Time(us)", "Time(%)", "Shape", "Inputs", "Outputs"]
+ lines = ["---------", "---", "--------", "-------", "-----", "------", "-------"]
eid = 0
data = []
total_time = sum(time[0] for time in self._time_list)
continue
name = node['name']
shape = str(self._output_tensor_list[eid].shape)
- time_us = round(time[0] * 1000000, 2)
- time_percent = round(((time[0] / total_time) * 100), 2)
+ time_us = round(time[0] * 1000000, 3)
+ time_percent = round(((time[0] / total_time) * 100), 3)
inputs = str(node['attrs']['num_inputs'])
outputs = str(node['attrs']['num_outputs'])
- node_data = [name, op, time_us, time_percent, str(time[1]), str(time[2]), \
- shape, inputs, outputs]
+ node_data = [name, op, time_us, time_percent, shape, inputs, outputs]
data.append(node_data)
eid += 1
fmt = ""
import os
import tempfile
import shutil
-from datetime import datetime
from tvm._ffi.base import string_types
from tvm._ffi.function import get_global_func
from tvm.contrib import graph_runtime
_DUMP_ROOT_PREFIX = "tvmdbg_"
_DUMP_PATH_PREFIX = "_tvmdbg_"
+
def create(graph_json_str, libmod, ctx, dump_root=None):
"""Create a runtime executor module given a graph and module.
try:
fcreate = get_global_func("tvm.graph_runtime_debug.create")
except ValueError:
- raise ValueError("Please set '(USE_GRAPH_RUNTIME_DEBUG ON)' in " \
- "config.cmake and rebuild TVM to enable debug mode")
+ raise ValueError(
+ "Please set '(USE_GRAPH_RUNTIME_DEBUG ON)' in "
+ "config.cmake and rebuild TVM to enable debug mode"
+ )
ctx, num_rpc_ctx, device_type_id = graph_runtime.get_device_ctx(libmod, ctx)
if num_rpc_ctx == len(ctx):
libmod = rpc_base._ModuleHandle(libmod)
try:
- fcreate = ctx[0]._rpc_sess.get_function("tvm.graph_runtime_debug.remote_create")
+ fcreate = ctx[0]._rpc_sess.get_function(
+ "tvm.graph_runtime_debug.remote_create"
+ )
except ValueError:
- raise ValueError("Please set '(USE_GRAPH_RUNTIME_DEBUG ON)' in " \
- "config.cmake and rebuild TVM to enable debug mode")
+ raise ValueError(
+ "Please set '(USE_GRAPH_RUNTIME_DEBUG ON)' in "
+ "config.cmake and rebuild TVM to enable debug mode"
+ )
func_obj = fcreate(graph_json_str, libmod, *device_type_id)
return GraphModuleDebug(func_obj, ctx, graph_json_str, dump_root)
To select which folder the outputs should be kept.
None will make a temp folder in /tmp/tvmdbg<rand_string> and does the dumping
"""
+
def __init__(self, module, ctx, graph_json_str, dump_root):
self._dump_root = dump_root
self._dump_path = None
- self._debug_run = module["debug_run"]
self._get_output_by_layer = module["get_output_by_layer"]
self._run_individual = module["run_individual"]
graph_runtime.GraphModule.__init__(self, module)
Time consumed for each execution will be set as debug output.
"""
- self.debug_datum._time_list = []
-
+ self.debug_datum._time_list = [
+ [float(t) * 1e-6] for t in self.run_individual(10, 1, 1)
+ ]
for i, node in enumerate(self.debug_datum.get_graph_nodes()):
- start_time = datetime.now().time()
- time_stamp = self._debug_run(i)
- end_time = datetime.now().time()
- self.debug_datum._time_list.append([time_stamp, start_time, end_time])
num_outputs = self.debug_datum.get_graph_node_output_num(node)
for j in range(num_outputs):
out_tensor = self._get_output_by_layer(i, j)
ret = output_tensors[node]
except:
node_list = output_tensors.keys()
- raise RuntimeError("Node " + node + " not found, available nodes are: "
- + str(node_list) + ".")
+ raise RuntimeError(
+ "Node "
+ + node
+ + " not found, available nodes are: "
+ + str(node_list)
+ + "."
+ )
elif isinstance(node, int):
output_tensors = self.debug_datum._output_tensor_list
ret = output_tensors[node]
self.debug_datum.display_debug_result()
def run_individual(self, number, repeat=1, min_repeat_ms=0):
- self._run_individual(number, repeat, min_repeat_ms)
+ ret = self._run_individual(number, repeat, min_repeat_ms)
+ return ret.strip(",").split(",") if ret else []
+
def exit(self):
"""Exits the dump folder and all its contents"""
* 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
#include <tvm/runtime/packed_func.h>
#include <tvm/runtime/registry.h>
#include <tvm/runtime/ndarray.h>
+
#include <chrono>
+#include <sstream>
#include "../graph_runtime.h"
namespace tvm {
class GraphRuntimeDebug : public GraphRuntime {
public:
/*!
- * \brief Run each operation and get the output.
- * \param index The index of op which needs to be run.
- * \return the elapsed time.
- */
- double DebugRun(size_t index) {
- CHECK(index < op_execs_.size());
- TVMContext ctx = data_entry_[entry_id(index, 0)]->ctx;
- auto tbegin = std::chrono::high_resolution_clock::now();
- if (op_execs_[index]) {
- op_execs_[index]();
- }
- TVMSynchronize(ctx.device_type, ctx.device_id, nullptr);
- auto tend = std::chrono::high_resolution_clock::now();
- double time = std::chrono::duration_cast<std::chrono::duration<double> >(
- tend - tbegin).count();
- return time;
- }
-
- /*!
- * \brief Run each operation in the graph and print out the runtime per op.
+ * \brief Run each operation in the graph and get the time per op for all ops.
* \param number The number of times to run this function for taking average.
* \param repeat The number of times to repeat the measurement.
- In total, the function will be invoked (1 + number x repeat) times,
- where the first one is warmed up and will be discarded in case
- there is lazy initialization.
+ * In total, the function will be invoked (1 + number x repeat) times,
+ * where the first one is warmed up and will be discarded in case
+ * there is lazy initialization.
* \param min_repeat_ms The minimum duration of one `repeat` in milliseconds.
- By default, one `repeat` contains `number` runs. If this parameter is set,
- the parameters `number` will be dynamically adjusted to meet the
- minimum duration requirement of one `repeat`.
+ * By default, one `repeat` contains `number` runs. If this parameter is set,
+ * the parameters `number` will be dynamically adjusted to meet the
+ * minimum duration requirement of one `repeat`.
+ * \return Comma seperated string containing the elapsed time per op for the last
+ * iteration only, because returning a long string over rpc can be expensive.
*/
- void RunIndividual(int number, int repeat, int min_repeat_ms) {
+ std::string RunIndividual(int number, int repeat, int min_repeat_ms) {
// warmup run
GraphRuntime::Run();
-
+ std::ostringstream os;
std::vector<double> time_per_op(op_execs_.size(), 0);
for (int i = 0; i < repeat; ++i) {
std::chrono::time_point<
auto op_tend = std::chrono::high_resolution_clock::now();
double op_duration = std::chrono::duration_cast<
std::chrono::duration<double> >(op_tend - op_tbegin).count();
- time_per_op[index] += op_duration * 1000; // ms
+ time_per_op[index] += op_duration * 1e6; // us
}
}
}
(tend - tbegin).count() * 1000;
} while (duration_ms < min_repeat_ms);
- LOG(INFO) << "Repeat: " << i;
+ LOG(INFO) << "Iteration: " << i;
int op = 0;
for (size_t index = 0; index < time_per_op.size(); index++) {
if (op_execs_[index]) {
time_per_op[index] /= number;
LOG(INFO) << "Op #" << op++ << " " << GetNodeName(index) << ": "
- << time_per_op[index] << " ms/iter";
+ << time_per_op[index] << " us/iter";
}
}
}
+ for (size_t index = 0; index < time_per_op.size(); index++) {
+ os << time_per_op[index] << ",";
+ }
+ return os.str();
}
/*!
const std::string& name,
const std::shared_ptr<ModuleNode>& sptr_to_self) {
// return member functions during query.
- if (name == "debug_run") {
- return PackedFunc([sptr_to_self, this](TVMArgs args, TVMRetValue* rv) {
- *rv = this->DebugRun(static_cast<size_t>(args[0].operator int64_t()));
- });
- } else if (name == "get_output_by_layer") {
+ if (name == "get_output_by_layer") {
return PackedFunc([sptr_to_self, this](TVMArgs args, TVMRetValue* rv) {
*rv = this->GetOutputByLayer(args[0], args[1]);
});
CHECK_GT(number, 0);
CHECK_GT(repeat, 0);
CHECK_GE(min_repeat_ms, 0);
- this->RunIndividual(number, repeat, min_repeat_ms);
+ *rv = this->RunIndividual(number, repeat, min_repeat_ms);
});
} else {
return GraphRuntime::GetFunction(name, sptr_to_self);
out = mod.get_output(0, tvm.nd.empty((n,)))
np.testing.assert_equal(out.asnumpy(), a + 1)
- #test individual run
- mod.run_individual(20, 2, 1)
-
mod.exit()
#verify dump root delete after cleanup
assert(not os.path.exists(directory))
mod.run(x=tvm.nd.array(a, ctx))
out = tvm.nd.empty((n,), ctx=ctx)
out = mod.get_output(0, out)
- mod.run_individual(20, 2, 1)
np.testing.assert_equal(out.asnumpy(), a + 1)
check_verify()
projects / platform / upstream / tvm.git / commitdiff