Files
SHARK-Studio/amdshark/amdshark_runner.py
pdhirajkumarprasad fe03539901 Migration to AMDShark (#2182)
Signed-off-by: pdhirajkumarprasad <dhirajp@amd.com>
2025-11-20 12:52:07 +05:30

128 lines
4.2 KiB
Python

# Copyright 2020 The Nod Team. All rights reserved.
#
# Licensed 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.
from amdshark.iree_utils.compile_utils import (
get_iree_compiled_module,
get_results,
export_iree_module_to_vmfb,
load_flatbuffer,
)
from amdshark.iree_utils._common import check_device_drivers, device_driver_info
from amdshark.parser import amdshark_args
import os
import sys
# supported dialects by the amdshark-runtime.
supported_dialects = {
"linalg",
"auto",
"stablehlo",
"tosa",
"tf-lite",
"tm_tensor",
}
class AMDSharkRunner:
"""
Base class for AMDSharkInference and AMDSharkTrainer
used to execute an mlir_module.
...
Attributes
----------
mlir_module : str
mlir_module path, string, or bytecode.
device : str
device to execute the mlir_module on.
currently supports cpu, cuda, vulkan, and metal backends.
mlir_dialect: str
The dialect in which the given mlir_module is in.
Refer to {https://mlir.llvm.org/docs/Dialects/}
Methods
-------
run(function_name, inputs=None):
Runs the function with `function_name` within the mlir_module along
with the given inputs, if the inputs are not given it autogenerates the
inputs. Also, the inputs should be a numpy array.
input_info():
Gives the information about the inputs required by the `function_name`.
This can be expensive as it does string matching to do so.
"""
def __init__(
self,
mlir_module: bytes = None,
device: str = "none",
mlir_dialect: str = "linalg",
extra_args: list = [],
compile_vmfb: bool = True,
device_idx: int = None,
rt_flags: list = [],
):
self.mlir_module = mlir_module
if self.mlir_module is not None:
if not os.path.isfile(mlir_module):
print(
"Warning: Initializing AMDSharkRunner with a mlir string/bytecode object will duplicate the model in RAM at compile time. To avoid this, initialize AMDSharkInference with a path to a MLIR module on your hard disk instead."
)
self.compile_str = True
else:
self.compile_str = False
self.device = amdshark_args.device if device == "none" else device
self.mlir_dialect = mlir_dialect
self.extra_args = extra_args
self.device_idx = device_idx
self.rt_flags = rt_flags
if check_device_drivers(self.device):
print(device_driver_info(self.device))
sys.exit(1)
if compile_vmfb == True:
# Compile the module to get the .vmfb.
params = get_iree_compiled_module(
self.mlir_module,
self.device,
self.mlir_dialect,
extra_args=self.extra_args,
device_idx=self.device_idx,
rt_flags=self.rt_flags,
compile_str=self.compile_str,
)
self.iree_compilation_module = params["vmfb"]
self.iree_config = params["config"]
self.temp_file_to_unlink = params["temp_file_to_unlink"]
del params
def run(
self, function_name, inputs: tuple, send_to_host=False, device=None
):
return get_results(
self.iree_compilation_module,
function_name,
inputs,
self.iree_config,
self.mlir_dialect,
send_to_host,
device=device,
)
# Get all function names defined within the compiled module.
def get_functions_in_module(self):
return self.iree_compilation_module._vm_module.function_names