[Target] Remove deprecated parameters from target

apps/hexagon_launcher/README.md

@@ -118,7 +118,7 @@ mod, params = relay.frontend.from_tflite(
     tflite_model, shape_dict=shape_dict, dtype_dict=dtype_dict
 )
 
-target = tvm.target.hexagon('v68', link_params=True)
+target = tvm.target.hexagon('v68')
 with tvm.transform.PassContext(opt_level=3):
     lib = relay.build(mod, tvm.target.Target(target, host=target), params=params, mod_name="default")
 
@@ -172,7 +172,7 @@ A sample output JSON from running the Inception V3 model may look like
 
 When using AoT, the `target` needs to be `llvm`:
 ```
-aot_target = "llvm -keys=hexagon -link-params=0 -mattr=+hvxv69,+hvx-length128b,+hvx-qfloat,-hvx-ieee-fp -mcpu=hexagonv69 -mtriple=hexagon"
+aot_target = "llvm -keys=hexagon -mattr=+hvxv69,+hvx-length128b,+hvx-qfloat,-hvx-ieee-fp -mcpu=hexagonv69 -mtriple=hexagon"
 aot_host_target = aot_target
 ```
 

apps/howto_deploy/prepare_test_libs.py

@@ -33,7 +33,7 @@ def prepare_test_libs(base_path):
     fadd_dylib.export_library(dylib_path)
 
     # Compile library in system library mode
-    fadd_syslib = tvm.build(s, [A, B], "llvm --system-lib", name="addonesys")
+    fadd_syslib = tvm.build(s, [A, B], "llvm", name="addonesys")
     syslib_path = os.path.join(base_path, "test_addone_sys.o")
     fadd_syslib.save(syslib_path)
 

apps/sgx/src/build_model.py

@@ -39,7 +39,12 @@ def main():
     )
 
     with tvm.transform.PassContext(opt_level=3):
-        graph, lib, params = relay.build(net, "llvm --system-lib", params=params)
+        graph, lib, params = relay.build(
+            net,
+            "llvm",
+            params=params,
+            runtime=tvm.relay.backend.Runtime("cpp", {"system-lib": True}),
+        )
 
     build_dir = osp.abspath(sys.argv[1])
     if not osp.isdir(build_dir):

apps/wasm-standalone/wasm-graph/tools/build_graph_lib.py

@@ -72,10 +72,15 @@ def build_graph_lib(opt_level):
     shape_dict = {input_name: img_data.shape}
 
     mod, params = relay.frontend.from_onnx(onnx_model, shape_dict)
-    target = "llvm -mtriple=wasm32-unknown-unknown -mattr=+simd128 --system-lib"
+    target = "llvm -mtriple=wasm32-unknown-unknown -mattr=+simd128"
 
     with tvm.transform.PassContext(opt_level=opt_level):
-        factory = relay.build(mod, target=target, params=params)
+        factory = relay.build(
+            mod,
+            target=target,
+            params=params,
+            runtime=tvm.relay.backend.Runtime("cpp", {"system-lib": True}),
+        )
 
     # Save the model artifacts to obj_file
     obj_file = os.path.join(out_dir, "graph.o")

gallery/how_to/tune_with_autoscheduler/ci_logs/matmul.json

@@ -1,2 +1,2 @@
 # Keep a valid schedule for demonstraction. This is used to prevent flasky errors in CI.
-{"i": [["[\"matmul_add\", 1024, 1024, 1024, \"float32\"]", "llvm -keys=cpu -link-params=0", [18, 64, 64, 0, 0, 0, 0, 0]], [[], [["SP", 2, 0, 1024, [2, 1, 4], 1], ["SP", 2, 4, 1024, [1, 1, 8], 1], ["SP", 2, 8, 1024, [4], 1], ["RE", 2, [0, 4, 1, 5, 8, 2, 6, 9, 3, 7]], ["FSP", 4, 0, 0, 2], ["FSP", 4, 3, 1, 2], ["RE", 4, [0, 3, 1, 4, 2, 5]], ["CA", 2, 4, 3], ["FU", 4, [0, 1]], ["AN", 4, 0, 3], ["PR", 2, 0, "auto_unroll_max_step$8"], ["AN", 2, 9, 2], ["AN", 4, 4, 2]]]], "r": [[0.0044742], 0, 0.335558, 1607112214], "v": "v0.3"}
+{"i": [["[\"matmul_add\", 1024, 1024, 1024, \"float32\"]", "llvm -keys=cpu", [18, 64, 64, 0, 0, 0, 0, 0]], [[], [["SP", 2, 0, 1024, [2, 1, 4], 1], ["SP", 2, 4, 1024, [1, 1, 8], 1], ["SP", 2, 8, 1024, [4], 1], ["RE", 2, [0, 4, 1, 5, 8, 2, 6, 9, 3, 7]], ["FSP", 4, 0, 0, 2], ["FSP", 4, 3, 1, 2], ["RE", 4, [0, 3, 1, 4, 2, 5]], ["CA", 2, 4, 3], ["FU", 4, [0, 1]], ["AN", 4, 0, 3], ["PR", 2, 0, "auto_unroll_max_step$8"], ["AN", 2, 9, 2], ["AN", 4, 4, 2]]]], "r": [[0.0044742], 0, 0.335558, 1607112214], "v": "v0.3"}

gallery/how_to/tune_with_autoscheduler/ci_logs/sparse_dense.json

@@ -1,2 +1,2 @@
 # Keep a valid schedule for demonstraction. This is used to prevent flasky errors in CI.
-{"i": [["[\"sparse_dense\", 512, 512, 512, [9831, 16, 1], [9831], [33], \"float32\"]", "llvm -keys=cpu -link-params=0", [6, 64, 64, 0, 0, 0, 0, 0], "", 1, ["sparse_dense_bsr_512_512_512_16_1_0.60_W_data", "sparse_dense_bsr_512_512_512_16_1_0.60_W_indices", "sparse_dense_bsr_512_512_512_16_1_0.60_W_indptr"]], [[], [["CI", 8], ["CI", 6], ["SP", 5, 0, 512, [1, 8], 1], ["FSP", 9, 0, 2, 1], ["SP", 5, 3, 32, [32], 1], ["FSP", 9, 2, 4, 1], ["RE", 5, [0, 3, 1, 4, 6, 2, 5, 7]], ["RE", 9, [0, 2, 1, 3]], ["CA", 5, 9, 1], ["CI", 4], ["FU", 9, [0, 1]], ["AN", 9, 0, 3], ["PR", 5, 0, "auto_unroll_max_step$0"], ["AN", 9, 2, 2]]]], "r": [[0.000957008], 0, 0.605709, 1614689820], "v": "v0.6"}
+{"i": [["[\"sparse_dense\", 512, 512, 512, [9831, 16, 1], [9831], [33], \"float32\"]", "llvm -keys=cpu", [6, 64, 64, 0, 0, 0, 0, 0], "", 1, ["sparse_dense_bsr_512_512_512_16_1_0.60_W_data", "sparse_dense_bsr_512_512_512_16_1_0.60_W_indices", "sparse_dense_bsr_512_512_512_16_1_0.60_W_indptr"]], [[], [["CI", 8], ["CI", 6], ["SP", 5, 0, 512, [1, 8], 1], ["FSP", 9, 0, 2, 1], ["SP", 5, 3, 32, [32], 1], ["FSP", 9, 2, 4, 1], ["RE", 5, [0, 3, 1, 4, 6, 2, 5, 7]], ["RE", 9, [0, 2, 1, 3]], ["CA", 5, 9, 1], ["CI", 4], ["FU", 9, [0, 1]], ["AN", 9, 0, 3], ["PR", 5, 0, "auto_unroll_max_step$0"], ["AN", 9, 2, 2]]]], "r": [[0.000957008], 0, 0.605709, 1614689820], "v": "v0.6"}

gallery/how_to/tune_with_autotvm/tune_relay_x86.py

@@ -298,7 +298,7 @@ def tune_and_evaluate(tuning_opt):
 #
 #    Evaluation of the network been tuned on graph level:
 #    Compile...
-#    Config for target=llvm -keys=cpu -link-params=0, workload=('dense_nopack.x86', ('TENSOR', (1, 512), 'float32'), ('TENSOR', (1000, 512), 'float32'), None, 'float32') is missing in ApplyGraphBest context. A fallback configuration is used, which may bring great performance regression.
-#    Config for target=llvm -keys=cpu -link-params=0, workload=('dense_pack.x86', ('TENSOR', (1, 512), 'float32'), ('TENSOR', (1000, 512), 'float32'), None, 'float32') is missing in ApplyGraphBest context. A fallback configuration is used, which may bring great performance regression.
+#    Config for target=llvm -keys=cpu, workload=('dense_nopack.x86', ('TENSOR', (1, 512), 'float32'), ('TENSOR', (1000, 512), 'float32'), None, 'float32') is missing in ApplyGraphBest context. A fallback configuration is used, which may bring great performance regression.
+#    Config for target=llvm -keys=cpu, workload=('dense_pack.x86', ('TENSOR', (1, 512), 'float32'), ('TENSOR', (1000, 512), 'float32'), None, 'float32') is missing in ApplyGraphBest context. A fallback configuration is used, which may bring great performance regression.
 #    Evaluate inference time cost...
 #    Mean inference time (std dev): 3.16 ms (0.03 ms)

gallery/how_to/work_with_microtvm/micro_tvmc.sh

@@ -99,7 +99,7 @@ wget https://github.com/tensorflow/tflite-micro/raw/main/tensorflow/lite/micro/e
 #
 # bash
 tvmc compile magic_wand.tflite \
-    --target='c -keys=cpu -link-params=0 -model=host' \
+    --target='c -keys=cpu -model=host' \
     --runtime=crt \
     --runtime-crt-system-lib 1 \
     --executor='graph' \
@@ -111,7 +111,7 @@ tvmc compile magic_wand.tflite \
 # bash
 # This will generate a ``model.tar`` file which contains TVM compiler output files. To run this command for
 # a different Zephyr device, you need to update ``target``. For instance, for ``nrf5340dk_nrf5340_cpuapp`` board
-# the target is ``--target='c -keys=cpu -link-params=0 -model=nrf5340dk'``.
+# the target is ``--target='c -keys=cpu -model=nrf5340dk'``.
 #
 
 

gallery/tutorial/auto_scheduler_matmul_x86.py

@@ -44,8 +44,6 @@
 testing.utils.install_request_hook(depth=3)
 # sphinx_gallery_end_ignore
 
-import os
-
 import numpy as np
 import tvm
 from tvm import te, auto_scheduler

python/tvm/contrib/hexagon/pytest_plugin.py

@@ -245,7 +245,7 @@ def terminate_rpc_servers():
 
 aot_host_target = tvm.testing.parameter(
     "c",
-    "llvm -keys=hexagon -link-params=0 "
+    "llvm -keys=hexagon "
     "-mattr=+hvxv68,+hvx-length128b,+hvx-qfloat,-hvx-ieee-fp "
     "-mcpu=hexagonv68 -mtriple=hexagon",
 )

python/tvm/relay/build_module.py

@@ -274,69 +274,6 @@ def _build_module_no_factory(mod, target=None, target_host=None, params=None, mo
     return _build_module_no_factory_impl(mod, target, target_host, params, mod_name)
 
 
-def _reconstruct_from_deprecated_options(deprecated_params_target):
-    executor = None
-    runtime = None
-
-    deprecated_executor = None
-    deprecated_executor_args = {}
-    if "executor" in deprecated_params_target.attrs:
-        _deprecated_target_param_warning("Executor", "executor")
-        deprecated_executor = deprecated_params_target.attrs.get("executor", "graph")
-    if "interface-api" in deprecated_params_target.attrs:
-        _deprecated_target_sub_param_warning("Executor", "interface-api")
-        deprecated_executor_args.update(
-            {"interface-api": deprecated_params_target.attrs["interface-api"]}
-        )
-    if "unpacked-api" in deprecated_params_target.attrs:
-        _deprecated_target_sub_param_warning("Executor", "unpacked-api")
-        deprecated_executor_args.update(
-            {"unpacked-api": deprecated_params_target.attrs["unpacked-api"]}
-        )
-    if (
-        "link-params" in deprecated_params_target.attrs
-        and deprecated_params_target.attrs["link-params"]
-    ):
-        _deprecated_target_sub_param_warning("Executor", "link-params")
-        if deprecated_executor != "aot":
-            deprecated_executor_args.update(
-                {"link-params": deprecated_params_target.attrs["link-params"]}
-            )
-    if deprecated_executor or deprecated_executor_args:
-        executor = Executor(deprecated_executor or "graph", deprecated_executor_args)
-
-    deprecated_runtime = None
-    deprecated_runtime_args = {}
-    if "runtime" in deprecated_params_target.attrs:
-        _deprecated_target_param_warning("Runtime", "runtime")
-        deprecated_runtime = deprecated_params_target.attrs.get("runtime", "cpp")
-        if deprecated_runtime == "c":
-            deprecated_runtime = "crt"
-    if "system-lib" in deprecated_params_target.attrs:
-        _deprecated_target_sub_param_warning("Runtime", "system-lib")
-        deprecated_runtime_args.update({"system-lib": deprecated_params_target.attrs["system-lib"]})
-    if deprecated_runtime or deprecated_runtime_args:
-        runtime = Runtime(deprecated_runtime or "cpp", deprecated_runtime_args)
-
-    return executor, runtime
-
-
-def _deprecated_target_param_warning(registry, param):
-    warnings.warn(
-        f"Please use {registry} (tvm.relay.backend.{registry}) "
-        f"instead of deprecated Target parameter -{param}",
-        DeprecationWarning,
-    )
-
-
-def _deprecated_target_sub_param_warning(registry, param):
-    warnings.warn(
-        f"Please use {registry} (tvm.relay.backend.{registry}) parameter {param} "
-        f"instead of deprecated Target parameter -{param}",
-        DeprecationWarning,
-    )
-
-
 def build(
     ir_mod,
     target=None,
@@ -415,17 +352,6 @@ def build(
     assert len(raw_targets) > 0
     target_host = raw_targets[0].host
 
-    # All of this logic is to raise deprecation warnings for various parameters
-    # TODO(Mousius) Remove these after some time
-    deprecated_params_target = target_host or list(raw_targets)[0]
-    deprecated_executor, deprecated_runtime = _reconstruct_from_deprecated_options(
-        deprecated_params_target
-    )
-    if deprecated_executor:
-        executor = deprecated_executor
-    if deprecated_runtime:
-        runtime = deprecated_runtime
-
     # If current dispatch context is fallback context (the default root context),
     # then load pre-tuned parameters from TopHub
     if isinstance(autotvm.DispatchContext.current, autotvm.FallbackContext):
@@ -756,9 +682,5 @@ def create_executor(kind="debug", mod=None, device=None, target="llvm", params=N
     if kind == "vm":
         return VMExecutor(mod, device, raw_targets)
     if kind == "aot":
-        # The AOT requires the executor as a target attribute.
-        # (The compilation paths for the other executors currently do not always provide this
-        # attribute, hence the above generic assert is more forgiving).
-        assert "executor" in raw_targets[0].attrs
         return AotExecutor(mod, device, raw_targets)
     raise RuntimeError("unknown execution strategy: {0}".format(kind))

python/tvm/target/target.py

@@ -636,8 +636,6 @@ def hexagon(cpu_ver="v66", **kwargs):
         Whether to use QFloat HVX instructions.
     use_ieee_fp : bool (default: False)
         Whether to use IEEE HVX instructions
-    link_params : bool (default: False)
-        Whether to link graph parameters into the LLVM module.
 
     Note: Floating point support in HVX requires LLVM 14+.
     """
@@ -671,7 +669,6 @@ def get_arch_version(cpu_ver):
         "llvm_options": None,
         "use_qfloat": arch_version >= 68,
         "use_ieee_fp": False,
-        "link_params": False,
     }
     config.update(kwargs)
 
@@ -732,24 +729,10 @@ def create_llvm_options(cpu_ver, config):  # pylint: disable=unused-argument
         args = [s.replace("=", "@") for s in llvm_options.split()]
         return "--llvm-options=" + ",".join(args)
 
-    # TVM target attributes string
-    def create_tvm_options(cpu_ver, config):  # pylint: disable=unused-argument
-        """Create TVM target features string."""
-
-        features = {
-            "link_params": "link-params",
-        }
-        opts = ""
-        for k in config:
-            if k in features:
-                opts += " --" + features[k] + "=" + str(config[k])
-        return opts
-
     target_str = create_llvm_target(cpu_ver, config)
     llvm_str = create_llvm_options(cpu_ver, config)
-    tvm_str = create_tvm_options(cpu_ver, config)
 
-    args_list = target_str.split() + llvm_str.split() + tvm_str.split()
+    args_list = target_str.split() + llvm_str.split()
 
     return Target(" ".join(["hexagon"] + args_list))
 

src/target/target_kind.cc

@@ -264,12 +264,7 @@ TVM_REGISTER_TARGET_KIND("llvm", kDLCPU)
     .add_attr_option<String>("mtriple")
     .add_attr_option<String>("mfloat-abi")
     .add_attr_option<String>("mabi")
-    .add_attr_option<Bool>("system-lib")
-    .add_attr_option<String>("runtime")
     .add_attr_option<Integer>("num-cores")
-    .add_attr_option<Bool>("link-params", Bool(false))
-    .add_attr_option<Bool>("unpacked-api")
-    .add_attr_option<String>("interface-api")
     // Fast math flags, see https://llvm.org/docs/LangRef.html#fast-math-flags
     .add_attr_option<Bool>("fast-math")  // implies all the below
     .add_attr_option<Bool>("fast-math-nnan")
@@ -286,23 +281,16 @@ TVM_REGISTER_TARGET_KIND("llvm", kDLCPU)
     .set_target_parser(tvm::target::parsers::cpu::ParseTarget);
 
 TVM_REGISTER_TARGET_KIND("c", kDLCPU)
-    .add_attr_option<Bool>("system-lib")
-    .add_attr_option<Bool>("link-params", Bool(false))
-    .add_attr_option<String>("runtime")
     .add_attr_option<String>("mcpu")
     .add_attr_option<String>("march")
-    .add_attr_option<String>("executor")
     .add_attr_option<Integer>("workspace-byte-alignment")
     .add_attr_option<Integer>("constants-byte-alignment")
-    .add_attr_option<Bool>("unpacked-api")
-    .add_attr_option<String>("interface-api")
     .set_default_keys({"cpu"})
     .set_target_parser(tvm::target::parsers::cpu::ParseTarget);
 
 TVM_REGISTER_TARGET_KIND("cuda", kDLCUDA)
     .add_attr_option<String>("mcpu")
     .add_attr_option<String>("arch")
-    .add_attr_option<Bool>("system-lib")
     .add_attr_option<Integer>("max_shared_memory_per_block")
     .add_attr_option<Integer>("max_threads_per_block")
     .add_attr_option<Integer>("thread_warp_size", Integer(32))
@@ -314,7 +302,6 @@ TVM_REGISTER_TARGET_KIND("cuda", kDLCUDA)
 TVM_REGISTER_TARGET_KIND("nvptx", kDLCUDA)
     .add_attr_option<String>("mcpu")
     .add_attr_option<String>("mtriple")
-    .add_attr_option<Bool>("system-lib")
     .add_attr_option<Integer>("max_num_threads", Integer(1024))
     .add_attr_option<Integer>("thread_warp_size", Integer(32))
     .set_default_keys({"cuda", "gpu"})
@@ -324,7 +311,6 @@ TVM_REGISTER_TARGET_KIND("rocm", kDLROCM)
     .add_attr_option<String>("mcpu")
     .add_attr_option<String>("mtriple")
     .add_attr_option<Array<String>>("mattr")
-    .add_attr_option<Bool>("system-lib")
     // TODO(masahi): Support querying from a target device
     // On RDNA cards, thread_warp_size should be 32
     .add_attr_option<Integer>("max_num_threads", Integer(256))
@@ -335,7 +321,6 @@ TVM_REGISTER_TARGET_KIND("rocm", kDLROCM)
     .set_target_parser(UpdateROCmAttrs);
 
 TVM_REGISTER_TARGET_KIND("opencl", kDLOpenCL)
-    .add_attr_option<Bool>("system-lib")
     .add_attr_option<Integer>("max_num_threads", Integer(256))
     .add_attr_option<Integer>("thread_warp_size", Integer(1))
     .add_attr_option<Integer>("texture_spatial_limit", Integer(16384))
@@ -346,15 +331,13 @@ TVM_REGISTER_TARGET_KIND("opencl", kDLOpenCL)
 // information about this limitation can be found here:
 // https://developer.apple.com/documentation/metal/buffers/about_argument_buffers?language=objc
 TVM_REGISTER_TARGET_KIND("metal", kDLMetal)
-    .add_attr_option<Bool>("system-lib")
     .add_attr_option<Integer>("max_num_threads", Integer(256))
     .add_attr_option<Integer>("thread_warp_size", Integer(16))
     .add_attr_option<Integer>("max_function_args", Integer(31))
     .set_default_keys({"metal", "gpu"});
 
 TVM_REGISTER_TARGET_KIND("vulkan", kDLVulkan)
     .add_attr_option<Array<String>>("mattr")
-    .add_attr_option<Bool>("system-lib")
     // Feature support
     .add_attr_option<Bool>("supports_float16")
     .add_attr_option<Bool>("supports_float32", Bool(true))
@@ -393,39 +376,30 @@ TVM_REGISTER_TARGET_KIND("vulkan", kDLVulkan)
     .set_default_keys({"vulkan", "gpu"});
 
 TVM_REGISTER_TARGET_KIND("webgpu", kDLWebGPU)
-    .add_attr_option<Bool>("system-lib")
     .add_attr_option<Integer>("max_num_threads", Integer(256))
     .set_default_keys({"webgpu", "gpu"});
 
-TVM_REGISTER_TARGET_KIND("sdaccel", kDLOpenCL)
-    .add_attr_option<Bool>("system-lib")
+TVM_REGISTER_TARGET_KIND("sdaccel", kDLOpenCL)  // line break
     .set_default_keys({"sdaccel", "hls"});
 
-TVM_REGISTER_TARGET_KIND("aocl", kDLAOCL)
-    .add_attr_option<Bool>("system-lib")
+TVM_REGISTER_TARGET_KIND("aocl", kDLAOCL)  // line break
     .set_default_keys({"aocl", "hls"});
 
-TVM_REGISTER_TARGET_KIND("aocl_sw_emu", kDLAOCL)
-    .add_attr_option<Bool>("system-lib")
+TVM_REGISTER_TARGET_KIND("aocl_sw_emu", kDLAOCL)  // line break
     .set_default_keys({"aocl", "hls"});
 
 TVM_REGISTER_TARGET_KIND("hexagon", kDLHexagon)
     .add_attr_option<Array<String>>("mattr")
     .add_attr_option<String>("mcpu")
     .add_attr_option<String>("mtriple")
-    .add_attr_option<Bool>("system-lib")
-    .add_attr_option<Bool>("link-params", Bool(false))
     .add_attr_option<Array<String>>("llvm-options")
     .set_default_keys({"hexagon"});
 
-TVM_REGISTER_TARGET_KIND("stackvm", kDLCPU)  // line break
-    .add_attr_option<Bool>("system-lib");
+TVM_REGISTER_TARGET_KIND("stackvm", kDLCPU);
 
-TVM_REGISTER_TARGET_KIND("ext_dev", kDLExtDev)  // line break
-    .add_attr_option<Bool>("system-lib");
+TVM_REGISTER_TARGET_KIND("ext_dev", kDLExtDev);
 
-TVM_REGISTER_TARGET_KIND("hybrid", kDLCPU)  // line break
-    .add_attr_option<Bool>("system-lib");
+TVM_REGISTER_TARGET_KIND("hybrid", kDLCPU);
 
 TVM_REGISTER_TARGET_KIND("composite", kDLCPU)  // line break
     .add_attr_option<Array<Target>>("devices");