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tqchen pushed a commit to branch master
in repository https://gitbox.apache.org/repos/asf/incubator-tvm.git
The following commit(s) were added to refs/heads/master by this push:
new c55ed37 [REFACTOR][RELAY] Replace build_config with PassContext
(#5698)
c55ed37 is described below
commit c55ed371693740291b82cc8d88bf09c830d029c7
Author: Zhi <[email protected]>
AuthorDate: Fri May 29 21:59:35 2020 -0700
[REFACTOR][RELAY] Replace build_config with PassContext (#5698)
---
apps/android_camera/models/prepare_model.py | 2 +-
apps/benchmark/arm_cpu_imagenet_bench.py | 2 +-
apps/benchmark/gpu_imagenet_bench.py | 2 +-
apps/benchmark/mobile_gpu_imagenet_bench.py | 2 +-
apps/bundle_deploy/build_model.py | 2 +-
apps/sgx/src/build_model.py | 2 +-
golang/sample/gen_mobilenet_lib.py | 4 ++--
python/tvm/relay/frontend/common.py | 2 +-
python/tvm/relay/quantize/_calibrate.py | 3 +--
python/tvm/relay/transform/transform.py | 10 +++++++---
rust/frontend/examples/resnet/src/build_resnet.py | 4 ++--
src/relay/backend/build_module.cc | 5 ++---
tests/cpp/relay_transform_sequential.cc | 2 +-
tests/python/frontend/caffe2/test_forward.py | 2 +-
tests/python/frontend/coreml/test_forward.py | 4 ++--
tests/python/frontend/keras/test_forward.py | 2 +-
tests/python/frontend/mxnet/test_forward.py | 2 +-
tests/python/frontend/mxnet/test_qnn_ops_utils.py | 6 +++---
tests/python/frontend/onnx/test_forward.py | 2 +-
tests/python/frontend/pytorch/qnn_test.py | 2 +-
tests/python/frontend/pytorch/test_forward.py | 4 ++--
tests/python/frontend/tensorflow/test_bn_dynamic.py | 2 +-
tests/python/frontend/tensorflow/test_forward.py | 4 ++--
tests/python/frontend/tflite/test_forward.py | 2 +-
.../nightly/quantization/test_quantization_accuracy.py | 4 ++--
tests/python/relay/benchmarking/benchmark_vm.py | 4 ++--
tests/python/relay/test_backend_compile_engine.py | 2 +-
tests/python/relay/test_backend_graph_runtime.py | 2 +-
tests/python/relay/test_cpp_build_module.py | 2 +-
tests/python/relay/test_external_codegen.py | 6 ++++--
tests/python/relay/test_memory_passes.py | 2 +-
tests/python/relay/test_op_fast_math.py | 2 +-
tests/python/relay/test_op_level2.py | 10 +++++-----
tests/python/relay/test_op_qnn_conv2d.py | 10 +++++-----
tests/python/relay/test_op_qnn_dense.py | 2 +-
tests/python/relay/test_op_qnn_dequantize.py | 2 +-
tests/python/relay/test_op_qnn_quantize.py | 2 +-
tests/python/relay/test_op_qnn_requantize.py | 2 +-
tests/python/relay/test_pass_annotate_target.py | 4 ++--
tests/python/relay/test_pass_fast_math.py | 4 ++--
tests/python/relay/test_pass_fold_constant.py | 2 +-
tests/python/relay/test_pass_manager.py | 12 ++++++------
tests/python/relay/test_pass_partition_graph.py | 11 ++++++-----
tests/python/relay/test_simplify_fc_transpose.py | 2 +-
tests/python/relay/test_sparse_dense_convert.py | 2 +-
tests/python/unittest/test_runtime_module_export.py | 6 +++---
tests/python/unittest/test_target_codegen_blob.py | 4 ++--
tutorials/autotvm/tune_relay_arm.py | 2 +-
tutorials/autotvm/tune_relay_cuda.py | 2 +-
tutorials/autotvm/tune_relay_mobile_gpu.py | 2 +-
tutorials/autotvm/tune_relay_x86.py | 2 +-
tutorials/dev/relay_pass_infra.py | 10 +++++-----
tutorials/frontend/build_gcn.py | 2 +-
tutorials/frontend/deploy_model_on_android.py | 2 +-
tutorials/frontend/deploy_model_on_rasp.py | 2 +-
tutorials/frontend/deploy_prequantized.py | 2 +-
tutorials/frontend/deploy_prequantized_tflite.py | 2 +-
tutorials/frontend/deploy_ssd_gluoncv.py | 2 +-
tutorials/frontend/from_caffe2.py | 4 ++--
tutorials/frontend/from_coreml.py | 2 +-
tutorials/frontend/from_darknet.py | 2 +-
tutorials/frontend/from_keras.py | 2 +-
tutorials/frontend/from_mxnet.py | 2 +-
tutorials/frontend/from_onnx.py | 2 +-
tutorials/frontend/from_pytorch.py | 2 +-
tutorials/frontend/from_tensorflow.py | 2 +-
tutorials/frontend/from_tflite.py | 4 ++--
tutorials/relay_quick_start.py | 2 +-
vta/scripts/tune_resnet.py | 4 ++--
vta/tutorials/autotvm/tune_relay_vta.py | 2 +-
vta/tutorials/frontend/deploy_classification.py | 2 +-
vta/tutorials/frontend/deploy_detection.py | 2 +-
72 files changed, 121 insertions(+), 116 deletions(-)
diff --git a/apps/android_camera/models/prepare_model.py
b/apps/android_camera/models/prepare_model.py
index 36674d2..703a465 100644
--- a/apps/android_camera/models/prepare_model.py
+++ b/apps/android_camera/models/prepare_model.py
@@ -87,7 +87,7 @@ def main(model_str, output_path):
except FileExistsError:
pass
print("building...")
- with relay.build_config(opt_level=3):
+ with tvm.transform.PassContext(opt_level=3):
graph, lib, params = relay.build(net, target, target_host=target_host,
params=params)
print("dumping lib...")
lib.export_library(output_path_str + '/' + 'deploy_lib_cpu.so',
ndk.create_shared)
diff --git a/apps/benchmark/arm_cpu_imagenet_bench.py
b/apps/benchmark/arm_cpu_imagenet_bench.py
index 53b6168..f319d5a 100644
--- a/apps/benchmark/arm_cpu_imagenet_bench.py
+++ b/apps/benchmark/arm_cpu_imagenet_bench.py
@@ -39,7 +39,7 @@ def evaluate_network(network, target, target_host, repeat):
net, params, input_shape, output_shape = get_network(network, batch_size=1)
print_progress("%-20s building..." % network)
- with relay.build_config(opt_level=3):
+ with tvm.transform.PassContext(opt_level=3):
graph, lib, params = relay.build(
net, target=target, target_host=target_host, params=params)
diff --git a/apps/benchmark/gpu_imagenet_bench.py
b/apps/benchmark/gpu_imagenet_bench.py
index 0023700..a3df2c4 100644
--- a/apps/benchmark/gpu_imagenet_bench.py
+++ b/apps/benchmark/gpu_imagenet_bench.py
@@ -33,7 +33,7 @@ from util import get_network
def benchmark(network, target):
net, params, input_shape, output_shape = get_network(network, batch_size=1)
- with relay.build_config(opt_level=3):
+ with tvm.transform.PassContext(opt_level=3):
graph, lib, params = relay.build(net, target=target, params=params)
# create runtime
diff --git a/apps/benchmark/mobile_gpu_imagenet_bench.py
b/apps/benchmark/mobile_gpu_imagenet_bench.py
index 4f93a0d..83127ff 100644
--- a/apps/benchmark/mobile_gpu_imagenet_bench.py
+++ b/apps/benchmark/mobile_gpu_imagenet_bench.py
@@ -38,7 +38,7 @@ def evaluate_network(network, target, target_host, dtype,
repeat):
net, params, input_shape, output_shape = get_network(network,
batch_size=1, dtype=dtype)
print_progress("%-20s building..." % network)
- with relay.build_config(opt_level=3):
+ with tvm.transform.PassContext(opt_level=3):
graph, lib, params = relay.build(
net, target=target, target_host=target_host, params=params)
diff --git a/apps/bundle_deploy/build_model.py
b/apps/bundle_deploy/build_model.py
index 63d658e..1d415cd 100644
--- a/apps/bundle_deploy/build_model.py
+++ b/apps/bundle_deploy/build_model.py
@@ -33,7 +33,7 @@ def build_module(opts):
func = mod["main"]
func = relay.Function(func.params, relay.nn.softmax(func.body), None,
func.type_params, func.attrs)
- with relay.build_config(opt_level=3):
+ with tvm.transform.PassContext(opt_level=3):
graph, lib, params = relay.build(
func, 'llvm --system-lib', params=params)
diff --git a/apps/sgx/src/build_model.py b/apps/sgx/src/build_model.py
index 6e0933e..b988574 100755
--- a/apps/sgx/src/build_model.py
+++ b/apps/sgx/src/build_model.py
@@ -37,7 +37,7 @@ def main():
net, params = relay.testing.resnet.get_workload(
layers=18, batch_size=dshape[0], image_shape=dshape[1:])
- with relay.build_config(opt_level=3):
+ with tvm.transform.PassContext(opt_level=3):
graph, lib, params = relay.build(
net, 'llvm --system-lib', params=params)
diff --git a/golang/sample/gen_mobilenet_lib.py
b/golang/sample/gen_mobilenet_lib.py
index 8becd07..d4dcf21 100644
--- a/golang/sample/gen_mobilenet_lib.py
+++ b/golang/sample/gen_mobilenet_lib.py
@@ -16,7 +16,7 @@
# under the License.
import os
-from tvm import relay
+from tvm import relay, transform
from tvm.contrib.download import download_testdata
@@ -77,7 +77,7 @@ mod, params = relay.frontend.from_tflite(tflite_model,
target = 'llvm'
# Build with Relay
-with relay.build_config(opt_level=3):
+with transform.PassContext(opt_level=3):
graph, lib, params = relay.build_module.build(
mod, target, params=params)
diff --git a/python/tvm/relay/frontend/common.py
b/python/tvm/relay/frontend/common.py
index e86890f..05222c6 100644
--- a/python/tvm/relay/frontend/common.py
+++ b/python/tvm/relay/frontend/common.py
@@ -505,7 +505,7 @@ def infer_value(input_val, params, mod=None):
assert all(var.name_hint in params.keys() for var in
analysis.free_vars(
input_val)), "All inputs to infer must be available in params."
func = _function.Function(analysis.free_vars(input_val), input_val)
- with tvm.relay.build_config(opt_level=0):
+ with tvm.transform.PassContext(opt_level=0):
graph, lib, params = tvm.relay.build(func, target="llvm",
params=params)
ctx = tvm.cpu(0)
m = graph_runtime.create(graph, lib, ctx)
diff --git a/python/tvm/relay/quantize/_calibrate.py
b/python/tvm/relay/quantize/_calibrate.py
index 9794698..59ee51b 100644
--- a/python/tvm/relay/quantize/_calibrate.py
+++ b/python/tvm/relay/quantize/_calibrate.py
@@ -28,7 +28,6 @@ from . import quantize
from .. import op as _op
from .. import expr as _expr
from .. import analysis as _analysis
-from .. import transform as _transform
from .. import build_module as _build_module
from ...contrib import graph_runtime
from .kl_divergence import _find_scale_by_kl
@@ -45,7 +44,7 @@ def _get_profile_runtime(mod):
target = 'llvm'
ctx = tvm.context(target)
- with _transform.build_config(opt_level=3):
+ with tvm.transform.PassContext(opt_level=3):
graph, lib, params = _build_module.build(func, target=target)
runtime = graph_runtime.create(graph, lib, ctx)
runtime.set_input(**params)
diff --git a/python/tvm/relay/transform/transform.py
b/python/tvm/relay/transform/transform.py
index 19ddb32..8f4ec10 100644
--- a/python/tvm/relay/transform/transform.py
+++ b/python/tvm/relay/transform/transform.py
@@ -21,6 +21,7 @@ Relay pass transformation infrastructure.
import types
import inspect
import functools
+import warnings
import tvm.ir
from tvm import te
@@ -34,7 +35,9 @@ def build_config(opt_level=2,
required_pass=None,
disabled_pass=None,
trace=None):
- """Configure the build behavior by setting config variables.
+ """Configure the build behavior by setting config variables. This function
+ will be deprecated in TVM v0.7. Instead, we should directly use
+ tvm.transform.PassContext.
Parameters
----------
@@ -72,8 +75,9 @@ def build_config(opt_level=2,
pass_context: PassContext
The pass context for optimizations.
"""
- return tvm.ir.transform.PassContext(opt_level, required_pass,
- disabled_pass, trace)
+ warnings.warn("relay.build_config will be deprecated. Please use \
+ tvm.transform.PassContext directly", DeprecationWarning)
+ return tvm.transform.PassContext(opt_level, required_pass, disabled_pass,
trace)
@tvm._ffi.register_object("relay.FunctionPass")
diff --git a/rust/frontend/examples/resnet/src/build_resnet.py
b/rust/frontend/examples/resnet/src/build_resnet.py
index 49c67bf..a09a0c3 100644
--- a/rust/frontend/examples/resnet/src/build_resnet.py
+++ b/rust/frontend/examples/resnet/src/build_resnet.py
@@ -75,8 +75,8 @@ def build(target_dir):
num_layers=18, batch_size=batch_size, image_shape=image_shape)
# compile the model
- with relay.build_config(opt_level=opt_level):
- graph, lib, params = relay.build_module.build(net, target,
params=params)
+ with tvm.transform.PassContext(opt_level=opt_level):
+ graph, lib, params = relay.build_module.build(net, target,
params=params)
# save the model artifacts
lib.save(deploy_lib)
diff --git a/src/relay/backend/build_module.cc
b/src/relay/backend/build_module.cc
index abce068..7aad766 100644
--- a/src/relay/backend/build_module.cc
+++ b/src/relay/backend/build_module.cc
@@ -304,9 +304,8 @@ class RelayBuildModule : public runtime::ModuleNode {
// Handle heterogeneous compilation.
transform::PassContext pass_ctx = PassContext::Current();
if (targets_.size() > 1) {
- Optional<IntImm> opt_fallback_dev =
- pass_ctx->GetConfig("relay.fallback_device_type",
- IntImm(runtime::DataType::Int(32),
static_cast<int>(kDLCPU)));
+ Optional<Integer> opt_fallback_dev =
+ pass_ctx->GetConfig("relay.fallback_device_type",
Integer(static_cast<int>(kDLCPU)));
auto fallback_dev = opt_fallback_dev.value();
CHECK_GT(fallback_dev->value, 0U);
relay_module = RunDeviceAnnotationPass(relay_module,
fallback_dev->value);
diff --git a/tests/cpp/relay_transform_sequential.cc
b/tests/cpp/relay_transform_sequential.cc
index 60d3a5e..f08d557 100644
--- a/tests/cpp/relay_transform_sequential.cc
+++ b/tests/cpp/relay_transform_sequential.cc
@@ -70,7 +70,7 @@ TEST(Relay, Sequential) {
auto mod = IRModule::FromExpr(func);
auto pass_ctx = relay::transform::PassContext::Create();
pass_ctx->opt_level = 3;
- pass_ctx->config.Set("relay.fallback_device_type", IntImm(DataType::Int(32),
1));
+ pass_ctx->config.Set("relay.fallback_device_type", Integer(1));
{
tvm::With<relay::transform::PassContext> ctx_scope(pass_ctx);
tvm::With<tvm::Target> tctx(tvm::Target::Create("llvm"));
diff --git a/tests/python/frontend/caffe2/test_forward.py
b/tests/python/frontend/caffe2/test_forward.py
index f052872..50a8781 100644
--- a/tests/python/frontend/caffe2/test_forward.py
+++ b/tests/python/frontend/caffe2/test_forward.py
@@ -43,7 +43,7 @@ def get_tvm_output(model,
dtype_dict = {input_names: input_data.dtype}
mod, params = relay.frontend.from_caffe2(
model.init_net, model.predict_net, shape_dict, dtype_dict)
- with relay.build_config(opt_level=3):
+ with tvm.transform.PassContext(opt_level=3):
graph, lib, params = relay.build(mod, target, params=params)
m = graph_runtime.create(graph, lib, ctx)
diff --git a/tests/python/frontend/coreml/test_forward.py
b/tests/python/frontend/coreml/test_forward.py
index 3a15638..179f5b4 100644
--- a/tests/python/frontend/coreml/test_forward.py
+++ b/tests/python/frontend/coreml/test_forward.py
@@ -33,7 +33,7 @@ import model_zoo
def get_tvm_output(func, x, params, target, ctx,
out_shape=(1, 1000), input_name='image', dtype='float32'):
- with relay.transform.build_config(opt_level=3):
+ with tvm.transform.PassContext(opt_level=3):
graph, lib, params = relay.build(func, target, params=params)
m = graph_runtime.create(graph, lib, ctx)
# set inputs
@@ -76,7 +76,7 @@ def run_tvm_graph(coreml_model, target, ctx, input_data,
input_name, output_shap
dtype_dict = {input_name: input_data.dtype}
mod, params = relay.frontend.from_coreml(coreml_model, shape_dict)
- with relay.transform.build_config(opt_level=3):
+ with tvm.transform.PassContext(opt_level=3):
graph, lib, params = relay.build(mod, target, params=params)
from tvm.contrib import graph_runtime
diff --git a/tests/python/frontend/keras/test_forward.py
b/tests/python/frontend/keras/test_forward.py
index ed0181f..9b963c3 100644
--- a/tests/python/frontend/keras/test_forward.py
+++ b/tests/python/frontend/keras/test_forward.py
@@ -84,7 +84,7 @@ def verify_keras_frontend(keras_model, need_transpose=True,
layout='NCHW'):
def get_tvm_output(xs, target, ctx, dtype='float32'):
shape_dict = {name: x.shape for (name, x) in
zip(keras_model.input_names, xs)}
mod, params = relay.frontend.from_keras(keras_model, shape_dict,
layout=layout)
- with relay.transform.build_config(opt_level=2):
+ with tvm.transform.PassContext(opt_level=2):
graph, lib, params = relay.build(mod,
target,
params=params)
diff --git a/tests/python/frontend/mxnet/test_forward.py
b/tests/python/frontend/mxnet/test_forward.py
index 6d36ea3..5ed2fb8 100644
--- a/tests/python/frontend/mxnet/test_forward.py
+++ b/tests/python/frontend/mxnet/test_forward.py
@@ -66,7 +66,7 @@ def verify_mxnet_frontend_impl(mx_symbol,
shape_dict,
arg_params=args,
aux_params=auxs)
- with relay.build_config(opt_level=3):
+ with tvm.transform.PassContext(opt_level=3):
graph, lib, params = relay.build(mod, target, params=params)
m = graph_runtime.create(graph, lib, ctx)
# set inputs
diff --git a/tests/python/frontend/mxnet/test_qnn_ops_utils.py
b/tests/python/frontend/mxnet/test_qnn_ops_utils.py
index d130eef..541162d 100644
--- a/tests/python/frontend/mxnet/test_qnn_ops_utils.py
+++ b/tests/python/frontend/mxnet/test_qnn_ops_utils.py
@@ -15,8 +15,8 @@
# specific language governing permissions and limitations
# under the License.
-import tvm
import numpy as np
+import tvm
from tvm import relay
from tvm.contrib import graph_runtime
from tvm.relay.frontend.mxnet_qnn_op_utils import dequantize_mxnet_min_max, \
@@ -39,7 +39,7 @@ def test_mkldnn_dequantize():
in_dtype=in_dtype)
mod = relay.Function(relay.analysis.free_vars(dequantized_output),
dequantized_output)
mod = tvm.IRModule.from_expr(mod)
- with relay.build_config(opt_level=3):
+ with tvm.transform.PassContext(opt_level=3):
graph, lib, params = relay.build(mod, "llvm", params=None)
rt_mod = graph_runtime.create(graph, lib, ctx=tvm.cpu(0))
rt_mod.set_input(input_data=in_data)
@@ -93,7 +93,7 @@ def test_mkldnn_quantize():
out_dtype=out_dtype)
mod = relay.Function(relay.analysis.free_vars(quantized_output),
quantized_output)
mod = tvm.IRModule.from_expr(mod)
- with relay.build_config(opt_level=3):
+ with tvm.transform.PassContext(opt_level=3):
graph, lib, params = relay.build(mod, "llvm", params=None)
rt_mod = graph_runtime.create(graph, lib, ctx=tvm.cpu(0))
rt_mod.set_input(input_data=in_data)
diff --git a/tests/python/frontend/onnx/test_forward.py
b/tests/python/frontend/onnx/test_forward.py
index cd01294..edc33b7 100644
--- a/tests/python/frontend/onnx/test_forward.py
+++ b/tests/python/frontend/onnx/test_forward.py
@@ -65,7 +65,7 @@ def get_tvm_output(graph_def, input_data, target, ctx,
output_shape=None, output
mod, params = relay.frontend.from_onnx(graph_def, shape_dict, opset=opset)
- with relay.build_config(opt_level=1):
+ with tvm.transform.PassContext(opt_level=1):
graph, lib, params = relay.build(mod,
target,
params=params)
diff --git a/tests/python/frontend/pytorch/qnn_test.py
b/tests/python/frontend/pytorch/qnn_test.py
index bf5fa98..551cdc4 100644
--- a/tests/python/frontend/pytorch/qnn_test.py
+++ b/tests/python/frontend/pytorch/qnn_test.py
@@ -41,7 +41,7 @@ def get_tvm_runtime(script_module, input_name, ishape):
input_shapes = [(input_name, ishape)]
mod, params = relay.frontend.from_pytorch(script_module, input_shapes)
- with relay.build_config(opt_level=3):
+ with tvm.transform.PassContext(opt_level=3):
# test on only cpu for now, torch cannot run quant models on cuda
# also not to make CI too slow
json, lib, params = relay.build(mod, target="llvm", params=params)
diff --git a/tests/python/frontend/pytorch/test_forward.py
b/tests/python/frontend/pytorch/test_forward.py
index 6159bb8..f6edbf1 100644
--- a/tests/python/frontend/pytorch/test_forward.py
+++ b/tests/python/frontend/pytorch/test_forward.py
@@ -176,7 +176,7 @@ def verify_model(model_name, input_data=[],
compiled_input = dict(zip(input_names,
[inp.cpu().numpy() for inp in baseline_input]))
- with relay.build_config(opt_level=3):
+ with tvm.transform.PassContext(opt_level=3):
for target, ctx in ctx_list:
relay_graph, relay_lib, relay_params = relay.build(mod,
target=target, params=params)
relay_model = graph_runtime.create(relay_graph, relay_lib, ctx)
@@ -2294,7 +2294,7 @@ def test_forward_pretrained_bert_base_uncased():
# ----------------------------
target = 'llvm'
- with relay.build_config(opt_level=3):
+ with tvm.transform.PassContext(opt_level=3):
relay_graph, relay_lib, relay_params = relay.build(mod, target=target,
params=params)
######################################################################
diff --git a/tests/python/frontend/tensorflow/test_bn_dynamic.py
b/tests/python/frontend/tensorflow/test_bn_dynamic.py
index a2d6903..e80d774 100644
--- a/tests/python/frontend/tensorflow/test_bn_dynamic.py
+++ b/tests/python/frontend/tensorflow/test_bn_dynamic.py
@@ -50,7 +50,7 @@ def verify_fused_batch_norm(shape):
continue
mod, params = relay.frontend.from_tensorflow(constant_graph,
outputs=['output'])
- with relay.build_config(opt_level=3):
+ with tvm.transform.PassContext(opt_level=3):
graph, lib, params = relay.build(mod,
target=device,
params=params)
diff --git a/tests/python/frontend/tensorflow/test_forward.py
b/tests/python/frontend/tensorflow/test_forward.py
index c6a285c..89a0335 100644
--- a/tests/python/frontend/tensorflow/test_forward.py
+++ b/tests/python/frontend/tensorflow/test_forward.py
@@ -123,7 +123,7 @@ def run_tvm_graph(graph_def, input_data, input_node,
num_output=1,
result = ex.evaluate()(*inputs)
return vmobj_to_list(result)
else:
- with relay.build_config(opt_level=opt_level):
+ with tvm.transform.PassContext(opt_level=opt_level):
graph, lib, params = relay.build(mod, target, target_host, params)
ctx = tvm.context(target, 0)
@@ -2307,7 +2307,7 @@ def test_forward_ptb():
'Model/RNN/RNN/multi_rnn_cell/cell_0/lstm_cell/LSTMBlockCell_c': 'float32',
'Model/RNN/RNN/multi_rnn_cell/cell_0/lstm_cell/LSTMBlockCell_h': 'float32'}
target = 'llvm'
- with relay.build_config(opt_level=0):
+ with tvm.transform.PassContext(opt_level=0):
graph, lib, params = relay.build(mod,
target,
params=params)
diff --git a/tests/python/frontend/tflite/test_forward.py
b/tests/python/frontend/tflite/test_forward.py
index a68fd90..24b82c6 100644
--- a/tests/python/frontend/tflite/test_forward.py
+++ b/tests/python/frontend/tflite/test_forward.py
@@ -109,7 +109,7 @@ def run_tvm_graph(tflite_model_buf, input_data, input_node,
num_output=1, target
shape_dict=shape_dict,
dtype_dict=dtype_dict)
- with relay.build_config(opt_level=3):
+ with tvm.transform.PassContext(opt_level=3):
graph, lib, params = relay.build(mod, target, params=params)
ctx = tvm.context(target, 0)
diff --git a/tests/python/nightly/quantization/test_quantization_accuracy.py
b/tests/python/nightly/quantization/test_quantization_accuracy.py
index 4818cc6..d4b55f1 100644
--- a/tests/python/nightly/quantization/test_quantization_accuracy.py
+++ b/tests/python/nightly/quantization/test_quantization_accuracy.py
@@ -66,7 +66,7 @@ def get_model(model_name, batch_size, qconfig, target=None,
original=False, simu
mod, params = relay.frontend.from_mxnet(gluon_model, {"data": data_shape})
net = mod['main']
- with relay.build_config(opt_level=3):
+ with tvm.transform.PassContext(opt_level=3):
qfunc = relay.quantize.prerequisite_optimize(net, params=params)
logging.debug('original')
logging.debug(qfunc.astext(show_meta_data=False))
@@ -83,7 +83,7 @@ def get_model(model_name, batch_size, qconfig, target=None,
original=False, simu
def eval_acc(model, dataset, batch_fn, target=tvm.target.cuda(),
ctx=tvm.gpu(), log_interval=100):
- with relay.build_config(opt_level=3):
+ with tvm.transform.PassContext(opt_level=3):
graph, lib, params = relay.build(model, target)
# create runtime module
m = tvm.contrib.graph_runtime.create(graph, lib, ctx)
diff --git a/tests/python/relay/benchmarking/benchmark_vm.py
b/tests/python/relay/benchmarking/benchmark_vm.py
index 1e9030c..a6e05be 100644
--- a/tests/python/relay/benchmarking/benchmark_vm.py
+++ b/tests/python/relay/benchmarking/benchmark_vm.py
@@ -36,7 +36,7 @@ def benchmark_execution(mod,
model="unknown"):
def get_graph_runtime_output(mod, data, params, target, ctx,
dtype='float32', number=2, repeat=20):
- with relay.build_config(opt_level=3):
+ with tvm.transform.PassContext(opt_level=3):
graph, lib, params = relay.build(mod, target, params=params)
m = graph_runtime.create(graph, lib, ctx)
@@ -59,7 +59,7 @@ def benchmark_execution(mod,
def get_vm_output(mod, data, params, target, ctx, dtype='float32',
number=2, repeat=20):
- with relay.build_config(opt_level=3):
+ with tvm.transform.PassContext(opt_level=3):
exe = vm.compile(mod, target, params=params)
rly_vm = vm_rt.VirtualMachine(exe)
rly_vm.init(ctx)
diff --git a/tests/python/relay/test_backend_compile_engine.py
b/tests/python/relay/test_backend_compile_engine.py
index eb018fe..1b4e08f 100644
--- a/tests/python/relay/test_backend_compile_engine.py
+++ b/tests/python/relay/test_backend_compile_engine.py
@@ -184,7 +184,7 @@ def test_compile_placeholder_bypass():
z = relay.var("z", shape=(2, 3))
result = relay.Tuple([x, relay.op.concatenate([y, z], axis=0)])
func = relay.Function(relay.analysis.free_vars(result), result)
- with relay.build_config(opt_level=0):
+ with tvm.transform.PassContext(opt_level=0):
graph, lib, params = relay.build(tvm.IRModule.from_expr(func), 'llvm')
diff --git a/tests/python/relay/test_backend_graph_runtime.py
b/tests/python/relay/test_backend_graph_runtime.py
index 226d5ba..f0785bc 100644
--- a/tests/python/relay/test_backend_graph_runtime.py
+++ b/tests/python/relay/test_backend_graph_runtime.py
@@ -166,7 +166,7 @@ def test_gru_like():
z = unit(rnn_dim)
for target, ctx in ctx_list():
- with relay.build_config(opt_level=2):
+ with tvm.transform.PassContext(opt_level=2):
graph, lib, params = relay.build(tvm.IRModule.from_expr(z), target)
m = graph_runtime.create(graph, lib, ctx)
m.set_input("X", tvm.nd.array(x.astype(dtype)))
diff --git a/tests/python/relay/test_cpp_build_module.py
b/tests/python/relay/test_cpp_build_module.py
index 171b6b0..8d54384 100644
--- a/tests/python/relay/test_cpp_build_module.py
+++ b/tests/python/relay/test_cpp_build_module.py
@@ -115,7 +115,7 @@ def test_fp16_conversion():
X = tvm.nd.array(n * np.random.randn(n).astype(src) - n / 2)
# build
- with relay.build_config(opt_level=1):
+ with tvm.transform.PassContext(opt_level=1):
g_json, mmod, params =
relay.build(tvm.IRModule.from_expr(func), tgt)
# test
diff --git a/tests/python/relay/test_external_codegen.py
b/tests/python/relay/test_external_codegen.py
index 3797910..c449ce3 100644
--- a/tests/python/relay/test_external_codegen.py
+++ b/tests/python/relay/test_external_codegen.py
@@ -49,7 +49,8 @@ def check_result(mod, map_inputs, out_shape, result,
tol=1e-5, target="llvm",
return lib
def check_vm_result():
- with relay.build_config(opt_level=3, disabled_pass=["AlterOpLayout"]):
+ with tvm.transform.PassContext(opt_level=3,
+ disabled_pass=["AlterOpLayout"]):
exe = relay.vm.compile(mod, target=target)
code, lib = exe.save()
lib = update_lib(lib)
@@ -60,7 +61,8 @@ def check_result(mod, map_inputs, out_shape, result,
tol=1e-5, target="llvm",
tvm.testing.assert_allclose(out.asnumpy(), result, rtol=tol, atol=tol)
def check_graph_runtime_result():
- with relay.build_config(opt_level=3, disabled_pass=["AlterOpLayout"]):
+ with tvm.transform.PassContext(opt_level=3,
+ disabled_pass=["AlterOpLayout"]):
json, lib, _ = relay.build(mod, target=target)
lib = update_lib(lib)
rt_mod = tvm.contrib.graph_runtime.create(json, lib, ctx)
diff --git a/tests/python/relay/test_memory_passes.py
b/tests/python/relay/test_memory_passes.py
index 70e7086..dc16865 100644
--- a/tests/python/relay/test_memory_passes.py
+++ b/tests/python/relay/test_memory_passes.py
@@ -37,7 +37,7 @@ def check_memory_plan(func, check_fn):
no_plan_result = ex.evaluate(mod['main'])(*args)
# Compute with memory planning.
- with relay.build_config(opt_level=1, disabled_pass=["MemoryPlan"]):
+ with tvm.transform.PassContext(opt_level=1, disabled_pass=["MemoryPlan"]):
plan_result = ex.evaluate(mod['main'])(*args)
# Compute Python result.
diff --git a/tests/python/relay/test_op_fast_math.py
b/tests/python/relay/test_op_fast_math.py
index 215b83e..a771d29 100644
--- a/tests/python/relay/test_op_fast_math.py
+++ b/tests/python/relay/test_op_fast_math.py
@@ -34,7 +34,7 @@ def test_fastmath():
func = relay.Function([x], y)
mod = tvm.IRModule.from_expr(func)
- with relay.build_config(opt_level=3, required_pass=['FastMath']):
+ with tvm.transform.PassContext(opt_level=3,
required_pass=['FastMath']):
graph, lib, params = relay.build(mod, target="llvm", params=None)
# Check that the op related to fast math have been convered to
function in lib
diff --git a/tests/python/relay/test_op_level2.py
b/tests/python/relay/test_op_level2.py
index 68eced3..3e8720d 100644
--- a/tests/python/relay/test_op_level2.py
+++ b/tests/python/relay/test_op_level2.py
@@ -262,7 +262,7 @@ def test_conv2d_run():
with open(temp.relpath("temp.log"), "w") as log_file:
log_file.write(test_schedule)
with autotvm.apply_history_best(temp.relpath("temp.log")):
- with relay.build_config(opt_level=3):
+ with tvm.transform.PassContext(opt_level=3):
print('Compiling...')
graph_json, mod, params = tvm.relay.build(mod, target="llvm
-device=arm_cpu")
@@ -356,7 +356,7 @@ def test_conv2d_winograd():
data.astype(out_dtype), kernel.astype(out_dtype), 1, padding,
groups=groups)
- with WinogradFallback(), relay.build_config(opt_level=3):
+ with WinogradFallback(), tvm.transform.PassContext(opt_level=3):
for target, ctx in ctx_list():
if target != 'cuda':
continue
@@ -578,7 +578,7 @@ def test_conv3d_winograd():
data.astype(out_dtype), kernel.astype(out_dtype), 1, padding,
groups=groups)
- with WinogradFallback(), relay.build_config(opt_level=3):
+ with WinogradFallback(), tvm.transform.PassContext(opt_level=3):
for target, ctx in ctx_list():
if target != 'cuda':
continue
@@ -1199,7 +1199,7 @@ def test_conv2d_int8_intrinsics():
wdata = np.random.rand(*kernel_shape) * 10
parameters = {"weight": tvm.nd.array(wdata.astype(weight_dtype))}
- with relay.build_config(opt_level=3):
+ with tvm.transform.PassContext(opt_level=3):
graph, lib, params = relay.build(func, target, params=parameters)
assembly = lib.get_source("asm")
@@ -1314,7 +1314,7 @@ def test_depthwise_conv2d_int8():
llvm_version = tvm.target.codegen.llvm_version_major()
for target in targets:
if llvm_version >= 8:
- with relay.build_config(opt_level=3):
+ with tvm.transform.PassContext(opt_level=3):
graph, lib, params = relay.build(func, target,
params=parameters)
diff --git a/tests/python/relay/test_op_qnn_conv2d.py
b/tests/python/relay/test_op_qnn_conv2d.py
index 6911c52..fcb335f 100644
--- a/tests/python/relay/test_op_qnn_conv2d.py
+++ b/tests/python/relay/test_op_qnn_conv2d.py
@@ -182,7 +182,7 @@ def verify(ref_func, qnn_func, data_shape, data_dtype,
kernel_shape,
def get_output(func, golden_inputs):
- with relay.build_config(opt_level=2):
+ with tvm.transform.PassContext(opt_level=2):
golden_data, golden_weight = golden_inputs
params = {'kernel': golden_weight}
graph, lib, params = relay.build(func, "llvm", params=params)
@@ -655,7 +655,7 @@ def test_tflite_large_irregular():
golden_data = np.full(data_shape, 127).astype('uint8')
golden_weight = np.full(kernel_shape, 127).astype('uint8')
- with relay.build_config(opt_level=2):
+ with tvm.transform.PassContext(opt_level=2):
params = {'kernel': golden_weight}
graph, lib, params = relay.build(qnn_func, "llvm", params=params)
mod = graph_runtime.create(graph, lib, ctx=tvm.cpu(0))
@@ -698,7 +698,7 @@ def test_tflite_output_multiplier_greater_than_one():
-1, -1, 1, 1)).reshape(kernel_shape)
golden_weight = golden_weight.astype('uint8')
- with relay.build_config(opt_level=2):
+ with tvm.transform.PassContext(opt_level=2):
params = {'kernel': golden_weight}
graph, lib, params = relay.build(qnn_func, "llvm", params=params)
mod = graph_runtime.create(graph, lib, ctx=tvm.cpu(0))
@@ -744,7 +744,7 @@ def test_tflite_anistropic_strides():
golden_weight = np.array((129, 131, 133, 135)).reshape(kernel_shape)
golden_weight = golden_weight.astype('uint8')
- with relay.build_config(opt_level=2):
+ with tvm.transform.PassContext(opt_level=2):
params = {'kernel': golden_weight}
graph, lib, params = relay.build(qnn_func, "llvm", params=params)
mod = graph_runtime.create(graph, lib, ctx=tvm.cpu(0))
@@ -789,7 +789,7 @@ def test_broadcast_layout():
func = relay.add(func, bias)
func = relay.Function(relay.analysis.free_vars(func), func)
mod = tvm.IRModule.from_expr(func)
- with relay.build_config(opt_level=3):
+ with tvm.transform.PassContext(opt_level=3):
graph, lib, params = relay.build(mod, "llvm -mcpu=skylake-avx512")
def test_depthwise_depth_multiplier():
diff --git a/tests/python/relay/test_op_qnn_dense.py
b/tests/python/relay/test_op_qnn_dense.py
index 3cfcfd1..0ba3210 100644
--- a/tests/python/relay/test_op_qnn_dense.py
+++ b/tests/python/relay/test_op_qnn_dense.py
@@ -167,7 +167,7 @@ def qnn_dense_driver(test_configuration):
mod = relay.Function(relay.analysis.free_vars(mod), mod)
mod = tvm.IRModule.from_expr(mod)
mod = relay.qnn.transform.CanonicalizeOps()(mod)
- with relay.build_config(opt_level=2):
+ with tvm.transform.PassContext(opt_level=2):
graph, lib, params = relay.build(mod, "llvm", params=None)
mod = graph_runtime.create(graph, lib, ctx=tvm.cpu(0))
mod.set_input(quantized_data_name,
test_configuration[quantized_data_name])
diff --git a/tests/python/relay/test_op_qnn_dequantize.py
b/tests/python/relay/test_op_qnn_dequantize.py
index febf5c5..3c82b7f 100644
--- a/tests/python/relay/test_op_qnn_dequantize.py
+++ b/tests/python/relay/test_op_qnn_dequantize.py
@@ -30,7 +30,7 @@ def quantize_test_driver(in_dtype, quant_args, in_data,
verify_output_data):
input_zero_point=input_zero_point)
mod = relay.Function(relay.analysis.free_vars(quantized_output),
quantized_output)
mod = tvm.IRModule.from_expr(mod)
- with relay.build_config(opt_level=3):
+ with tvm.transform.PassContext(opt_level=3):
graph, lib, params = relay.build(mod, "llvm", params=None)
rt_mod = graph_runtime.create(graph, lib, ctx=tvm.cpu(0))
rt_mod.set_input(input_data=in_data)
diff --git a/tests/python/relay/test_op_qnn_quantize.py
b/tests/python/relay/test_op_qnn_quantize.py
index 09b04d8..a284e8b 100644
--- a/tests/python/relay/test_op_qnn_quantize.py
+++ b/tests/python/relay/test_op_qnn_quantize.py
@@ -32,7 +32,7 @@ def quantize_test_driver(in_dtype, quant_args, axis,
out_dtype, in_data, verify_
out_dtype=out_dtype)
mod = relay.Function(relay.analysis.free_vars(quantized_output),
quantized_output)
mod = tvm.IRModule.from_expr(mod)
- with relay.build_config(opt_level=3):
+ with tvm.transform.PassContext(opt_level=3):
graph, lib, params = relay.build(mod, "llvm", params=None)
rt_mod = graph_runtime.create(graph, lib, ctx=tvm.cpu(0))
rt_mod.set_input(input_data=in_data)
diff --git a/tests/python/relay/test_op_qnn_requantize.py
b/tests/python/relay/test_op_qnn_requantize.py
index 8123397..fb52b30 100644
--- a/tests/python/relay/test_op_qnn_requantize.py
+++ b/tests/python/relay/test_op_qnn_requantize.py
@@ -24,7 +24,7 @@ from tvm.contrib import graph_runtime
roundings = ["UPWARD", "TONEAREST"]
def verify(mod, goldens):
- with relay.build_config(opt_level=3):
+ with tvm.transform.PassContext(opt_level=3):
graph, lib, params = relay.build(mod, "llvm", params=None)
golden_data, golden_output = goldens
rt_mod = graph_runtime.create(graph, lib, ctx=tvm.cpu(0))
diff --git a/tests/python/relay/test_pass_annotate_target.py
b/tests/python/relay/test_pass_annotate_target.py
index 01ba9b6..0583946 100644
--- a/tests/python/relay/test_pass_annotate_target.py
+++ b/tests/python/relay/test_pass_annotate_target.py
@@ -52,7 +52,7 @@ def check_result(mod, map_inputs, out_shape, result,
tol=1e-5, target="llvm",
return lib
def check_vm_result():
- with relay.build_config(opt_level=3, disabled_pass=["AlterOpLayout"]):
+ with tvm.transform.PassContext(opt_level=3,
disabled_pass=["AlterOpLayout"]):
exe = relay.vm.compile(mod, target=target, params=params)
code, lib = exe.save()
lib = update_lib(lib)
@@ -63,7 +63,7 @@ def check_result(mod, map_inputs, out_shape, result,
tol=1e-5, target="llvm",
tvm.testing.assert_allclose(out.asnumpy(), result, rtol=tol, atol=tol)
def check_graph_runtime_result():
- with relay.build_config(opt_level=3, disabled_pass=["AlterOpLayout"]):
+ with tvm.transform.PassContext(opt_level=3,
disabled_pass=["AlterOpLayout"]):
json, lib, param = relay.build(mod, target=target, params=params)
lib = update_lib(lib)
rt_mod = tvm.contrib.graph_runtime.create(json, lib, ctx)
diff --git a/tests/python/relay/test_pass_fast_math.py
b/tests/python/relay/test_pass_fast_math.py
index e75316f..93ad034 100644
--- a/tests/python/relay/test_pass_fast_math.py
+++ b/tests/python/relay/test_pass_fast_math.py
@@ -29,7 +29,7 @@ def test_exp():
assert "fast_exp" in fast_mod.astext()
# Check that FastMath option works for relay.build.
- with relay.build_config(opt_level=3, required_pass=['FastMath']):
+ with tvm.transform.PassContext(opt_level=3, required_pass=['FastMath']):
fast_mod = relay.optimize(mod, target='llvm', params=None)
assert "fast_exp" in fast_mod[0].astext()
@@ -43,7 +43,7 @@ def test_tanh():
assert "fast_tanh" in fast_mod.astext()
# Check that FastMath option works for relay.build.
- with relay.build_config(opt_level=3, required_pass=['FastMath']):
+ with tvm.transform.PassContext(opt_level=3, required_pass=['FastMath']):
fast_mod = relay.optimize(mod, target='llvm', params=None)
assert "fast_tanh" in fast_mod[0].astext()
diff --git a/tests/python/relay/test_pass_fold_constant.py
b/tests/python/relay/test_pass_fold_constant.py
index 1e8c6da..fcccab5 100644
--- a/tests/python/relay/test_pass_fold_constant.py
+++ b/tests/python/relay/test_pass_fold_constant.py
@@ -213,7 +213,7 @@ def test_fold_batch_norm():
mod, params = create_workload(bn_output[0], initializer)
mod["main"] = bind_params_by_name(mod["main"], params)
- with relay.build_config(opt_level=3):
+ with tvm.transform.PassContext(opt_level=3):
mod = remove_bn_pass(mod)
expect = run_infer_type(expected())
diff --git a/tests/python/relay/test_pass_manager.py
b/tests/python/relay/test_pass_manager.py
index d6037b5..25299ca 100644
--- a/tests/python/relay/test_pass_manager.py
+++ b/tests/python/relay/test_pass_manager.py
@@ -382,7 +382,7 @@ def test_sequential_pass():
def test_only_module_pass():
passes = [module_pass]
sequential = tvm.transform.Sequential(opt_level=1, passes=passes)
- with relay.build_config(required_pass=["mod_transform"]):
+ with tvm.transform.PassContext(required_pass=["mod_transform"]):
ret_mod = sequential(mod)
# Check the subtract function.
sub_var, new_sub = extract_var_func(ret_mod, v_sub.name_hint)
@@ -397,7 +397,7 @@ def test_sequential_pass():
# Check the subtract function.
passes = [function_pass]
sequential = tvm.transform.Sequential(opt_level=1, passes=passes)
- with relay.build_config(required_pass=["func_transform"]):
+ with tvm.transform.PassContext(required_pass=["func_transform"]):
ret_mod = sequential(mod)
_, new_sub = extract_var_func(ret_mod, v_sub.name_hint)
check_func(new_sub, get_ref_sub())
@@ -413,7 +413,7 @@ def test_sequential_pass():
passes = [module_pass, function_pass]
sequential = tvm.transform.Sequential(opt_level=1, passes=passes)
required = ["mod_transform", "func_transform"]
- with relay.build_config(required_pass=required):
+ with tvm.transform.PassContext(required_pass=required):
ret_mod = sequential(mod)
# Check the abs function is added.
@@ -490,7 +490,7 @@ def test_sequential_with_scoping():
])
mod = tvm.IRModule({"main": before()})
- with relay.build_config(opt_level=3):
+ with tvm.transform.PassContext(opt_level=3):
with tvm.target.create("llvm"):
mod = seq(mod)
@@ -515,7 +515,7 @@ def test_print_ir(capfd):
])
mod = tvm.IRModule({"main": func})
- with relay.build_config(opt_level=3):
+ with tvm.transform.PassContext(opt_level=3):
mod = seq(mod)
out = capfd.readouterr().err
@@ -549,7 +549,7 @@ def test_print_debug_callback():
assert __TRACE_COUNTER__ == 0
mod = tvm.IRModule({"main": func})
- with relay.build_config(opt_level=3, trace=_tracer):
+ with tvm.transform.PassContext(opt_level=3, trace=_tracer):
mod = seq(mod)
assert __TRACE_COUNTER__ == 3
diff --git a/tests/python/relay/test_pass_partition_graph.py
b/tests/python/relay/test_pass_partition_graph.py
index 354b616..23bf618 100644
--- a/tests/python/relay/test_pass_partition_graph.py
+++ b/tests/python/relay/test_pass_partition_graph.py
@@ -195,7 +195,7 @@ def check_result(mod, map_inputs, out_shape, result,
tol=1e-5, target="llvm",
def check_vm_result():
compile_engine.get().clear()
- with relay.build_config(opt_level=3):
+ with tvm.transform.PassContext(opt_level=3):
exe = relay.vm.compile(mod, target=target, params=params)
code, lib = exe.save()
lib = update_lib(lib)
@@ -210,7 +210,7 @@ def check_result(mod, map_inputs, out_shape, result,
tol=1e-5, target="llvm",
def check_graph_runtime_result():
compile_engine.get().clear()
- with relay.build_config(opt_level=3):
+ with tvm.transform.PassContext(opt_level=3):
json, lib, param = relay.build(mod, target=target, params=params)
lib = update_lib(lib)
rt_mod = tvm.contrib.graph_runtime.create(json, lib, ctx)
@@ -512,7 +512,7 @@ def test_function_lifting():
transform.AlterOpLayout(),
])
- with relay.build_config(opt_level=3):
+ with tvm.transform.PassContext(opt_level=3):
mod = opt_pass(mod)
return mod
@@ -595,7 +595,7 @@ def test_function_lifting_inline():
transform.Inline(),
])
- with relay.build_config(opt_level=3):
+ with tvm.transform.PassContext(opt_level=3):
mod = opt_pass(mod)
return mod
@@ -885,7 +885,8 @@ def test_dnnl_fuse():
transform.PartitionGraph()
])
- with relay.build_config(opt_level=3, disabled_pass=["AlterOpLayout"]):
+ with tvm.transform.PassContext(opt_level=3,
+ disabled_pass=["AlterOpLayout"]):
return composite_partition(mod)
def test_detect_pattern(pattern_table, include_bn, include_sigmoid,
diff --git a/tests/python/relay/test_simplify_fc_transpose.py
b/tests/python/relay/test_simplify_fc_transpose.py
index 537a5a2..e29038c 100644
--- a/tests/python/relay/test_simplify_fc_transpose.py
+++ b/tests/python/relay/test_simplify_fc_transpose.py
@@ -27,7 +27,7 @@ from tvm import relay
from tvm.relay.data_dep_optimization import simplify_fc_transpose
def run_func(func, params, x):
- with relay.build_config(opt_level=3):
+ with tvm.transform.PassContext(opt_level=3):
graph, lib, new_params = relay.build(func, "llvm", params=params)
from tvm.contrib import graph_runtime
diff --git a/tests/python/relay/test_sparse_dense_convert.py
b/tests/python/relay/test_sparse_dense_convert.py
index c4f0572..e0204ae 100644
--- a/tests/python/relay/test_sparse_dense_convert.py
+++ b/tests/python/relay/test_sparse_dense_convert.py
@@ -46,7 +46,7 @@ def random_bsr_matrix(M, N, BS_R, BS_C, density,
dtype="float32"):
return s
def run_func(func, params, x):
- with relay.build_config(opt_level=3):
+ with tvm.transform.PassContext(opt_level=3):
graph, lib, new_params = relay.build(func, "llvm", params=params)
from tvm.contrib import graph_runtime
diff --git a/tests/python/unittest/test_runtime_module_export.py
b/tests/python/unittest/test_runtime_module_export.py
index fce7d2f..8473a67 100644
--- a/tests/python/unittest/test_runtime_module_export.py
+++ b/tests/python/unittest/test_runtime_module_export.py
@@ -67,7 +67,7 @@ def test_mod_export():
resnet18_mod, resnet18_params =
relay.testing.resnet.get_workload(num_layers=18)
resnet50_mod, resnet50_params =
relay.testing.resnet.get_workload(num_layers=50)
- with relay.build_config(opt_level=3):
+ with tvm.transform.PassContext(opt_level=3):
_, resnet18_gpu_lib, _ = relay.build_module.build(resnet18_mod,
"cuda", params=resnet18_params)
_, resnet50_cpu_lib, _ = relay.build_module.build(resnet50_mod,
"llvm", params=resnet50_params)
@@ -93,7 +93,7 @@ def test_mod_export():
return
resnet18_mod, resnet18_params =
relay.testing.resnet.get_workload(num_layers=18)
- with relay.build_config(opt_level=3):
+ with tvm.transform.PassContext(opt_level=3):
_, resnet18_cpu_lib, _ = relay.build_module.build(resnet18_mod,
"llvm", params=resnet18_params)
A = te.placeholder((1024,), name='A')
@@ -177,7 +177,7 @@ def test_mod_export():
return
resnet18_mod, resnet18_params =
relay.testing.resnet.get_workload(num_layers=18)
- with relay.build_config(opt_level=3):
+ with tvm.transform.PassContext(opt_level=3):
_, resnet18_cpu_lib, _ = relay.build_module.build(resnet18_mod,
"llvm", params=resnet18_params)
A = te.placeholder((1024,), name='A')
diff --git a/tests/python/unittest/test_target_codegen_blob.py
b/tests/python/unittest/test_target_codegen_blob.py
index 719ddfe..7cd5793 100644
--- a/tests/python/unittest/test_target_codegen_blob.py
+++ b/tests/python/unittest/test_target_codegen_blob.py
@@ -31,7 +31,7 @@ def test_resnet18():
def verify(data):
mod, params = relay.testing.resnet.get_workload(num_layers=18)
- with relay.build_config(opt_level=3):
+ with tvm.transform.PassContext(opt_level=3):
graph, lib, graph_params = relay.build_module.build(mod, "llvm",
params=params)
ctx = tvm.cpu()
module = graph_runtime.create(graph, lib, ctx)
@@ -42,7 +42,7 @@ def test_resnet18():
return out
resnet18_mod, resnet18_params =
relay.testing.resnet.get_workload(num_layers=18)
- with relay.build_config(opt_level=3):
+ with tvm.transform.PassContext(opt_level=3):
graph, resnet18_gpu_lib, graph_params =
relay.build_module.build(resnet18_mod, "cuda", params=resnet18_params)
from tvm.contrib import util
diff --git a/tutorials/autotvm/tune_relay_arm.py
b/tutorials/autotvm/tune_relay_arm.py
index ffd3e8b..3b07097 100644
--- a/tutorials/autotvm/tune_relay_arm.py
+++ b/tutorials/autotvm/tune_relay_arm.py
@@ -311,7 +311,7 @@ def tune_and_evaluate(tuning_opt):
# compile kernels with history best records
with autotvm.apply_history_best(log_file):
print("Compile...")
- with relay.build_config(opt_level=3):
+ with tvm.transform.PassContext(opt_level=3):
graph, lib, params = relay.build_module.build(
mod, target=target, params=params)
diff --git a/tutorials/autotvm/tune_relay_cuda.py
b/tutorials/autotvm/tune_relay_cuda.py
index 4195075..a6fe45b 100644
--- a/tutorials/autotvm/tune_relay_cuda.py
+++ b/tutorials/autotvm/tune_relay_cuda.py
@@ -222,7 +222,7 @@ def tune_and_evaluate(tuning_opt):
# compile kernels with history best records
with autotvm.apply_history_best(log_file):
print("Compile...")
- with relay.build_config(opt_level=3):
+ with tvm.transform.PassContext(opt_level=3):
graph, lib, params = relay.build_module.build(
mod, target=target, params=params)
diff --git a/tutorials/autotvm/tune_relay_mobile_gpu.py
b/tutorials/autotvm/tune_relay_mobile_gpu.py
index ad74608..4748f41 100644
--- a/tutorials/autotvm/tune_relay_mobile_gpu.py
+++ b/tutorials/autotvm/tune_relay_mobile_gpu.py
@@ -308,7 +308,7 @@ def tune_and_evaluate(tuning_opt):
# compile kernels with history best records
with autotvm.apply_history_best(log_file):
print("Compile...")
- with relay.build_config(opt_level=3):
+ with tvm.transform.PassContext(opt_level=3):
graph, lib, params = relay.build_module.build(
mod, target=target, params=params, target_host=target_host)
# export library
diff --git a/tutorials/autotvm/tune_relay_x86.py
b/tutorials/autotvm/tune_relay_x86.py
index 15ce2de..dcc5b25 100644
--- a/tutorials/autotvm/tune_relay_x86.py
+++ b/tutorials/autotvm/tune_relay_x86.py
@@ -189,7 +189,7 @@ def tune_and_evaluate(tuning_opt):
# compile kernels with graph-level best records
with autotvm.apply_graph_best(graph_opt_sch_file):
print("Compile...")
- with relay.build_config(opt_level=3):
+ with tvm.transform.PassContext(opt_level=3):
graph, lib, params = relay.build_module.build(
mod, target=target, params=params)
diff --git a/tutorials/dev/relay_pass_infra.py
b/tutorials/dev/relay_pass_infra.py
index 980d96c..df40733 100644
--- a/tutorials/dev/relay_pass_infra.py
+++ b/tutorials/dev/relay_pass_infra.py
@@ -160,7 +160,7 @@ print(mod1)
# however, provides a configuration interface
# for users to customize the optimization level that they want to execute.
-with relay.build_config(opt_level=3):
+with tvm.transform.PassContext(opt_level=3):
mod2 = seq(mod)
print(mod2)
@@ -173,7 +173,7 @@ print(mod2)
# EliminateCommonSubexpr as following. The printed module will again show two
# identical addition operations.
-with relay.build_config(opt_level=3, disabled_pass=["EliminateCommonSubexpr"]):
+with tvm.transform.PassContext(opt_level=3,
disabled_pass=["EliminateCommonSubexpr"]):
mod3 = seq(mod)
print(mod3)
@@ -182,12 +182,12 @@ print(mod3)
# provides a means to make pass target-aware. For example, the layout
# alteration pass falls in such category.
-with relay.build_config(opt_level=3):
+with tvm.transform.PassContext(opt_level=3):
mod4 = seq(mod)
print(mod4)
seq1 = tvm.transform.Sequential([relay.transform.AlterOpLayout()])
-with relay.build_config(opt_level=3):
+with tvm.transform.PassContext(opt_level=3):
with tvm.target.create("llvm"):
mod5 = seq1(mod)
print(mod5)
@@ -242,7 +242,7 @@ seq =
tvm.transform.Sequential([relay.transform.FoldConstant(),
relay.transform.EliminateCommonSubexpr(),
relay.transform.FuseOps(),
tvm.transform.PrintIR()])
-with relay.build_config(opt_level=3):
+with tvm.transform.PassContext(opt_level=3):
mod = seq(mod)
print("done")
diff --git a/tutorials/frontend/build_gcn.py b/tutorials/frontend/build_gcn.py
index 6ac518e..19719a5 100644
--- a/tutorials/frontend/build_gcn.py
+++ b/tutorials/frontend/build_gcn.py
@@ -336,7 +336,7 @@ func = relay.build_module.bind_params_by_name(func, params)
mod = tvm.IRModule()
mod["main"] = func
# Build with Relay
-with relay.build_config(opt_level=0): # Currently only support opt_level=0
+with tvm.transform.PassContext(opt_level=0): # Currently only support
opt_level=0
graph, lib, params = relay.build(mod, target, params=params)
# Generate graph runtime
diff --git a/tutorials/frontend/deploy_model_on_android.py
b/tutorials/frontend/deploy_model_on_android.py
index 17ec9cb..bc5b523 100644
--- a/tutorials/frontend/deploy_model_on_android.py
+++ b/tutorials/frontend/deploy_model_on_android.py
@@ -263,7 +263,7 @@ input_name = 'input_1'
shape_dict = {input_name: x.shape}
mod, params = relay.frontend.from_keras(keras_mobilenet_v2, shape_dict)
-with relay.build_config(opt_level=3):
+with tvm.transform.PassContext(opt_level=3):
graph, lib, params = relay.build(mod, target=target,
target_host=target_host, params=params)
diff --git a/tutorials/frontend/deploy_model_on_rasp.py
b/tutorials/frontend/deploy_model_on_rasp.py
index ef707fe..25df341 100644
--- a/tutorials/frontend/deploy_model_on_rasp.py
+++ b/tutorials/frontend/deploy_model_on_rasp.py
@@ -179,7 +179,7 @@ else:
# The above line is a simple form of
# target = tvm.target.create('llvm -device=arm_cpu -model=bcm2837
-target=armv7l-linux-gnueabihf -mattr=+neon')
-with relay.build_config(opt_level=3):
+with tvm.transform.PassContext(opt_level=3):
graph, lib, params = relay.build(func, target, params=params)
# After `relay.build`, you will get three return values: graph,
diff --git a/tutorials/frontend/deploy_prequantized.py
b/tutorials/frontend/deploy_prequantized.py
index 4027977..d6183d6 100644
--- a/tutorials/frontend/deploy_prequantized.py
+++ b/tutorials/frontend/deploy_prequantized.py
@@ -81,7 +81,7 @@ def get_synset():
def run_tvm_model(mod, params, input_name, inp, target="llvm"):
- with relay.build_config(opt_level=3):
+ with tvm.transform.PassContext(opt_level=3):
json, lib, params = relay.build(mod, target=target, params=params)
runtime = tvm.contrib.graph_runtime.create(json, lib, tvm.context(target,
0))
diff --git a/tutorials/frontend/deploy_prequantized_tflite.py
b/tutorials/frontend/deploy_prequantized_tflite.py
index 5fd6837..ecd283a 100644
--- a/tutorials/frontend/deploy_prequantized_tflite.py
+++ b/tutorials/frontend/deploy_prequantized_tflite.py
@@ -198,7 +198,7 @@ mod, params = relay.frontend.from_tflite(tflite_model,
# Lets now the compile the Relay module. We use the "llvm" target here. Please
replace it with the
# target platform that you are interested in.
target = 'llvm'
-with relay.build_config(opt_level=3):
+with tvm.transform.PassContext(opt_level=3):
graph, lib, params = relay.build_module.build(mod, target=target,
params=params)
diff --git a/tutorials/frontend/deploy_ssd_gluoncv.py
b/tutorials/frontend/deploy_ssd_gluoncv.py
index 6126df0..e2fc3c5 100644
--- a/tutorials/frontend/deploy_ssd_gluoncv.py
+++ b/tutorials/frontend/deploy_ssd_gluoncv.py
@@ -87,7 +87,7 @@ block = model_zoo.get_model(model_name, pretrained=True)
def build(target):
mod, params = relay.frontend.from_mxnet(block, {"data": dshape})
- with relay.build_config(opt_level=3):
+ with tvm.transform.PassContext(opt_level=3):
graph, lib, params = relay.build(mod, target, params=params)
return graph, lib, params
diff --git a/tutorials/frontend/from_caffe2.py
b/tutorials/frontend/from_caffe2.py
index 8fad80d..5988525 100644
--- a/tutorials/frontend/from_caffe2.py
+++ b/tutorials/frontend/from_caffe2.py
@@ -82,13 +82,13 @@ shape_dict = {input_name: data.shape}
dtype_dict = {input_name: data.dtype}
# parse Caffe2 model and convert into Relay computation graph
-from tvm import relay
+from tvm import relay, transform
mod, params = relay.frontend.from_caffe2(resnet50.init_net,
resnet50.predict_net, shape_dict, dtype_dict)
# compile the model
# target x86 CPU
target = 'llvm'
-with relay.build_config(opt_level=3):
+with transform.PassContext(opt_level=3):
graph, lib, params = relay.build(mod, target, params=params)
######################################################################
diff --git a/tutorials/frontend/from_coreml.py
b/tutorials/frontend/from_coreml.py
index 2a0c8db..beac4832 100644
--- a/tutorials/frontend/from_coreml.py
+++ b/tutorials/frontend/from_coreml.py
@@ -74,7 +74,7 @@ shape_dict = {'image': x.shape}
# Parse CoreML model and convert into Relay computation graph
mod, params = relay.frontend.from_coreml(mlmodel, shape_dict)
-with relay.build_config(opt_level=3):
+with tvm.transform.PassContext(opt_level=3):
graph, lib, params = relay.build(mod,
target,
params=params)
diff --git a/tutorials/frontend/from_darknet.py
b/tutorials/frontend/from_darknet.py
index e2c1ea5..6d84463 100644
--- a/tutorials/frontend/from_darknet.py
+++ b/tutorials/frontend/from_darknet.py
@@ -100,7 +100,7 @@ ctx = tvm.cpu(0)
data = np.empty([batch_size, net.c, net.h, net.w], dtype)
shape = {'data': data.shape}
print("Compiling the model...")
-with relay.build_config(opt_level=3):
+with tvm.transform.PassContext(opt_level=3):
graph, lib, params = relay.build(mod,
target=target,
target_host=target_host,
diff --git a/tutorials/frontend/from_keras.py b/tutorials/frontend/from_keras.py
index 928a8ac..7ece790 100644
--- a/tutorials/frontend/from_keras.py
+++ b/tutorials/frontend/from_keras.py
@@ -79,7 +79,7 @@ mod, params = relay.frontend.from_keras(keras_resnet50,
shape_dict)
# compile the model
target = 'cuda'
ctx = tvm.gpu(0)
-with relay.build_config(opt_level=3):
+with tvm.transform.PassContext(opt_level=3):
executor = relay.build_module.create_executor('graph', mod, ctx, target)
######################################################################
diff --git a/tutorials/frontend/from_mxnet.py b/tutorials/frontend/from_mxnet.py
index d0e4c4a..6e6b2d7 100644
--- a/tutorials/frontend/from_mxnet.py
+++ b/tutorials/frontend/from_mxnet.py
@@ -90,7 +90,7 @@ func = relay.Function(func.params,
relay.nn.softmax(func.body), None, func.type_
######################################################################
# now compile the graph
target = 'cuda'
-with relay.build_config(opt_level=3):
+with tvm.transform.PassContext(opt_level=3):
graph, lib, params = relay.build(func, target, params=params)
######################################################################
diff --git a/tutorials/frontend/from_onnx.py b/tutorials/frontend/from_onnx.py
index 766451c..9973a08 100644
--- a/tutorials/frontend/from_onnx.py
+++ b/tutorials/frontend/from_onnx.py
@@ -74,7 +74,7 @@ input_name = '1'
shape_dict = {input_name: x.shape}
mod, params = relay.frontend.from_onnx(onnx_model, shape_dict)
-with relay.build_config(opt_level=1):
+with tvm.transform.PassContext(opt_level=1):
intrp = relay.build_module.create_executor('graph', mod, tvm.cpu(0),
target)
######################################################################
diff --git a/tutorials/frontend/from_pytorch.py
b/tutorials/frontend/from_pytorch.py
index 8354b0e..53d29a9 100644
--- a/tutorials/frontend/from_pytorch.py
+++ b/tutorials/frontend/from_pytorch.py
@@ -101,7 +101,7 @@ mod, params = relay.frontend.from_pytorch(scripted_model,
target = 'llvm'
target_host = 'llvm'
ctx = tvm.cpu(0)
-with relay.build_config(opt_level=3):
+with tvm.transform.PassContext(opt_level=3):
graph, lib, params = relay.build(mod,
target=target,
target_host=target_host,
diff --git a/tutorials/frontend/from_tensorflow.py
b/tutorials/frontend/from_tensorflow.py
index 0ebd733..b7b3d69 100644
--- a/tutorials/frontend/from_tensorflow.py
+++ b/tutorials/frontend/from_tensorflow.py
@@ -144,7 +144,7 @@ print("Tensorflow protobuf imported to relay frontend.")
# params: final params after compilation.
# lib: target library which can be deployed on target with TVM runtime.
-with relay.build_config(opt_level=3):
+with tvm.transform.PassContext(opt_level=3):
graph, lib, params = relay.build(mod,
target=target,
target_host=target_host,
diff --git a/tutorials/frontend/from_tflite.py
b/tutorials/frontend/from_tflite.py
index e01a4ec..35a308c 100644
--- a/tutorials/frontend/from_tflite.py
+++ b/tutorials/frontend/from_tflite.py
@@ -128,14 +128,14 @@ input_shape = (1, 224, 224, 3)
input_dtype = "float32"
# Parse TFLite model and convert it to a Relay module
-from tvm import relay
+from tvm import relay, transform
mod, params = relay.frontend.from_tflite(tflite_model,
shape_dict={input_tensor:
input_shape},
dtype_dict={input_tensor:
input_dtype})
# Build the module against to x86 CPU
target = "llvm"
-with relay.build_config(opt_level=3):
+with transform.PassContext(opt_level=3):
graph, lib, params = relay.build(mod, target, params=params)
######################################################################
diff --git a/tutorials/relay_quick_start.py b/tutorials/relay_quick_start.py
index b2174a0..e52a99a 100644
--- a/tutorials/relay_quick_start.py
+++ b/tutorials/relay_quick_start.py
@@ -96,7 +96,7 @@ print(mod.astext(show_meta_data=False))
opt_level = 3
target = tvm.target.cuda()
-with relay.build_config(opt_level=opt_level):
+with tvm.transform.PassContext(opt_level=opt_level):
graph, lib, params = relay.build(mod, target, params=params)
#####################################################################
diff --git a/vta/scripts/tune_resnet.py b/vta/scripts/tune_resnet.py
index 26c240e..2d358d3 100644
--- a/vta/scripts/tune_resnet.py
+++ b/vta/scripts/tune_resnet.py
@@ -127,7 +127,7 @@ def compile_network(opt, env, target):
# Perform quantization in Relay
# Note: We set opt_level to 3 in order to fold batch norm
- with relay.build_config(opt_level=3):
+ with tvm.transform.PassContext(opt_level=3):
with relay.quantize.qconfig(global_scale=8.0,
skip_conv_layers=[0]):
relay_prog = relay.quantize.quantize(mod["main"], params=params)
@@ -272,7 +272,7 @@ if __name__ == '__main__':
# Compile network
print("Compiling network with best tuning parameters...")
if target.device_name != "vta":
- with relay.build_config(opt_level=3,
disabled_pass={"AlterOpLayout"}):
+ with tvm.transform.PassContext(opt_level=3,
disabled_pass={"AlterOpLayout"}):
graph, lib, params = relay.build(
relay_prog, target=target,
params=params, target_host=env.target_host)
diff --git a/vta/tutorials/autotvm/tune_relay_vta.py
b/vta/tutorials/autotvm/tune_relay_vta.py
index 63106a5..a92b1ee 100644
--- a/vta/tutorials/autotvm/tune_relay_vta.py
+++ b/vta/tutorials/autotvm/tune_relay_vta.py
@@ -92,7 +92,7 @@ def compile_network(env, target, model, start_pack,
stop_pack):
# Perform quantization in Relay
# Note: We set opt_level to 3 in order to fold batch norm
- with relay.build_config(opt_level=3):
+ with tvm.transform.PassContext(opt_level=3):
with relay.quantize.qconfig(global_scale=8.0, skip_conv_layers=[0]):
mod = relay.quantize.quantize(mod, params=params)
diff --git a/vta/tutorials/frontend/deploy_classification.py
b/vta/tutorials/frontend/deploy_classification.py
index 7ca4b98..3a36785 100644
--- a/vta/tutorials/frontend/deploy_classification.py
+++ b/vta/tutorials/frontend/deploy_classification.py
@@ -171,7 +171,7 @@ with autotvm.tophub.context(target):
if target.device_name == "vta":
# Perform quantization in Relay
# Note: We set opt_level to 3 in order to fold batch norm
- with relay.build_config(opt_level=3):
+ with tvm.transform.PassContext(opt_level=3):
with relay.quantize.qconfig(global_scale=8.0,
skip_conv_layers=[0]):
mod = relay.quantize.quantize(mod, params=params)
diff --git a/vta/tutorials/frontend/deploy_detection.py
b/vta/tutorials/frontend/deploy_detection.py
index efcd2c4..5039488 100644
--- a/vta/tutorials/frontend/deploy_detection.py
+++ b/vta/tutorials/frontend/deploy_detection.py
@@ -207,7 +207,7 @@ with autotvm.tophub.context(target):
if target.device_name == "vta":
# Perform quantization in Relay
# Note: We set opt_level to 3 in order to fold batch norm
- with relay.build_config(opt_level=3):
+ with tvm.transform.PassContext(opt_level=3):
with relay.quantize.qconfig(global_scale=33.0,
skip_conv_layers=[0],
store_lowbit_output=True,
