分析 TVM 自动量化结果#
import numpy as np
from tvm import relay
import tvm
from tvm_book.tvm_utils.llvm_utils import run_llvm_graph
from tqdm import tqdm
from tvm_book.tvm_utils.relay_pattern import *
def load_model(input_shape=[1, 3, 224, 224]):
"""加载前端模型"""
import torch
from torchvision.models import mobilenet_v2 as model
from torchvision.models.mobilenet import MobileNet_V2_Weights
model = model(weights=MobileNet_V2_Weights.IMAGENET1K_V1)
data = torch.randn(*input_shape)
return torch.jit.trace(model.eval(), data)
size = 224, 224
input_shape = (1, 3, *size)
input_name = "data"
traced_model = load_model(input_shape).eval()
# 将前端模型翻译为 relay 模型
origin_mod, origin_params = relay.frontend.from_pytorch(traced_model, [(input_name, input_shape)])
先以 mod 子图为例研究定义量化过程：
from tvm_book.tvm_utils.split_graph import graph_split
split_conf = [{"op_name": "clip", "op_index": 1}]
mod = graph_split(origin_mod["main"], split_conf)[0]
print(mod["main"])
记录量化过程：
# 定义校准数据集
def data_iter(input_name, input_shape, num=1):
for _ in range(num):
yield {input_name: np.random.normal(size=input_shape)}
dataset = data_iter(input_name, input_shape)
calibrate_pass = tvm.transform.module_pass(
relay.quantize.calibrate(dataset), opt_level=1, name="QuantizeCalibrate"
)
with tvm.transform.PassContext(opt_level=3):
with relay.quantize.qconfig(
calibrate_mode="kl_divergence",
weight_scale="max",
skip_conv_layers=[],
skip_dense_layer=False
):
# 量化前准备
opt_mod = relay.quantize.prerequisite_optimize(mod, origin_params)
# 划分计算图
partition_mod = relay.quantize.partition()(opt_mod)
# 注解计算图
annotate_mod = relay.quantize.annotate()(partition_mod)
# 校准量化
calibrate_mod = calibrate_pass(annotate_mod)
# 量化实现
qmod = relay.quantize.realize()(calibrate_mod)
定义融合规则：
# 配置融合规则
compiler_name = "ccompiler"
pattern_table = [
(f"{compiler_name}.conv_add_relu_max_pool2d", make_conv_add_relu_max_pool2d_pattern()),
(f"{compiler_name}.conv2d_transpose_add_activate", make_conv2d_transpose_add_activate_pattern()),
(f"{compiler_name}.conv_add_activate", make_conv_add_activate_pattern()),
(f"{compiler_name}.max_pool2d", make_max_pool2d_pattern()),
(f"{compiler_name}.dense_add", make_dense_add_pattern()),
(f"{compiler_name}.adaptive_avg_pool2d", make_adaptive_avg_pool2d_pattern()),
(f"{compiler_name}.avg_pool2dd", make_avg_pool2d_pattern()),
(f"{compiler_name}.add_multiply_add", make_add_multiply_add_pattern()),
(f"{compiler_name}.add", make_add_pattern()),
(f"{compiler_name}.multiply", make_multiply_pattern()),
# (f"{compiler_name}.strided_slice", make_strided_slice_pattern()),
]
merge_passes = tvm.transform.Sequential([
relay.transform.InferType(),
relay.transform.MergeComposite(pattern_table),
# relay.transform.ToANormalForm(),
# relay.transform.ToGraphNormalForm(),
])
with tvm.transform.PassContext(opt_level=3):
partition_mod_f = merge_passes(partition_mod)
calibrate_mod_f = merge_passes(calibrate_mod)
print(calibrate_mod_f)
print(partition_mod_f["main"])
部分量化#
from tvm.relay.testing import run_opt_pass
from tvm.relay.expr import Constant, Call, Let
from tvm.relay.function import Function, FunctionWithFields
from tvm.relay import GlobalVar
from tvm.relay.op.annotation import compiler_begin, compiler_end
class DefuseMutator(tvm.relay.ExprMutator):
def __init__(self, mod):
super().__init__()
self.mod = mod
self.bind_params = {}
# def visit_let(self, let):
# new_var = self.visit(let.var)
# new_val = self.visit(let.value)
# new_body = self.visit(let.body)
# if new_var == let.var and new_val == let.value and new_body == let.body:
# new_let = let
# else:
# new_let = Let(new_var, new_val, new_body)
# return new_let
def visit_call(self, call):
new_fn = self.visit(call.op)
new_args = [self.visit(arg) for arg in call.args]
if isinstance(new_fn, GlobalVar):
self.bind_params[new_fn.name_hint] = new_args
new_fn = self.mod[new_fn.name_hint].body
new_fn = run_opt_pass(new_fn, relay.transform.DefuseOps())
# _bind_dict = dict(zip(new_fn.params, new_args))
# new_fn = relay.bind(new_fn, _bind_dict)
new_call = Call(new_fn, new_args, call.attrs, call.type_args, call.span)
return new_call
@tvm.relay.transform.function_pass(opt_level=1)
class CalibrateGraphTransform:
def __init__(self, compiler):
self.reset()
self.compiler = compiler
def reset(self):
self._func_index = 0
self.bind_dict = {}
self.nodes = []
def transform_function(self, func, mod, ctx):
obj = self
class Replace(tvm.relay.ExprMutator):
def visit_call(self, call):
new_fn = self.visit(call.op)
new_args = [self.visit(arg) for arg in call.args]
if isinstance(new_fn, Function):
compiler = obj.compiler
func_name = f"f_{obj._func_index:04d}"
# obj.nodes.append(call)
new_args = [compiler_begin(arg, obj.compiler) for arg in new_args]
# obj.bind_dict[func_name] = new_args
# _bind_dict = dict(zip(new_fn.params, new_args))
# new_fn = relay.bind(new_fn, _bind_dict)
# if new_fn.attrs:
# name = new_fn.attrs["PartitionedFromPattern"].replace("relay.op.annotation.simulated_quantize_", "").replace("annotation.cast_hint_", "")
# if "conv" in name:
# data = new_args[0]
# old_data = new_fn.params[0]
# body = new_fn.body
# if data.op.name == "relay.op.annotation.simulated_quantize":
# ...
# mod[func_name] = new_fn
# # # new_args = new_fn.params
# new_fn = mod.get_global_var(func_name)
# obj._func_index += 1
else:
compiler = "default"
call = Call(new_fn, new_args, call.attrs, call.type_args, call.span)
return compiler_end(call, compiler)
return Replace().visit(func)
@tvm.relay.transform.function_pass(opt_level=1)
class PartitionGraphTransform:
def __init__(self):
self.reset()
def reset(self):
self._func_index = 0
self.bind_dict = {}
def transform_function(self, func, mod, ctx):
obj = self
class Replace(tvm.relay.ExprMutator):
def visit_call(self, call):
new_fn = self.visit(call.op)
new_args = [self.visit(arg) for arg in call.args]
if isinstance(call.op, Function):
func_name = f"f_{obj._func_index:04d}"
_bind_dict = dict(zip(new_fn.params, new_args))
_new_fn = relay.bind(new_fn, _bind_dict)
_new_fn = run_opt_pass(_new_fn, relay.transform.DefuseOps())
obj.bind_dict[func_name] = _new_fn
obj._func_index += 1
call = Call(new_fn, new_args, call.attrs, call.type_args, call.span)
return call
return Replace().visit(func)
calibrate_transform = CalibrateGraphTransform("test")
# partition_transform = PartitionGraphTransform()
with tvm.transform.PassContext(opt_level=3):
calibrate_mod_t = calibrate_transform(calibrate_mod_f)
# partition_mod_t = partition_transform(partition_mod_f)
# call = relay.Tuple(calibrate_transform.nodes)
# call = run_opt_pass(call, relay.transform.DefuseOps())
run_mod = relay.transform.ToANormalForm()(calibrate_mod_f)
from tvm.relay.expr import Var
@tvm.relay.transform.function_pass(opt_level=1)
class CalibrateGraphTransform:
def __init__(self, compiler):
self.reset()
self.compiler = compiler
def reset(self):
self._func_index = 0
self.bind_dict = {}
self.nodes = []
def transform_function(self, func, mod, ctx):
obj = self
class Replace(tvm.relay.ExprMutator):
def visit_call(self, call):
new_fn = self.visit(call.op)
new_args = [self.visit(arg) for arg in call.args]
# print(type(new_fn))
if isinstance(new_fn, Var):
compiler = obj.compiler
func_name = f"f_{obj._func_index:04d}"
obj.nodes.append(call)
new_args = [compiler_begin(arg, compiler) for arg in new_args]
# new_fn = run_opt_pass(new_fn, relay.transform.DefuseOps())
# obj.bind_dict[func_name] = new_args
# _bind_dict = dict(zip(new_fn.params, new_args))
# new_fn = relay.bind(new_fn, _bind_dict)
# if new_fn.attrs:
# name = new_fn.attrs["PartitionedFromPattern"].replace("relay.op.annotation.simulated_quantize_", "").replace("annotation.cast_hint_", "")
# if "conv" in name:
# data = new_args[0]
# old_data = new_fn.params[0]
# body = new_fn.body
# if data.op.name == "relay.op.annotation.simulated_quantize":
# ...
new_fn = compiler_begin(new_fn, compiler)
# mod[func_name] = new_fn
# # # new_args = new_fn.params
# new_fn = mod.get_global_var(func_name)
# obj._func_index += 1
else:
compiler = "default"
call = Call(new_fn, new_args, call.attrs, call.type_args, call.span)
return compiler_end(call, compiler)
return Replace().visit(func)
# def visit_let(self, let):
# new_var = self.visit(let.var)
# new_val = self.visit(let.value)
# new_body = self.visit(let.body)
# if new_var == let.var and new_val == let.value and new_body == let.body:
# new_let = let
# else:
# new_let = Let(new_var, new_val, new_body)
# if isinstance(let.value, Function):
# func_name = f"f_{obj._func_index:04d}"
# obj.nodes.append(let)
# # mod[func_name] = tvm.IRModule.from_expr(let.body)["main"]
# obj._func_index += 1
# # new_val, new_body = new_body, new_val
# return new_let
return Replace().visit(func)
calibrate_transform = CalibrateGraphTransform("test")
with tvm.transform.PassContext(opt_level=3):
calibrate_mod_t = calibrate_transform(run_mod)
nodes = calibrate_transform.nodes
node = nodes[0]
print(relay.transform.PartitionGraph()(calibrate_mod_t))
---------------------------------------------------------------------------
InternalError Traceback (most recent call last)
/media/pc/data/lxw/ai/tvm/xinetzone/tvm-book/doc/quantize/analysis.ipynb 单元格 19 line 1
----> <a href='vscode-notebook-cell://ssh-remote%2B10.16.11.3/media/pc/data/lxw/ai/tvm/xinetzone/tvm-book/doc/quantize/analysis.ipynb#X63sdnNjb2RlLXJlbW90ZQ%3D%3D?line=0'>1</a> print(relay.transform.PartitionGraph()(calibrate_mod_t))
File /media/pc/data/lxw/ai/tvm/xinetzone/__pypackages__/3.10/lib/tvm/ir/transform.py:238, in Pass.__call__(self, mod)
224 def __call__(self, mod):
225 """Execute the pass. Note that for sequential pass, the dependency among
226 different passes will be resolved in the backend.
227
(...)
236 The updated module after applying this pass.
237 """
--> 238 return _ffi_transform_api.RunPass(self, mod)
File /media/pc/data/lxw/ai/tvm/xinetzone/__pypackages__/3.10/lib/tvm/_ffi/_ctypes/packed_func.py:239, in PackedFuncBase.__call__(self, *args)
227 ret_tcode = ctypes.c_int()
228 if (
229 _LIB.TVMFuncCall(
230 self.handle,
(...)
237 != 0
238 ):
--> 239 raise_last_ffi_error()
240 _ = temp_args
241 _ = args
File /media/pc/data/lxw/ai/tvm/xinetzone/__pypackages__/3.10/lib/tvm/_ffi/base.py:476, in raise_last_ffi_error()
470 # The exception PyObject may contain a large amount of state,
471 # including all stack frames that may be inspected in a later
472 # PDB post-mortem. Therefore, we must make sure to remove the
473 # underlying PyObject* from the C++ side after we retrieve it.
474 _LIB.TVMDropLastPythonError()
--> 476 raise py_err
InternalError: Traceback (most recent call last):
104: tvm::runtime::PackedFuncObj::Extractor<tvm::runtime::PackedFuncSubObj<tvm::runtime::TypedPackedFunc<tvm::IRModule (tvm::transform::Pass, tvm::IRModule)>::AssignTypedLambda<tvm::transform::{lambda(tvm::transform::Pass, tvm::IRModule)#7}>(tvm::transform::{lambda(tvm::transform::Pass, tvm::IRModule)#7}, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >)::{lambda(tvm::runtime::TVMArgs const&, tvm::runtime::TVMRetValue*)#1}> >::Call(tvm::runtime::PackedFuncObj const*, tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*)
103: tvm::transform::Pass::operator()(tvm::IRModule) const
102: tvm::transform::Pass::operator()(tvm::IRModule, tvm::transform::PassContext const&) const
101: tvm::transform::SequentialNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const
100: tvm::transform::Pass::operator()(tvm::IRModule, tvm::transform::PassContext const&) const
99: tvm::transform::ModulePassNode::operator()(tvm::IRModule, tvm::transform::PassContext const&) const
98: _ZN3tvm7runtime13PackedFuncObj
97: tvm::runtime::TypedPackedFunc<tvm::IRModule (tvm::IRModule, tvm::transform::PassContext)>::AssignTypedLambda<tvm::relay::transform::PartitionGraph(tvm::runtime::String, bool)::{lambda(tvm::IRModule, tvm::transform::PassContext)#3}>(tvm::relay::transform::PartitionGraph(tvm::runtime::String, bool)::{lambda(tvm::IRModule, tvm::transform::PassContext)#3})::{lambda(tvm::runtime::TVMArgs const&, tvm::runtime::TVMRetValue*)#1}::operator()(tvm::runtime::TVMArgs const&, tvm::runtime::TVMRetValue*) const
96: tvm::relay::partitioning::Partitioner::Partition()
95: tvm::relay::MixedModeMutator::VisitExpr(tvm::RelayExpr const&)
94: void tvm::relay::ExpandDataflow<tvm::relay::MixedModeMutator::VisitExpr(tvm::RelayExpr const&)::{lambda(tvm::RelayExpr const&)#1}, tvm::relay::MixedModeMutator::VisitExpr(tvm::RelayExpr const&)::{lambda(tvm::RelayExpr const&)#2}, tvm::relay::ExpandDataflow<tvm::relay::MixedModeMutator::VisitExpr(tvm::RelayExpr const&)::{lambda(tvm::RelayExpr const&)#1}, tvm::relay::MixedModeMutator::VisitExpr(tvm::RelayExpr const&)::{lambda(tvm::RelayExpr const&)#2}>(tvm::RelayExpr, tvm::relay::MixedModeMutator::VisitExpr(tvm::RelayExpr const&)::{lambda(tvm::RelayExpr const&)#1}, tvm::relay::MixedModeMutator::VisitExpr(tvm::RelayExpr const&)::{lambda(tvm::RelayExpr const&)#2})::{lambda(tvm::RelayExpr const&)#1}>(tvm::RelayExpr, tvm::relay::MixedModeMutator::VisitExpr(tvm::RelayExpr const&)::{lambda(tvm::RelayExpr const&)#1}, tvm::relay::MixedModeMutator::VisitExpr(tvm::RelayExpr const&)::{lambda(tvm::RelayExpr const&)#2}, tvm::relay::ExpandDataflow<tvm::relay::MixedModeMutator::VisitExpr(tvm::RelayExpr const&)::{lambda(tvm::RelayExpr const&)#1}, tvm::relay::MixedModeMutator::VisitExpr(tvm::RelayExpr const&)::{lambda(tvm::RelayExpr const&)#2}>(tvm::RelayExpr, tvm::relay::MixedModeMutator::VisitExpr(tvm::RelayExpr const&)::{lambda(tvm::RelayExpr const&)#1}, tvm::relay::MixedModeMutator::VisitExpr(tvm::RelayExpr const&)::{lambda(tvm::RelayExpr const&)#2})::{lambda(tvm::RelayExpr const&)#1}) [clone .isra.0]
93: tvm::relay::MixedModeMutator::VisitLeaf(tvm::RelayExpr const&)
92: _ZN3tvm5relay16MixedModeMutator17DispatchVisitExprERKNS_9RelayExp
91: tvm::relay::ExprMutator::VisitExpr(tvm::RelayExpr const&)
90: _ZZN3tvm5relay11ExprFunctorIFNS_9RelayExprERKS2_EE10InitVTableEvENUlRKNS_7r
89: tvm::relay::ExprMutator::VisitExpr_(tvm::relay::LetNode const*)
88: tvm::relay::MixedModeMutator::VisitExpr(tvm::RelayExpr const&)
87: void tvm::relay::ExpandDataflow<tvm::relay::MixedModeMutator::VisitExpr(tvm::RelayExpr const&)::{lambda(tvm::RelayExpr const&)#1}, tvm::relay::MixedModeMutator::VisitExpr(tvm::RelayExpr const&)::{lambda(tvm::RelayExpr const&)#2}, tvm::relay::ExpandDataflow<tvm::relay::MixedModeMutator::VisitExpr(tvm::RelayExpr const&)::{lambda(tvm::RelayExpr const&)#1}, tvm::relay::MixedModeMutator::VisitExpr(tvm::RelayExpr const&)::{lambda(tvm::RelayExpr const&)#2}>(tvm::RelayExpr, tvm::relay::MixedModeMutator::VisitExpr(tvm::RelayExpr const&)::{lambda(tvm::RelayExpr const&)#1}, tvm::relay::MixedModeMutator::VisitExpr(tvm::RelayExpr const&)::{lambda(tvm::RelayExpr const&)#2})::{lambda(tvm::RelayExpr const&)#1}>(tvm::RelayExpr, tvm::relay::MixedModeMutator::VisitExpr(tvm::RelayExpr const&)::{lambda(tvm::RelayExpr const&)#1}, tvm::relay::MixedModeMutator::VisitExpr(tvm::RelayExpr const&)::{lambda(tvm::RelayExpr const&)#2}, tvm::relay::ExpandDataflow<tvm::relay::MixedModeMutator::VisitExpr(tvm::RelayExpr const&)::{lambda(tvm::RelayExpr const&)#1}, tvm::relay::MixedModeMutator::VisitExpr(tvm::RelayExpr const&)::{lambda(tvm::RelayExpr const&)#2}>(tvm::RelayExpr, tvm::relay::MixedModeMutator::VisitExpr(tvm::RelayExpr const&)::{lambda(tvm::RelayExpr const&)#1}, tvm::relay::MixedModeMutator::VisitExpr(tvm::RelayExpr const&)::{lambda(tvm::RelayExpr const&)#2})::{lambda(tvm::RelayExpr const&)#1}) [clone .isra.0]
86: tvm::relay::MixedModeMutator::VisitLeaf(tvm::RelayExpr const&)
85: _ZN3tvm5relay16MixedModeMutator17DispatchVisitExprERKNS_9RelayExp
84: tvm::relay::ExprMutator::VisitExpr(tvm::RelayExpr const&)
83: _ZZN3tvm5relay11ExprFunctorIFNS_9RelayExprERKS2_EE10InitVTableEvENUlRKNS_7r
82: tvm::relay::ExprMutator::VisitExpr_(tvm::relay::LetNode const*)
81: tvm::relay::MixedModeMutator::VisitExpr(tvm::RelayExpr const&)
80: void tvm::relay::ExpandDataflow<tvm::relay::MixedModeMutator::VisitExpr(tvm::RelayExpr const&)::{lambda(tvm::RelayExpr const&)#1}, tvm::relay::MixedModeMutator::VisitExpr(tvm::RelayExpr const&)::{lambda(tvm::RelayExpr const&)#2}, tvm::relay::ExpandDataflow<tvm::relay::MixedModeMutator::VisitExpr(tvm::RelayExpr const&)::{lambda(tvm::RelayExpr const&)#1}, tvm::relay::MixedModeMutator::VisitExpr(tvm::RelayExpr const&)::{lambda(tvm::RelayExpr const&)#2}>(tvm::RelayExpr, tvm::relay::MixedModeMutator::VisitExpr(tvm::RelayExpr const&)::{lambda(tvm::RelayExpr const&)#1}, tvm::relay::MixedModeMutator::VisitExpr(tvm::RelayExpr const&)::{lambda(tvm::RelayExpr const&)#2})::{lambda(tvm::RelayExpr const&)#1}>(tvm::RelayExpr, tvm::relay::MixedModeMutator::VisitExpr(tvm::RelayExpr const&)::{lambda(tvm::RelayExpr const&)#1}, tvm::relay::MixedModeMutator::VisitExpr(tvm::RelayExpr const&)::{lambda(tvm::RelayExpr const&)#2}, tvm::relay::ExpandDataflow<tvm::relay::MixedModeMutator::VisitExpr(tvm::RelayExpr const&)::{lambda(tvm::RelayExpr const&)#1}, tvm::relay::MixedModeMutator::VisitExpr(tvm::RelayExpr const&)::{lambda(tvm::RelayExpr const&)#2}>(tvm::RelayExpr, tvm::relay::MixedModeMutator::VisitExpr(tvm::RelayExpr const&)::{lambda(tvm::RelayExpr const&)#1}, tvm::relay::MixedModeMutator::VisitExpr(tvm::RelayExpr const&)::{lambda(tvm::RelayExpr const&)#2})::{lambda(tvm::RelayExpr const&)#1}) [clone .isra.0]
79: tvm::relay::MixedModeMutator::VisitLeaf(tvm::RelayExpr const&)
78: _ZN3tvm5relay16MixedModeMutator17DispatchVisitExprERKNS_9RelayExp
77: tvm::relay::ExprMutator::VisitExpr(tvm::RelayExpr const&)
76: _ZZN3tvm5relay11ExprFunctorIFNS_9RelayExprERKS2_EE10InitVTableEvENUlRKNS_7r
75: tvm::relay::ExprMutator::VisitExpr_(tvm::relay::LetNode const*)
74: tvm::relay::MixedModeMutator::VisitExpr(tvm::RelayExpr const&)
73: void tvm::relay::ExpandDataflow<tvm::relay::MixedModeMutator::VisitExpr(tvm::RelayExpr const&)::{lambda(tvm::RelayExpr const&)#1}, tvm::relay::MixedModeMutator::VisitExpr(tvm::RelayExpr const&)::{lambda(tvm::RelayExpr const&)#2}, tvm::relay::ExpandDataflow<tvm::relay::MixedModeMutator::VisitExpr(tvm::RelayExpr const&)::{lambda(tvm::RelayExpr const&)#1}, tvm::relay::MixedModeMutator::VisitExpr(tvm::RelayExpr const&)::{lambda(tvm::RelayExpr const&)#2}>(tvm::RelayExpr, tvm::relay::MixedModeMutator::VisitExpr(tvm::RelayExpr const&)::{lambda(tvm::RelayExpr const&)#1}, tvm::relay::MixedModeMutator::VisitExpr(tvm::RelayExpr const&)::{lambda(tvm::RelayExpr const&)#2})::{lambda(tvm::RelayExpr const&)#1}>(tvm::RelayExpr, tvm::relay::MixedModeMutator::VisitExpr(tvm::RelayExpr const&)::{lambda(tvm::RelayExpr const&)#1}, tvm::relay::MixedModeMutator::VisitExpr(tvm::RelayExpr const&)::{lambda(tvm::RelayExpr const&)#2}, tvm::relay::ExpandDataflow<tvm::relay::MixedModeMutator::VisitExpr(tvm::RelayExpr const&)::{lambda(tvm::RelayExpr const&)#1}, tvm::relay::MixedModeMutator::VisitExpr(tvm::RelayExpr const&)::{lambda(tvm::RelayExpr const&)#2}>(tvm::RelayExpr, tvm::relay::MixedModeMutator::VisitExpr(tvm::RelayExpr const&)::{lambda(tvm::RelayExpr const&)#1}, tvm::relay::MixedModeMutator::VisitExpr(tvm::RelayExpr const&)::{lambda(tvm::RelayExpr const&)#2})::{lambda(tvm::RelayExpr const&)#1}) [clone .isra.0]
72: tvm::relay::MixedModeMutator::VisitLeaf(tvm::RelayExpr const&)
71: _ZN3tvm5relay16MixedModeMutator17DispatchVisitExprERKNS_9RelayExp
70: tvm::relay::ExprMutator::VisitExpr(tvm::RelayExpr const&)
69: _ZZN3tvm5relay11ExprFunctorIFNS_9RelayExprERKS2_EE10InitVTableEvENUlRKNS_7r
68: tvm::relay::ExprMutator::VisitExpr_(tvm::relay::LetNode const*)
67: tvm::relay::MixedModeMutator::VisitExpr(tvm::RelayExpr const&)
66: void tvm::relay::ExpandDataflow<tvm::relay::MixedModeMutator::VisitExpr(tvm::RelayExpr const&)::{lambda(tvm::RelayExpr const&)#1}, tvm::relay::MixedModeMutator::VisitExpr(tvm::RelayExpr const&)::{lambda(tvm::RelayExpr const&)#2}, tvm::relay::ExpandDataflow<tvm::relay::MixedModeMutator::VisitExpr(tvm::RelayExpr const&)::{lambda(tvm::RelayExpr const&)#1}, tvm::relay::MixedModeMutator::VisitExpr(tvm::RelayExpr const&)::{lambda(tvm::RelayExpr const&)#2}>(tvm::RelayExpr, tvm::relay::MixedModeMutator::VisitExpr(tvm::RelayExpr const&)::{lambda(tvm::RelayExpr const&)#1}, tvm::relay::MixedModeMutator::VisitExpr(tvm::RelayExpr const&)::{lambda(tvm::RelayExpr const&)#2})::{lambda(tvm::RelayExpr const&)#1}>(tvm::RelayExpr, tvm::relay::MixedModeMutator::VisitExpr(tvm::RelayExpr const&)::{lambda(tvm::RelayExpr const&)#1}, tvm::relay::MixedModeMutator::VisitExpr(tvm::RelayExpr const&)::{lambda(tvm::RelayExpr const&)#2}, tvm::relay::ExpandDataflow<tvm::relay::MixedModeMutator::VisitExpr(tvm::RelayExpr const&)::{lambda(tvm::RelayExpr const&)#1}, tvm::relay::MixedModeMutator::VisitExpr(tvm::RelayExpr const&)::{lambda(tvm::RelayExpr const&)#2}>(tvm::RelayExpr, tvm::relay::MixedModeMutator::VisitExpr(tvm::RelayExpr const&)::{lambda(tvm::RelayExpr const&)#1}, tvm::relay::MixedModeMutator::VisitExpr(tvm::RelayExpr const&)::{lambda(tvm::RelayExpr const&)#2})::{lambda(tvm::RelayExpr const&)#1}) [clone .isra.0]
65: tvm::relay::MixedModeMutator::VisitLeaf(tvm::RelayExpr const&)
64: _ZN3tvm5relay16MixedModeMutator17DispatchVisitExprERKNS_9RelayExp
63: tvm::relay::ExprMutator::VisitExpr(tvm::RelayExpr const&)
62: _ZZN3tvm5relay11ExprFunctorIFNS_9RelayExprERKS2_EE10InitVTableEvENUlRKNS_7r
61: tvm::relay::ExprMutator::VisitExpr_(tvm::relay::LetNode const*)
60: tvm::relay::MixedModeMutator::VisitExpr(tvm::RelayExpr const&)
59: void tvm::relay::ExpandDataflow<tvm::relay::MixedModeMutator::VisitExpr(tvm::RelayExpr const&)::{lambda(tvm::RelayExpr const&)#1}, tvm::relay::MixedModeMutator::VisitExpr(tvm::RelayExpr const&)::{lambda(tvm::RelayExpr const&)#2}, tvm::relay::ExpandDataflow<tvm::relay::MixedModeMutator::VisitExpr(tvm::RelayExpr const&)::{lambda(tvm::RelayExpr const&)#1}, tvm::relay::MixedModeMutator::VisitExpr(tvm::RelayExpr const&)::{lambda(tvm::RelayExpr const&)#2}>(tvm::RelayExpr, tvm::relay::MixedModeMutator::VisitExpr(tvm::RelayExpr const&)::{lambda(tvm::RelayExpr const&)#1}, tvm::relay::MixedModeMutator::VisitExpr(tvm::RelayExpr const&)::{lambda(tvm::RelayExpr const&)#2})::{lambda(tvm::RelayExpr const&)#1}>(tvm::RelayExpr, tvm::relay::MixedModeMutator::VisitExpr(tvm::RelayExpr const&)::{lambda(tvm::RelayExpr const&)#1}, tvm::relay::MixedModeMutator::VisitExpr(tvm::RelayExpr const&)::{lambda(tvm::RelayExpr const&)#2}, tvm::relay::ExpandDataflow<tvm::relay::MixedModeMutator::VisitExpr(tvm::RelayExpr const&)::{lambda(tvm::RelayExpr const&)#1}, tvm::relay::MixedModeMutator::VisitExpr(tvm::RelayExpr const&)::{lambda(tvm::RelayExpr const&)#2}>(tvm::RelayExpr, tvm::relay::MixedModeMutator::VisitExpr(tvm::RelayExpr const&)::{lambda(tvm::RelayExpr const&)#1}, tvm::relay::MixedModeMutator::VisitExpr(tvm::RelayExpr const&)::{lambda(tvm::RelayExpr const&)#2})::{lambda(tvm::RelayExpr const&)#1}) [clone .isra.0]
58: tvm::relay::MixedModeMutator::VisitLeaf(tvm::RelayExpr const&)
57: _ZN3tvm5relay16MixedModeMutator17DispatchVisitExprERKNS_9RelayExp
56: tvm::relay::ExprMutator::VisitExpr(tvm::RelayExpr const&)
55: _ZZN3tvm5relay11ExprFunctorIFNS_9RelayExprERKS2_EE10InitVTableEvENUlRKNS_7r
54: tvm::relay::ExprMutator::VisitExpr_(tvm::relay::LetNode const*)
53: tvm::relay::MixedModeMutator::VisitExpr(tvm::RelayExpr const&)
52: void tvm::relay::ExpandDataflow<tvm::relay::MixedModeMutator::VisitExpr(tvm::RelayExpr const&)::{lambda(tvm::RelayExpr const&)#1}, tvm::relay::MixedModeMutator::VisitExpr(tvm::RelayExpr const&)::{lambda(tvm::RelayExpr const&)#2}, tvm::relay::ExpandDataflow<tvm::relay::MixedModeMutator::VisitExpr(tvm::RelayExpr const&)::{lambda(tvm::RelayExpr const&)#1}, tvm::relay::MixedModeMutator::VisitExpr(tvm::RelayExpr const&)::{lambda(tvm::RelayExpr const&)#2}>(tvm::RelayExpr, tvm::relay::MixedModeMutator::VisitExpr(tvm::RelayExpr const&)::{lambda(tvm::RelayExpr const&)#1}, tvm::relay::MixedModeMutator::VisitExpr(tvm::RelayExpr const&)::{lambda(tvm::RelayExpr const&)#2})::{lambda(tvm::RelayExpr const&)#1}>(tvm::RelayExpr, tvm::relay::MixedModeMutator::VisitExpr(tvm::RelayExpr const&)::{lambda(tvm::RelayExpr const&)#1}, tvm::relay::MixedModeMutator::VisitExpr(tvm::RelayExpr const&)::{lambda(tvm::RelayExpr const&)#2}, tvm::relay::ExpandDataflow<tvm::relay::MixedModeMutator::VisitExpr(tvm::RelayExpr const&)::{lambda(tvm::RelayExpr const&)#1}, tvm::relay::MixedModeMutator::VisitExpr(tvm::RelayExpr const&)::{lambda(tvm::RelayExpr const&)#2}>(tvm::RelayExpr, tvm::relay::MixedModeMutator::VisitExpr(tvm::RelayExpr const&)::{lambda(tvm::RelayExpr const&)#1}, tvm::relay::MixedModeMutator::VisitExpr(tvm::RelayExpr const&)::{lambda(tvm::RelayExpr const&)#2})::{lambda(tvm::RelayExpr const&)#1}) [clone .isra.0]
51: tvm::relay::MixedModeMutator::VisitLeaf(tvm::RelayExpr const&)
50: _ZN3tvm5relay16MixedModeMutator17DispatchVisitExprERKNS_9RelayExp
49: tvm::relay::ExprMutator::VisitExpr(tvm::RelayExpr const&)
48: _ZZN3tvm5relay11ExprFunctorIFNS_9RelayExprERKS2_EE10InitVTableEvENUlRKNS_7r
47: tvm::relay::ExprMutator::VisitExpr_(tvm::relay::LetNode const*)
46: tvm::relay::MixedModeMutator::VisitExpr(tvm::RelayExpr const&)
45: void tvm::relay::ExpandDataflow<tvm::relay::MixedModeMutator::VisitExpr(tvm::RelayExpr const&)::{lambda(tvm::RelayExpr const&)#1}, tvm::relay::MixedModeMutator::VisitExpr(tvm::RelayExpr const&)::{lambda(tvm::RelayExpr const&)#2}, tvm::relay::ExpandDataflow<tvm::relay::MixedModeMutator::VisitExpr(tvm::RelayExpr const&)::{lambda(tvm::RelayExpr const&)#1}, tvm::relay::MixedModeMutator::VisitExpr(tvm::RelayExpr const&)::{lambda(tvm::RelayExpr const&)#2}>(tvm::RelayExpr, tvm::relay::MixedModeMutator::VisitExpr(tvm::RelayExpr const&)::{lambda(tvm::RelayExpr const&)#1}, tvm::relay::MixedModeMutator::VisitExpr(tvm::RelayExpr const&)::{lambda(tvm::RelayExpr const&)#2})::{lambda(tvm::RelayExpr const&)#1}>(tvm::RelayExpr, tvm::relay::MixedModeMutator::VisitExpr(tvm::RelayExpr const&)::{lambda(tvm::RelayExpr const&)#1}, tvm::relay::MixedModeMutator::VisitExpr(tvm::RelayExpr const&)::{lambda(tvm::RelayExpr const&)#2}, tvm::relay::ExpandDataflow<tvm::relay::MixedModeMutator::VisitExpr(tvm::RelayExpr const&)::{lambda(tvm::RelayExpr const&)#1}, tvm::relay::MixedModeMutator::VisitExpr(tvm::RelayExpr const&)::{lambda(tvm::RelayExpr const&)#2}>(tvm::RelayExpr, tvm::relay::MixedModeMutator::VisitExpr(tvm::RelayExpr const&)::{lambda(tvm::RelayExpr const&)#1}, tvm::relay::MixedModeMutator::VisitExpr(tvm::RelayExpr const&)::{lambda(tvm::RelayExpr const&)#2})::{lambda(tvm::RelayExpr const&)#1}) [clone .isra.0]
44: tvm::relay::MixedModeMutator::VisitLeaf(tvm::RelayExpr const&)
43: _ZN3tvm5relay16MixedModeMutator17DispatchVisitExprERKNS_9RelayExp
42: tvm::relay::ExprMutator::VisitExpr(tvm::RelayExpr const&)
41: _ZZN3tvm5relay11ExprFunctorIFNS_9RelayExprERKS2_EE10InitVTableEvENUlRKNS_7r
40: tvm::relay::ExprMutator::VisitExpr_(tvm::relay::LetNode const*)
39: tvm::relay::MixedModeMutator::VisitExpr(tvm::RelayExpr const&)
38: void tvm::relay::ExpandDataflow<tvm::relay::MixedModeMutator::VisitExpr(tvm::RelayExpr const&)::{lambda(tvm::RelayExpr const&)#1}, tvm::relay::MixedModeMutator::VisitExpr(tvm::RelayExpr const&)::{lambda(tvm::RelayExpr const&)#2}, tvm::relay::ExpandDataflow<tvm::relay::MixedModeMutator::VisitExpr(tvm::RelayExpr const&)::{lambda(tvm::RelayExpr const&)#1}, tvm::relay::MixedModeMutator::VisitExpr(tvm::RelayExpr const&)::{lambda(tvm::RelayExpr const&)#2}>(tvm::RelayExpr, tvm::relay::MixedModeMutator::VisitExpr(tvm::RelayExpr const&)::{lambda(tvm::RelayExpr const&)#1}, tvm::relay::MixedModeMutator::VisitExpr(tvm::RelayExpr const&)::{lambda(tvm::RelayExpr const&)#2})::{lambda(tvm::RelayExpr const&)#1}>(tvm::RelayExpr, tvm::relay::MixedModeMutator::VisitExpr(tvm::RelayExpr const&)::{lambda(tvm::RelayExpr const&)#1}, tvm::relay::MixedModeMutator::VisitExpr(tvm::RelayExpr const&)::{lambda(tvm::RelayExpr const&)#2}, tvm::relay::ExpandDataflow<tvm::relay::MixedModeMutator::VisitExpr(tvm::RelayExpr const&)::{lambda(tvm::RelayExpr const&)#1}, tvm::relay::MixedModeMutator::VisitExpr(tvm::RelayExpr const&)::{lambda(tvm::RelayExpr const&)#2}>(tvm::RelayExpr, tvm::relay::MixedModeMutator::VisitExpr(tvm::RelayExpr const&)::{lambda(tvm::RelayExpr const&)#1}, tvm::relay::MixedModeMutator::VisitExpr(tvm::RelayExpr const&)::{lambda(tvm::RelayExpr const&)#2})::{lambda(tvm::RelayExpr const&)#1}) [clone .isra.0]
37: tvm::relay::MixedModeMutator::VisitLeaf(tvm::RelayExpr const&)
36: _ZN3tvm5relay16MixedModeMutator17DispatchVisitExprERKNS_9RelayExp
35: tvm::relay::ExprMutator::VisitExpr(tvm::RelayExpr const&)
34: _ZZN3tvm5relay11ExprFunctorIFNS_9RelayExprERKS2_EE10InitVTableEvENUlRKNS_7r
33: tvm::relay::ExprMutator::VisitExpr_(tvm::relay::LetNode const*)
32: tvm::relay::MixedModeMutator::VisitExpr(tvm::RelayExpr const&)
31: void tvm::relay::ExpandDataflow<tvm::relay::MixedModeMutator::VisitExpr(tvm::RelayExpr const&)::{lambda(tvm::RelayExpr const&)#1}, tvm::relay::MixedModeMutator::VisitExpr(tvm::RelayExpr const&)::{lambda(tvm::RelayExpr const&)#2}, tvm::relay::ExpandDataflow<tvm::relay::MixedModeMutator::VisitExpr(tvm::RelayExpr const&)::{lambda(tvm::RelayExpr const&)#1}, tvm::relay::MixedModeMutator::VisitExpr(tvm::RelayExpr const&)::{lambda(tvm::RelayExpr const&)#2}>(tvm::RelayExpr, tvm::relay::MixedModeMutator::VisitExpr(tvm::RelayExpr const&)::{lambda(tvm::RelayExpr const&)#1}, tvm::relay::MixedModeMutator::VisitExpr(tvm::RelayExpr const&)::{lambda(tvm::RelayExpr const&)#2})::{lambda(tvm::RelayExpr const&)#1}>(tvm::RelayExpr, tvm::relay::MixedModeMutator::VisitExpr(tvm::RelayExpr const&)::{lambda(tvm::RelayExpr const&)#1}, tvm::relay::MixedModeMutator::VisitExpr(tvm::RelayExpr const&)::{lambda(tvm::RelayExpr const&)#2}, tvm::relay::ExpandDataflow<tvm::relay::MixedModeMutator::VisitExpr(tvm::RelayExpr const&)::{lambda(tvm::RelayExpr const&)#1}, tvm::relay::MixedModeMutator::VisitExpr(tvm::RelayExpr const&)::{lambda(tvm::RelayExpr const&)#2}>(tvm::RelayExpr, tvm::relay::MixedModeMutator::VisitExpr(tvm::RelayExpr const&)::{lambda(tvm::RelayExpr const&)#1}, tvm::relay::MixedModeMutator::VisitExpr(tvm::RelayExpr const&)::{lambda(tvm::RelayExpr const&)#2})::{lambda(tvm::RelayExpr const&)#1}) [clone .isra.0]
30: tvm::relay::MixedModeMutator::VisitLeaf(tvm::RelayExpr const&)
29: _ZN3tvm5relay16MixedModeMutator17DispatchVisitExprERKNS_9RelayExp
28: tvm::relay::ExprMutator::VisitExpr(tvm::RelayExpr const&)
27: _ZZN3tvm5relay11ExprFunctorIFNS_9RelayExprERKS2_EE10InitVTableEvENUlRKNS_7r
26: tvm::relay::ExprMutator::VisitExpr_(tvm::relay::LetNode const*)
25: tvm::relay::MixedModeMutator::VisitExpr(tvm::RelayExpr const&)
24: void tvm::relay::ExpandDataflow<tvm::relay::MixedModeMutator::VisitExpr(tvm::RelayExpr const&)::{lambda(tvm::RelayExpr const&)#1}, tvm::relay::MixedModeMutator::VisitExpr(tvm::RelayExpr const&)::{lambda(tvm::RelayExpr const&)#2}, tvm::relay::ExpandDataflow<tvm::relay::MixedModeMutator::VisitExpr(tvm::RelayExpr const&)::{lambda(tvm::RelayExpr const&)#1}, tvm::relay::MixedModeMutator::VisitExpr(tvm::RelayExpr const&)::{lambda(tvm::RelayExpr const&)#2}>(tvm::RelayExpr, tvm::relay::MixedModeMutator::VisitExpr(tvm::RelayExpr const&)::{lambda(tvm::RelayExpr const&)#1}, tvm::relay::MixedModeMutator::VisitExpr(tvm::RelayExpr const&)::{lambda(tvm::RelayExpr const&)#2})::{lambda(tvm::RelayExpr const&)#1}>(tvm::RelayExpr, tvm::relay::MixedModeMutator::VisitExpr(tvm::RelayExpr const&)::{lambda(tvm::RelayExpr const&)#1}, tvm::relay::MixedModeMutator::VisitExpr(tvm::RelayExpr const&)::{lambda(tvm::RelayExpr const&)#2}, tvm::relay::ExpandDataflow<tvm::relay::MixedModeMutator::VisitExpr(tvm::RelayExpr const&)::{lambda(tvm::RelayExpr const&)#1}, tvm::relay::MixedModeMutator::VisitExpr(tvm::RelayExpr const&)::{lambda(tvm::RelayExpr const&)#2}>(tvm::RelayExpr, tvm::relay::MixedModeMutator::VisitExpr(tvm::RelayExpr const&)::{lambda(tvm::RelayExpr const&)#1}, tvm::relay::MixedModeMutator::VisitExpr(tvm::RelayExpr const&)::{lambda(tvm::RelayExpr const&)#2})::{lambda(tvm::RelayExpr const&)#1}) [clone .isra.0]
23: tvm::relay::MixedModeMutator::VisitLeaf(tvm::RelayExpr const&)
22: _ZN3tvm5relay16MixedModeMutator17DispatchVisitExprERKNS_9RelayExp
21: tvm::relay::ExprMutator::VisitExpr(tvm::RelayExpr const&)
20: _ZZN3tvm5relay11ExprFunctorIFNS_9RelayExprERKS2_EE10InitVTableEvENUlRKNS_7r
19: tvm::relay::ExprMutator::VisitExpr_(tvm::relay::LetNode const*)
18: tvm::relay::MixedModeMutator::VisitExpr(tvm::RelayExpr const&)
17: void tvm::relay::ExpandDataflow<tvm::relay::MixedModeMutator::VisitExpr(tvm::RelayExpr const&)::{lambda(tvm::RelayExpr const&)#1}, tvm::relay::MixedModeMutator::VisitExpr(tvm::RelayExpr const&)::{lambda(tvm::RelayExpr const&)#2}, tvm::relay::ExpandDataflow<tvm::relay::MixedModeMutator::VisitExpr(tvm::RelayExpr const&)::{lambda(tvm::RelayExpr const&)#1}, tvm::relay::MixedModeMutator::VisitExpr(tvm::RelayExpr const&)::{lambda(tvm::RelayExpr const&)#2}>(tvm::RelayExpr, tvm::relay::MixedModeMutator::VisitExpr(tvm::RelayExpr const&)::{lambda(tvm::RelayExpr const&)#1}, tvm::relay::MixedModeMutator::VisitExpr(tvm::RelayExpr const&)::{lambda(tvm::RelayExpr const&)#2})::{lambda(tvm::RelayExpr const&)#1}>(tvm::RelayExpr, tvm::relay::MixedModeMutator::VisitExpr(tvm::RelayExpr const&)::{lambda(tvm::RelayExpr const&)#1}, tvm::relay::MixedModeMutator::VisitExpr(tvm::RelayExpr const&)::{lambda(tvm::RelayExpr const&)#2}, tvm::relay::ExpandDataflow<tvm::relay::MixedModeMutator::VisitExpr(tvm::RelayExpr const&)::{lambda(tvm::RelayExpr const&)#1}, tvm::relay::MixedModeMutator::VisitExpr(tvm::RelayExpr const&)::{lambda(tvm::RelayExpr const&)#2}>(tvm::RelayExpr, tvm::relay::MixedModeMutator::VisitExpr(tvm::RelayExpr const&)::{lambda(tvm::RelayExpr const&)#1}, tvm::relay::MixedModeMutator::VisitExpr(tvm::RelayExpr const&)::{lambda(tvm::RelayExpr const&)#2})::{lambda(tvm::RelayExpr const&)#1}) [clone .isra.0]
16: tvm::relay::MixedModeMutator::VisitLeaf(tvm::RelayExpr const&)
15: _ZN3tvm5relay16MixedModeMutator17DispatchVisitExprERKNS_9RelayExp
14: tvm::relay::ExprMutator::VisitExpr(tvm::RelayExpr const&)
13: _ZZN3tvm5relay11ExprFunctorIFNS_9RelayExprERKS2_EE10InitVTableEvENUlRKNS_7r
12: tvm::relay::ExprMutator::VisitExpr_(tvm::relay::LetNode const*)
11: tvm::relay::MixedModeMutator::VisitExpr(tvm::RelayExpr const&)
10: void tvm::relay::ExpandDataflow<tvm::relay::MixedModeMutator::VisitExpr(tvm::RelayExpr const&)::{lambda(tvm::RelayExpr const&)#1}, tvm::relay::MixedModeMutator::VisitExpr(tvm::RelayExpr const&)::{lambda(tvm::RelayExpr const&)#2}, tvm::relay::ExpandDataflow<tvm::relay::MixedModeMutator::VisitExpr(tvm::RelayExpr const&)::{lambda(tvm::RelayExpr const&)#1}, tvm::relay::MixedModeMutator::VisitExpr(tvm::RelayExpr const&)::{lambda(tvm::RelayExpr const&)#2}>(tvm::RelayExpr, tvm::relay::MixedModeMutator::VisitExpr(tvm::RelayExpr const&)::{lambda(tvm::RelayExpr const&)#1}, tvm::relay::MixedModeMutator::VisitExpr(tvm::RelayExpr const&)::{lambda(tvm::RelayExpr const&)#2})::{lambda(tvm::RelayExpr const&)#1}>(tvm::RelayExpr, tvm::relay::MixedModeMutator::VisitExpr(tvm::RelayExpr const&)::{lambda(tvm::RelayExpr const&)#1}, tvm::relay::MixedModeMutator::VisitExpr(tvm::RelayExpr const&)::{lambda(tvm::RelayExpr const&)#2}, tvm::relay::ExpandDataflow<tvm::relay::MixedModeMutator::VisitExpr(tvm::RelayExpr const&)::{lambda(tvm::RelayExpr const&)#1}, tvm::relay::MixedModeMutator::VisitExpr(tvm::RelayExpr const&)::{lambda(tvm::RelayExpr const&)#2}>(tvm::RelayExpr, tvm::relay::MixedModeMutator::VisitExpr(tvm::RelayExpr const&)::{lambda(tvm::RelayExpr const&)#1}, tvm::relay::MixedModeMutator::VisitExpr(tvm::RelayExpr const&)::{lambda(tvm::RelayExpr const&)#2})::{lambda(tvm::RelayExpr const&)#1}) [clone .isra.0]
9: tvm::relay::MixedModeMutator::VisitLeaf(tvm::RelayExpr const&)
8: _ZN3tvm5relay16MixedModeMutator17DispatchVisitExprERKNS_9RelayExp
7: tvm::relay::ExprMutator::VisitExpr(tvm::RelayExpr const&)
6: _ZZN3tvm5relay11ExprFunctorIFNS_9RelayExprERKS2_EE10InitVTableEvENUlRKNS_7r
5: tvm::relay::MixedModeMutator::VisitExpr_(tvm::relay::CallNode const*)
4: tvm::relay::partitioning::Partitioner::Rewrite_(tvm::relay::CallNode const*, tvm::RelayExpr const&)
3: tvm::relay::partitioning::Partitioner::CreateFunction(tvm::relay::AnnotatedRegion, tvm::relay::CallNode const*)
2: tvm::relay::partitioning::Partitioner::CreateRegionCall(tvm::relay::AnnotatedRegion, tvm::runtime::Array<tvm::RelayExpr, void> const&, tvm::relay::CallNode const*)
1: tvm::IRModuleNode::Add(tvm::GlobalVar const&, tvm::BaseFunc const&, bool)
0: tvm::runtime::PackedFuncObj::Extractor<tvm::runtime::PackedFuncSubObj<tvm::runtime::TypedPackedFunc<void (tvm::IRModule const&, tvm::BaseFunc const&)>::AssignTypedLambda<tvm::relay::{lambda(tvm::IRModule const&, tvm::BaseFunc const&)#3}>(tvm::relay::{lambda(tvm::IRModule const&, tvm::BaseFunc const&)#3}, std::__cxx11::basic_string<char, std::char_traits<char>, std::allocator<char> >)::{lambda(tvm::runtime::TVMArgs const&, tvm::runtime::TVMRetValue*)#1}> >::Call(tvm::runtime::PackedFuncObj const*, tvm::runtime::TVMArgs, tvm::runtime::TVMRetValue*)
File "/media/pc/data/lxw/ai/tvm/src/relay/ir/function.cc", line 156
InternalError: Check failed: fv.size() == 0 (1 vs. 0) : Function:
fn (%test_1_i0: Tensor[(1, 3, 224, 224), float32], %test_1_i1: Tensor[(32, 3, 3, 3), float32], %test_1_i2: Tensor[(32, 1, 1), float32], global_symbol="test_main_1", Primitive=1, Compiler="test", Inline=1) -> Tensor[(1, 32, 112, 112), float32] {
free_var %test_0_i0: fn (Tensor[(1, 3, 224, 224), float32], Tensor[(32, 3, 3, 3), float32], Tensor[(32, 1, 1), float32]) -> Tensor[(1, 32, 112, 112), float32];
%test_0_i0(%test_1_i0, %test_1_i1, %test_1_i2) /* ty=Tensor[(1, 32, 112, 112), float32] */
}
contains free variables: [Var(test_0_i0, ty=I.FuncType([], [I.TensorType([1, 3, 224, 224], "float32"), I.TensorType([32, 3, 3, 3], "float32"), I.TensorType([32, 1, 1], "float32")], I.TensorType([1, 32, 112, 112], "float32")))]
print(calibrate_mod_t)
def @main(%data: Tensor[(1, 3, 224, 224), float32] /* ty=Tensor[(1, 3, 224, 224), float32] span=aten::_convolution_0.data:0:0 */) -> Tensor[(1, 32, 112, 112), float32] {
let %x_417: float32 /* ty=float32 */ = 0.0340509f /* ty=float32 */;
let %x_418: float32 /* ty=float32 */ = -127f /* ty=float32 */;
let %x_419: float32 /* ty=float32 */ = 127f /* ty=float32 */;
%0 = relay.op.annotation.simulated_quantize(%data, %x_417, %x_418, %x_419, kind=1) /* ty=Tensor[(1, 3, 224, 224), float32] */;
let %x_420: Tensor[(1, 3, 224, 224), float32] /* ty=Tensor[(1, 3, 224, 224), float32] */ = annotation.compiler_end(%0, compiler="default") /* ty=Tensor[(1, 3, 224, 224), float32] */;
let %x_421: Tensor[(32, 3, 3, 3), float32] /* ty=Tensor[(32, 3, 3, 3), float32] */ = meta[relay.Constant][0] /* ty=Tensor[(32, 3, 3, 3), float32] */;
let %x_422: float32 /* ty=float32 */ = 0.00283333f /* ty=float32 */;
let %x_423: float32 /* ty=float32 */ = -127f /* ty=float32 */;
let %x_424: float32 /* ty=float32 */ = 127f /* ty=float32 */;
%1 = relay.op.annotation.simulated_quantize(%x_421, %x_422, %x_423, %x_424, kind=2) /* ty=Tensor[(32, 3, 3, 3), float32] */;
let %x_425: Tensor[(32, 3, 3, 3), float32] /* ty=Tensor[(32, 3, 3, 3), float32] */ = annotation.compiler_end(%1, compiler="default") /* ty=Tensor[(32, 3, 3, 3), float32] */;
let %x_426: Tensor[(32, 1, 1), float32] /* ty=Tensor[(32, 1, 1), float32] */ = meta[relay.Constant][1] /* ty=Tensor[(32, 1, 1), float32] */;
let %x_439: fn (Tensor[(1, 3, 224, 224), float32], Tensor[(32, 3, 3, 3), float32], Tensor[(32, 1, 1), float32]) -> Tensor[(1, 32, 112, 112), float32] /* ty=fn (Tensor[(1, 3, 224, 224), float32], Tensor[(32, 3, 3, 3), float32], Tensor[(32, 1, 1), float32]) -> Tensor[(1, 32, 112, 112), float32] */ = fn (%FunctionVar_1_0: Tensor[(1, 3, 224, 224), float32] /* ty=Tensor[(1, 3, 224, 224), float32] */, %FunctionVar_1_1: Tensor[(32, 3, 3, 3), float32] /* ty=Tensor[(32, 3, 3, 3), float32] */, %FunctionVar_1_2: Tensor[(32, 1, 1), float32] /* ty=Tensor[(32, 1, 1), float32] */, PartitionedFromPattern="nn.conv2d_relay.op.annotation.simulated_quantize_add_clip_relay.op.annotation.simulated_quantize_annotation.cast_hint_", Composite="ccompiler.conv_add_activate") -> Tensor[(1, 32, 112, 112), float32] {
%2 = nn.conv2d(%FunctionVar_1_0, %FunctionVar_1_1, strides=[2, 2], padding=[1, 1, 1, 1], channels=32, kernel_size=[3, 3]) /* ty=Tensor[(1, 32, 112, 112), float32] */;
let %x_427: Tensor[(1, 32, 112, 112), float32] /* ty=Tensor[(1, 32, 112, 112), float32] */ = annotation.compiler_end(%2, compiler="default") /* ty=Tensor[(1, 32, 112, 112), float32] */;
let %x_428: float32 /* ty=float32 */ = 0.00756057f /* ty=float32 */;
let %x_429: float32 /* ty=float32 */ = -127f /* ty=float32 */;
let %x_430: float32 /* ty=float32 */ = 127f /* ty=float32 */;
%3 = relay.op.annotation.simulated_quantize(%FunctionVar_1_2, %x_428, %x_429, %x_430, kind=2) /* ty=Tensor[(32, 1, 1), float32] */;
let %x_431: Tensor[(32, 1, 1), float32] /* ty=Tensor[(32, 1, 1), float32] */ = annotation.compiler_end(%3, compiler="default") /* ty=Tensor[(32, 1, 1), float32] */;
%4 = add(%x_427, %x_431) /* ty=Tensor[(1, 32, 112, 112), float32] */;
let %x_432: Tensor[(1, 32, 112, 112), float32] /* ty=Tensor[(1, 32, 112, 112), float32] */ = annotation.compiler_end(%4, compiler="default") /* ty=Tensor[(1, 32, 112, 112), float32] */;
%5 = clip(%x_432, a_min=0f, a_max=6f) /* ty=Tensor[(1, 32, 112, 112), float32] */;
let %x_433: Tensor[(1, 32, 112, 112), float32] /* ty=Tensor[(1, 32, 112, 112), float32] */ = annotation.compiler_end(%5, compiler="default") /* ty=Tensor[(1, 32, 112, 112), float32] */;
let %x_434: float32 /* ty=float32 */ = 0.0150701f /* ty=float32 */;
let %x_435: float32 /* ty=float32 */ = -127f /* ty=float32 */;
let %x_436: float32 /* ty=float32 */ = 127f /* ty=float32 */;
%6 = relay.op.annotation.simulated_quantize(%x_433, %x_434, %x_435, %x_436, kind=1) /* ty=Tensor[(1, 32, 112, 112), float32] */;
let %x_437: Tensor[(1, 32, 112, 112), float32] /* ty=Tensor[(1, 32, 112, 112), float32] */ = annotation.compiler_end(%6, compiler="default") /* ty=Tensor[(1, 32, 112, 112), float32] */;
%7 = annotation.cast_hint(%x_437, dtype="int8") /* ty=Tensor[(1, 32, 112, 112), float32] */;
let %x_438: Tensor[(1, 32, 112, 112), float32] /* ty=Tensor[(1, 32, 112, 112), float32] */ = annotation.compiler_end(%7, compiler="default") /* ty=Tensor[(1, 32, 112, 112), float32] */;
%x_438
} /* ty=fn (Tensor[(1, 3, 224, 224), float32], Tensor[(32, 3, 3, 3), float32], Tensor[(32, 1, 1), float32]) -> Tensor[(1, 32, 112, 112), float32] */;
%8 = annotation.compiler_begin(%x_420, compiler="test") /* ty=Tensor[(1, 3, 224, 224), float32] */;
%9 = annotation.compiler_begin(%x_425, compiler="test") /* ty=Tensor[(32, 3, 3, 3), float32] */;
%10 = annotation.compiler_begin(%x_426, compiler="test") /* ty=Tensor[(32, 1, 1), float32] */;
%11 = annotation.compiler_begin(%x_439, compiler="test") /* ty=fn (Tensor[(1, 3, 224, 224), float32], Tensor[(32, 3, 3, 3), float32], Tensor[(32, 1, 1), float32]) -> Tensor[(1, 32, 112, 112), float32] */;
%12 = %11(%8, %9, %10) /* ty=Tensor[(1, 32, 112, 112), float32] */;
let %x_440: Tensor[(1, 32, 112, 112), float32] /* ty=Tensor[(1, 32, 112, 112), float32] */ = annotation.compiler_end(%12, compiler="test") /* ty=Tensor[(1, 32, 112, 112), float32] */;
%13 = annotation.stop_fusion(%x_440) /* ty=Tensor[(1, 32, 112, 112), float32] */;
let %x_441: Tensor[(1, 32, 112, 112), float32] /* ty=Tensor[(1, 32, 112, 112), float32] */ = annotation.compiler_end(%13, compiler="default") /* ty=Tensor[(1, 32, 112, 112), float32] */;
let %x_442: Tensor[(32, 1, 3, 3), float32] /* ty=Tensor[(32, 1, 3, 3), float32] */ = meta[relay.Constant][2] /* ty=Tensor[(32, 1, 3, 3), float32] */;
let %x_443: float32 /* ty=float32 */ = 0.117756f /* ty=float32 */;
let %x_444: float32 /* ty=float32 */ = -127f /* ty=float32 */;
let %x_445: float32 /* ty=float32 */ = 127f /* ty=float32 */;
%14 = relay.op.annotation.simulated_quantize(%x_442, %x_443, %x_444, %x_445, kind=2) /* ty=Tensor[(32, 1, 3, 3), float32] */;
let %x_446: Tensor[(32, 1, 3, 3), float32] /* ty=Tensor[(32, 1, 3, 3), float32] */ = annotation.compiler_end(%14, compiler="default") /* ty=Tensor[(32, 1, 3, 3), float32] */;
let %x_447: Tensor[(32, 1, 1), float32] /* ty=Tensor[(32, 1, 1), float32] */ = meta[relay.Constant][3] /* ty=Tensor[(32, 1, 1), float32] */;
let %x_460: fn (Tensor[(1, 32, 112, 112), float32], Tensor[(32, 1, 3, 3), float32], Tensor[(32, 1, 1), float32]) -> Tensor[(1, 32, 112, 112), float32] /* ty=fn (Tensor[(1, 32, 112, 112), float32], Tensor[(32, 1, 3, 3), float32], Tensor[(32, 1, 1), float32]) -> Tensor[(1, 32, 112, 112), float32] */ = fn (%FunctionVar_0_0: Tensor[(1, 32, 112, 112), float32] /* ty=Tensor[(1, 32, 112, 112), float32] */, %FunctionVar_0_1: Tensor[(32, 1, 3, 3), float32] /* ty=Tensor[(32, 1, 3, 3), float32] */, %FunctionVar_0_2: Tensor[(32, 1, 1), float32] /* ty=Tensor[(32, 1, 1), float32] */, PartitionedFromPattern="nn.conv2d_relay.op.annotation.simulated_quantize_add_clip_relay.op.annotation.simulated_quantize_annotation.cast_hint_", Composite="ccompiler.conv_add_activate") -> Tensor[(1, 32, 112, 112), float32] {
%15 = nn.conv2d(%FunctionVar_0_0, %FunctionVar_0_1, padding=[1, 1, 1, 1], groups=32, channels=32, kernel_size=[3, 3]) /* ty=Tensor[(1, 32, 112, 112), float32] */;
let %x_448: Tensor[(1, 32, 112, 112), float32] /* ty=Tensor[(1, 32, 112, 112), float32] */ = annotation.compiler_end(%15, compiler="default") /* ty=Tensor[(1, 32, 112, 112), float32] */;
let %x_449: float32 /* ty=float32 */ = 0.0291522f /* ty=float32 */;
let %x_450: float32 /* ty=float32 */ = -127f /* ty=float32 */;
let %x_451: float32 /* ty=float32 */ = 127f /* ty=float32 */;
%16 = relay.op.annotation.simulated_quantize(%FunctionVar_0_2, %x_449, %x_450, %x_451, kind=2) /* ty=Tensor[(32, 1, 1), float32] */;
let %x_452: Tensor[(32, 1, 1), float32] /* ty=Tensor[(32, 1, 1), float32] */ = annotation.compiler_end(%16, compiler="default") /* ty=Tensor[(32, 1, 1), float32] */;
%17 = add(%x_448, %x_452) /* ty=Tensor[(1, 32, 112, 112), float32] */;
let %x_453: Tensor[(1, 32, 112, 112), float32] /* ty=Tensor[(1, 32, 112, 112), float32] */ = annotation.compiler_end(%17, compiler="default") /* ty=Tensor[(1, 32, 112, 112), float32] */;
%18 = clip(%x_453, a_min=0f, a_max=6f) /* ty=Tensor[(1, 32, 112, 112), float32] */;
let %x_454: Tensor[(1, 32, 112, 112), float32] /* ty=Tensor[(1, 32, 112, 112), float32] */ = annotation.compiler_end(%18, compiler="default") /* ty=Tensor[(1, 32, 112, 112), float32] */;
let %x_455: float32 /* ty=float32 */ = 0.0460431f /* ty=float32 */;
let %x_456: float32 /* ty=float32 */ = -127f /* ty=float32 */;
let %x_457: float32 /* ty=float32 */ = 127f /* ty=float32 */;
%19 = relay.op.annotation.simulated_quantize(%x_454, %x_455, %x_456, %x_457, kind=1) /* ty=Tensor[(1, 32, 112, 112), float32] */;
let %x_458: Tensor[(1, 32, 112, 112), float32] /* ty=Tensor[(1, 32, 112, 112), float32] */ = annotation.compiler_end(%19, compiler="default") /* ty=Tensor[(1, 32, 112, 112), float32] */;
%20 = annotation.cast_hint(%x_458, dtype="int8") /* ty=Tensor[(1, 32, 112, 112), float32] */;
let %x_459: Tensor[(1, 32, 112, 112), float32] /* ty=Tensor[(1, 32, 112, 112), float32] */ = annotation.compiler_end(%20, compiler="default") /* ty=Tensor[(1, 32, 112, 112), float32] */;
%x_459
} /* ty=fn (Tensor[(1, 32, 112, 112), float32], Tensor[(32, 1, 3, 3), float32], Tensor[(32, 1, 1), float32]) -> Tensor[(1, 32, 112, 112), float32] */;
%21 = annotation.compiler_begin(%x_441, compiler="test") /* ty=Tensor[(1, 32, 112, 112), float32] */;
%22 = annotation.compiler_begin(%x_446, compiler="test") /* ty=Tensor[(32, 1, 3, 3), float32] */;
%23 = annotation.compiler_begin(%x_447, compiler="test") /* ty=Tensor[(32, 1, 1), float32] */;
%24 = annotation.compiler_begin(%x_460, compiler="test") /* ty=fn (Tensor[(1, 32, 112, 112), float32], Tensor[(32, 1, 3, 3), float32], Tensor[(32, 1, 1), float32]) -> Tensor[(1, 32, 112, 112), float32] */;
%25 = %24(%21, %22, %23) /* ty=Tensor[(1, 32, 112, 112), float32] */;
let %x_461: Tensor[(1, 32, 112, 112), float32] /* ty=Tensor[(1, 32, 112, 112), float32] */ = annotation.compiler_end(%25, compiler="test") /* ty=Tensor[(1, 32, 112, 112), float32] */;
%26 = annotation.stop_fusion(%x_461) /* ty=Tensor[(1, 32, 112, 112), float32] */;
let %x_462: Tensor[(1, 32, 112, 112), float32] /* ty=Tensor[(1, 32, 112, 112), float32] */ = annotation.compiler_end(%26, compiler="default") /* ty=Tensor[(1, 32, 112, 112), float32] */;
%x_462
}
node.op
func_id = 0
call = calibrate_transform.nodes[func_id]
new_fn = call.op
new_args = new_fn.params
call = run_opt_pass(call, relay.transform.DefuseOps())
print(call)
new_fn = tvm.IRModule.from_expr(call)["main"]
call = Call(new_fn.body, new_fn.params, attrs=new_fn.attrs, type_args=new_fn.type_params, span=new_fn.span)
# call = run_opt_pass(call, relay.transform.DefuseOps())
# new_args = [relay.var(f"x_{func_id:03}_{ind:03d}") for ind, arg in enumerate(new_fn.params)]
# _bind_dict = dict(zip(new_fn.params, new_args))
# new_fn = relay.bind(new_fn, _bind_dict)
print(call)
g_var_names = []
for arg in calibrate_mod_t.get_global_vars():
if arg.name_hint == "main":
continue
new_fn = calibrate_mod_t[arg]
# 过滤不需要的函数
name = new_fn.attrs["PartitionedFromPattern"].replace("relay.op.annotation.simulated_quantize_", "").replace("annotation.cast_hint_", "")
if "squeeze" in name:
continue
g_var_names.append(arg.name_hint)
g_var_names.sort()
float_mod = tvm.IRModule()
for func_name in g_var_names:
new_fn = partition_transform.bind_dict[func_name]
float_mod[func_name] = new_fn
q_mod = tvm.IRModule()
for func_name in g_var_names:
new_fn = calibrate_mod_t[func_name]
new_fn = DefuseMutator(float_mod).visit(new_fn)
q_mod[func_name] = new_fn
# partial_quant_outputs = []
# for func_name in tqdm(g_var_names):
# new_fn = calibrate_mod_t[func_name]
# new_fn = DefuseMutator(float_mod).visit(new_fn)
# run_mod = tvm.IRModule.from_expr(new_fn)
# # partial_quant_output = run_llvm_graph(
# # run_mod,
# # origin_params,
# # {"data": np.random.normal(size=input_shape)}
# # )
# # partial_quant_outputs.append(partial_quant_output)
# break
float_mod_t = tvm.IRModule()
main_args = mod["main"].params
main_fns = []
for k, func_name in enumerate(g_var_names):
new_fn = float_mod[func_name]
new_args = [relay.var(f"x_{k:03}_{ind:03d}") for ind, arg in enumerate(new_fn.params)]
_bind_dict = dict(zip(new_fn.params, new_args))
new_fn = relay.bind(new_fn, _bind_dict)
float_mod_t[func_name] = new_fn
call = Call(new_fn, main_args, attrs=new_fn.attrs, type_args=new_fn.type_params, span=new_fn.span)
main_fns.append(call)
new_fn = run_opt_pass(relay.Tuple(main_fns), relay.transform.DefuseOps())
float_mod_t = tvm.IRModule.from_expr(new_fn)
float_outputs = run_llvm_graph(
float_mod_t,
origin_params,
{"data": np.random.normal(size=input_shape)}
)
partial_quant_outputs = run_llvm_graph(
float_mod_t,
origin_params,
{"data": np.random.normal(size=input_shape)}
)
partial_quant_outputs = []
for func_name in tqdm(g_var_names):
new_fn = calibrate_mod_t[func_name]
new_params = transform.bind_params[func_name]
_bind_dict = dict(zip(new_fn.params, new_params))
new_fn = relay.bind(new_fn, _bind_dict)
new_fn = DefuseTransform(partition_mod_t).visit(new_fn)
new_fn = run_opt_pass(new_fn, relay.transform.DefuseOps())
print(f"部分量化 {func_name}:\n{new_fn}")
run_mod = tvm.IRModule.from_expr(new_fn)
# 部分量化模型输出
partial_quant_output = run_llvm_graph(
run_mod,
origin_params,
{"data": np.random.normal(size=input_shape)}
)
partial_quant_outputs.append(partial_quant_output[0])
# if func_name == "f_0002":
# break
partial_quant_outputs[0].shape