From ee06d981afa0675c8637ab3edf60b1250b09c27c Mon Sep 17 00:00:00 2001
From: Yifan Zhao <yifanz16@illinois.edu>
Date: Tue, 2 Feb 2021 01:06:30 -0600
Subject: [PATCH] Supports selecting hpvm target name in frontend
---
.../projects/torch2hpvm/torch2hpvm/codegen_hpvm.py | 11 ++++++++++-
hpvm/projects/torch2hpvm/torch2hpvm/compile.py | 12 +++++++++---
.../torch2hpvm/torch2hpvm/template_hpvm.cpp.in | 2 +-
hpvm/test/dnn_benchmarks/pytorch/test_frontend.py | 14 +++++++++-----
4 files changed, 29 insertions(+), 10 deletions(-)
diff --git a/hpvm/projects/torch2hpvm/torch2hpvm/codegen_hpvm.py b/hpvm/projects/torch2hpvm/torch2hpvm/codegen_hpvm.py
index 439c5af5d7..cdba5f327f 100644
--- a/hpvm/projects/torch2hpvm/torch2hpvm/codegen_hpvm.py
+++ b/hpvm/projects/torch2hpvm/torch2hpvm/codegen_hpvm.py
@@ -52,7 +52,9 @@ class CodeGen:
for weight in weights:
name = cls.make_c_identifier(weight.name)
file_path = f"{weight.new_name}_path.bin"
- ret.append({"name": name, "shape": weight.output_shape, "filename": file_path})
+ ret.append(
+ {"name": name, "shape": weight.output_shape, "filename": file_path}
+ )
return ret
@staticmethod
@@ -67,6 +69,12 @@ class HpvmCodeGen(CodeGen):
# Variable indicator is always int for hpvm gen
variables: Dict[DFGNode, Tuple[int, bool]]
+ def __init__(self, dfg: DFG, prefix: PathLike, input_size: int, target: str):
+ super().__init__(dfg, prefix, input_size)
+ if target not in ("tensor", "cudnn"):
+ raise ValueError(f"Unsupported target {target}")
+ self.target = target
+
def _emit_hpvm_node_edges(self, input_vars: List[DFGNode]) -> List[dict]:
ret = []
it = 0
@@ -135,5 +143,6 @@ class HpvmCodeGen(CodeGen):
root_output_idx=output_var_idx,
weights=weights,
prefix=self.prefix,
+ target=self.target,
)
)
diff --git a/hpvm/projects/torch2hpvm/torch2hpvm/compile.py b/hpvm/projects/torch2hpvm/torch2hpvm/compile.py
index a94c16e6d4..cc2a670dad 100644
--- a/hpvm/projects/torch2hpvm/torch2hpvm/compile.py
+++ b/hpvm/projects/torch2hpvm/torch2hpvm/compile.py
@@ -41,7 +41,7 @@ class ModelExporter:
tune_dataset: DatasetTy,
test_dataset: DatasetTy,
output_dir: PathLike,
- hpvmc: bool = True,
+ target: str = "hpvm_tensor",
opset: Optional[int] = None,
):
from onnxsim import simplify
@@ -62,8 +62,14 @@ class ModelExporter:
self.weight_dir = self.output_dir / self.weight_dir_name
self.weight_dir.mkdir(exist_ok=True)
- flavor = HpvmCodeGen if hpvmc else TensorCodeGen
- self.codegen = flavor(self.dfg, self.weight_dir, self.dataset_size)
+ if target == "hpvm_tensor":
+ self.codegen = HpvmCodeGen(self.dfg, self.weight_dir, self.dataset_size, "tensor")
+ elif target == "hpvm_cudnn":
+ self.codegen = HpvmCodeGen(self.dfg, self.weight_dir, self.dataset_size, "cudnn")
+ elif target == "tensor":
+ self.codegen = TensorCodeGen(self.dfg, self.weight_dir, self.dataset_size)
+ else:
+ raise ValueError(f"Target {target} not recognized")
def export_source_code(self, output: PathLike, batch_size: Optional[int] = None):
self.codegen.compile(output, batch_size)
diff --git a/hpvm/projects/torch2hpvm/torch2hpvm/template_hpvm.cpp.in b/hpvm/projects/torch2hpvm/torch2hpvm/template_hpvm.cpp.in
index d7d62e4216..d7fd6c8884 100644
--- a/hpvm/projects/torch2hpvm/torch2hpvm/template_hpvm.cpp.in
+++ b/hpvm/projects/torch2hpvm/torch2hpvm/template_hpvm.cpp.in
@@ -8,7 +8,7 @@ void var_{{node.idx}}_node(
{%- for n in range(node.input_size) -%}
void *t{{n}}, size_t bytes_t{{n}}{{", " if not loop.last}}
{%- endfor %}) {
- __hpvm__hint(hpvm::CUDNN_TARGET);
+ __hpvm__hint(hpvm::{{target.upper()}}_TARGET);
__hpvm__attributes({{node.input_size}}, {% for n in range(node.input_size) -%}
t{{n}}{{", " if not loop.last}}
{%- endfor %}, 0);
diff --git a/hpvm/test/dnn_benchmarks/pytorch/test_frontend.py b/hpvm/test/dnn_benchmarks/pytorch/test_frontend.py
index 96de3db2ed..7395136eb5 100644
--- a/hpvm/test/dnn_benchmarks/pytorch/test_frontend.py
+++ b/hpvm/test/dnn_benchmarks/pytorch/test_frontend.py
@@ -29,24 +29,28 @@ for model_cls, nch, img_size, batch_size, pathname in benchmarks:
print(f"Generating {pathname} to {codegen_dir}")
if codegen_dir.exists():
shutil.rmtree(codegen_dir)
- prefix = self_folder / "../model_params" / pathname
+
+ params = self_folder / "../model_params" / pathname
dataset_shape = 5000, nch, img_size, img_size
bin_tuneset = BinDataset(
- prefix / "tune_input.bin", prefix / "tune_labels.bin", dataset_shape
+ params / "tune_input.bin", params / "tune_labels.bin", dataset_shape
)
bin_testset = BinDataset(
- prefix / "test_input.bin", prefix / "test_labels.bin", dataset_shape
+ params / "test_input.bin", params / "test_labels.bin", dataset_shape
)
model: Module = model_cls()
checkpoint = self_folder / "../model_params" / f"{pathname}.pth.tar"
model.load_state_dict(torch.load(checkpoint.as_posix()))
- exporter = ModelExporter(model, bin_tuneset, bin_testset, codegen_dir, True)
+
+ exporter = ModelExporter(model, bin_tuneset, bin_testset, codegen_dir)
exporter.export_all(batch_size=batch_size)
+
+ conf_file = self_folder / "../hpvm-c/benchmarks" / pathname / "data/tuner_confs.txt"
build_dir = codegen_dir / "build"
target_binary = build_dir / pathname
run([
"approxhpvm.py", str(codegen_dir / ModelExporter.source_file_name), str(target_binary),
"-d", str(build_dir),
- "-t", "cudnn"
+ "-t", "tensor", "--conf-file", str(conf_file)
], check=True)
run([str(target_binary), "test"], check=True)
--
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