From 50cd9c21d75f74a1d92a235a0c071b2c8f8eb83a Mon Sep 17 00:00:00 2001
From: Yifan Zhao <yifanz16@illinois.edu>
Date: Tue, 12 Jan 2021 18:18:35 -0600
Subject: [PATCH] Made graph_ir node arguments explicit
---
.../onnx_frontend/frontend/graph_builder.py | 6 +-
.../onnx_frontend/frontend/graph_ir.py | 55 +++++++++++--------
2 files changed, 34 insertions(+), 27 deletions(-)
diff --git a/hpvm/projects/onnx_frontend/frontend/graph_builder.py b/hpvm/projects/onnx_frontend/frontend/graph_builder.py
index e8bf86a84a..6452508e4a 100644
--- a/hpvm/projects/onnx_frontend/frontend/graph_builder.py
+++ b/hpvm/projects/onnx_frontend/frontend/graph_builder.py
@@ -175,14 +175,14 @@ class DFG(object):
assert isinstance(weight_tensor, g.WeightTensor)
if len(weight_tensor.shape) != 4:
return None # Only supports 2D conv
- conv_node = g.Conv2DNode(node.name, attrs)
+ conv_node = g.Conv2DNode(node.name, **attrs)
if len(predec) == 2:
return conv_node
# Split into conv followed by an addition
bias_node = g.BiasAddNode(f"Bias_{node.name.split('_')[-1]}")
return MarkedSubGraph.idiomatic_1to2(conv_node, bias_node, predec)
elif node.op_type in ("MatMul", "Gemm"):
- mul_node = g.MatMulNode(node.name, attrs)
+ mul_node = g.MatMulNode(node.name, **attrs)
if node.op_type == "Gemm":
mul_node.gemm_transpose(node, predec)
if len(predec) == 2:
@@ -203,7 +203,7 @@ class DFG(object):
"Flatten": g.FlattenNode,
}
if node.op_type in one_to_one_nodes:
- return one_to_one_nodes[node.op_type](node.name, attrs)
+ return one_to_one_nodes[node.op_type](node.name, **attrs)
return None
diff --git a/hpvm/projects/onnx_frontend/frontend/graph_ir.py b/hpvm/projects/onnx_frontend/frontend/graph_ir.py
index f567ddafed..4e313bd537 100644
--- a/hpvm/projects/onnx_frontend/frontend/graph_ir.py
+++ b/hpvm/projects/onnx_frontend/frontend/graph_ir.py
@@ -15,8 +15,8 @@ class DFGNode(abc.ABC):
op_type = ""
- def __init__(self, name: str, attrs: dict = {}):
- self.name, self.attrs = name, attrs
+ def __init__(self, name: str, **kwargs):
+ self.name = name
def codegen(self) -> Tuple[str, list]:
return "", []
@@ -37,7 +37,7 @@ class TensorNode(DFGNode, abc.ABC):
def __init__(self, proto: onnx.TensorProto, new_name: str):
if not proto.name.strip():
raise ValueError("Tensor's name is required.")
- super().__init__(proto.name, {})
+ super().__init__(proto.name)
self.new_name = new_name
def __str__(self):
@@ -102,25 +102,28 @@ class WeightTensor(TensorNode):
class Conv2DNode(DFGNode):
op_type = "Conv2D"
- def __init__(self, name: str, attrs: dict):
- super().__init__(name, attrs)
- padding = self.attrs["pads"]
- assert len(padding) == 4, "2D convolution must have 4 padding values"
- if any(p != padding[0] for p in padding[1:]):
+ def __init__(self, name: str, pads, strides, dilations, group: int, kernel_shape):
+ super().__init__(name)
+ assert len(pads) == 4, "2D convolution must have 4 padding values"
+ if any(p != pads[0] for p in pads[1:]):
raise ValueError("Convolution with different padding is unsupported")
- self.padding = padding[0]
- self.strides = self.attrs["strides"]
+ if dilations != [1, 1]:
+ raise ValueError("Dilation > 1 is unsupported")
+ if group != 1:
+ raise ValueError("Group > 1 is unsupported")
+ self.pads = pads[0]
+ self.strides = strides
def codegen(self):
return (
"tensorConvolution",
- [self.padding, self.padding, self.strides[0], self.strides[1]],
+ [self.pads, self.pads, self.strides[0], self.strides[1]],
)
def hpvm_codegen(self):
return (
"__visc__tensor_convolution",
- [self.padding, self.padding, self.strides[0], self.strides[1]],
+ [self.pads, self.pads, self.strides[0], self.strides[1]],
)
@@ -129,20 +132,24 @@ class _Pool2DNode(DFGNode, abc.ABC):
pool_type = "0"
- def __init__(self, name: str, attrs: dict):
- super().__init__(name, attrs)
- self.strides = self.attrs["strides"]
- self.pool_size = self.attrs["kernel_shape"]
- self.padding = 0
+ def __init__(self, name: str, strides, kernel_shape, pads, ceil_mode: int):
+ super().__init__(name)
+ self.strides = strides
+ self.kernel_shape = kernel_shape
+ pt, pb, pl, pr = pads
+ if pt != pb or pl != pr:
+ raise ValueError("Unequal padding on either side of same axis is unsupported")
+ self.pads = pt, pl
+ if ceil_mode != 0:
+ raise ValueError("Only ceil_mode == 0 is supported")
def codegen(self):
return (
"tensorPooling",
[
self.pool_type,
- *self.pool_size,
- self.padding,
- self.padding,
+ *self.kernel_shape,
+ *self.pads,
*self.strides,
],
)
@@ -150,7 +157,7 @@ class _Pool2DNode(DFGNode, abc.ABC):
def hpvm_codegen(self):
return (
"__visc__tensor_pool_max",
- [*self.pool_size, self.padding, self.padding, *self.strides],
+ [*self.kernel_shape, *self.pads, *self.strides],
)
@@ -249,9 +256,9 @@ class TanhNode(DFGNode):
class BatchNormalizationNode(DFGNode):
op_type = "BN"
- def __init__(self, name: str, attrs: dict):
- super().__init__(name, attrs)
- self.epsilon = self.attrs["epsilon"]
+ def __init__(self, name: str, epsilon: float, axis: int):
+ super().__init__(name)
+ self.epsilon = epsilon
def codegen(self):
return "tensorBatchNorm", [self.epsilon]
--
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