MLBEDSW-839: Code generation using external API
Added external API to generate register command streams.
Existing code generation has been refactored to make
use of this API.
Change-Id: Ibb4c2b167809869f16470b14da24f08a65c82b7b
Signed-off-by: Louis Verhaard <louis.verhaard@arm.com>
diff --git a/ethosu/vela/operation.py b/ethosu/vela/operation.py
index 1ba2a38..9f7d544 100644
--- a/ethosu/vela/operation.py
+++ b/ethosu/vela/operation.py
@@ -15,8 +15,10 @@
# limitations under the License.
# Description:
# Internal representation of a Neural Network Operation.
+import copy
from collections import namedtuple
from enum import Enum
+from typing import Optional
from .numeric_util import full_shape
@@ -35,24 +37,30 @@
class Kernel:
- def __init__(self, w, h, sx=1, sy=1, dx=1, dy=1):
- assert sx > 0 and sy > 0
- assert dx > 0 and dy > 0
+ """
+ Kernel information for NPU operations
+ """
+
+ def __init__(self, w: int, h: int, stride_x: int = 1, stride_y: int = 1, dilation_x: int = 1, dilation_y: int = 1):
+ assert stride_x > 0 and stride_y > 0
+ assert dilation_x > 0 and dilation_y > 0
self.width = w
self.height = h
- self.stride = PointXY(sx, sy)
- self.dilation = PointXY(dx, dy)
- self.upscale = 1
+ self.stride = PointXY(stride_x, stride_y)
+ self.dilation = PointXY(dilation_x, dilation_y)
- def elements_wh(self):
+ def elements_wh(self) -> int:
return self.width * self.height
- def area_width(self):
+ def area_width(self) -> int:
return (self.width - 1) * self.dilation.x + 1
- def area_height(self):
+ def area_height(self) -> int:
return (self.height - 1) * self.dilation.y + 1
+ def __str__(self):
+ return f"w={self.width}, h={self.height}, stride={tuple(self.stride)}, dilation={tuple(self.dilation)}"
+
# Classifies operators of type Custom
class CustomType(Enum):
@@ -109,6 +117,7 @@
Call = OperatorInfo()
Cast = OperatorInfo()
Ceil = OperatorInfo()
+ Clip = OperatorInfo() # NPU specific fused activation function for clipping between activation.min/max
Concat = OperatorInfo(indices=CONCAT_INDICES)
ConcatEmbeddings = OperatorInfo()
ConcatSliceWrite = OperatorInfo(indices=IFM_INDICES)
@@ -282,7 +291,7 @@
return self.info.block_type == NpuBlockType.ElementWise and not self.info.is_unary
def is_relu_op(self):
- return self in (Op.Relu, Op.Relu6, Op.ReluN1To1)
+ return self in (Op.Relu, Op.Relu6, Op.ReluN1To1, Op.Clip)
def is_activation_op(self):
return self.is_relu_op() or self in (Op.Tanh, Op.Sigmoid, Op.Softmax, Op.LUT)
@@ -310,6 +319,42 @@
return self.value.id < other.value.id
+class ActivationFunction:
+ """Fused activation function"""
+
+ def __init__(self, op_type: Op):
+ self.op_type = op_type # The activation operation to be performed
+ # min/max are optional; if present they are non-quantized values
+ self.min: Optional[float] = None
+ self.max: Optional[float] = None
+ # Table lookup index, only applicable for Op.LUT activation, 0-7
+ self.lut_index: int = 0
+
+ def clone(self):
+ res = copy.copy(self)
+ return res
+
+
+def create_activation_function(op_type: Op) -> ActivationFunction:
+ """Creates activation function with min/max depending on op_type"""
+ act = ActivationFunction(op_type)
+ if op_type == Op.Relu:
+ act.min = 0.0
+ elif op_type == Op.Relu6:
+ act.min = 0.0
+ act.max = 6.0
+ elif op_type == Op.ReluN1To1:
+ act.min = -1.0
+ act.max = 1.0
+ elif op_type == Op.Tanh:
+ act.min = -1.0
+ act.max = 1.0
+ elif op_type == Op.Sigmoid:
+ act.min = 0.0
+ act.max = 1.0
+ return act
+
+
def create_avgpool_nop(name):
op = Operation(Op.AvgPool, name)
op.attrs["padding"] = b"VALID"
@@ -358,7 +403,7 @@
"_kernel",
)
- def __init__(self, op_type, name):
+ def __init__(self, op_type: Op, name: str):
self.type = op_type
self.name = name
self.attrs = {}
@@ -367,7 +412,7 @@
self.flops = 0
self.run_on_npu = True
# Fused activation function. If not none: operator code.
- self.activation = None
+ self.activation: Optional[ActivationFunction] = None
# Fused memory function, if not None: operator code
self.memory_function = None
# If not none: contains QuantizationParameters to be used as output quantization
@@ -386,7 +431,7 @@
res.outputs = list(self.outputs)
res.flops = self.flops
res.run_on_npu = self.run_on_npu
- res.activation = self.activation
+ res.activation = None if self.activation is None else self.activation.clone()
res.memory_function = self.memory_function
res.forced_output_quantization = self.forced_output_quantization
res.scheduled_pass = self.scheduled_pass
@@ -405,6 +450,8 @@
weight_shape = full_shape(4, weights.shape, 1)
h = weight_shape[-4]
w = weight_shape[-3]
+ elif self.type.npu_block_type in (NpuBlockType.Pooling, NpuBlockType.ReduceSum) and "ksize" in self.attrs:
+ h, w = self.attrs["ksize"][1:3]
else:
h = self.attrs.get("filter_height", 1)
w = self.attrs.get("filter_width", 1)
@@ -597,7 +644,7 @@
return input_tens, outputs, axis, offset_start, offset_end
def set_activation_lut(self, lut_tensor):
- self.activation = Op.LUT
+ self.activation = ActivationFunction(Op.LUT)
self.activation_lut = lut_tensor
self.add_input_tensor(lut_tensor)