def set_op_node_name(self, node: pm.Node, operand: OperandTemplate):
if operand.node_name is not None and operand.node_name != node.name:
raise RuntimeError(
f"Name already set to different value for operand:\n"
f"Previous name: {operand.node_name}\n"
f"New name: {node.name}")
operand.set_node_name(node.name)
def set_dim_values(self, node: pm.Node, operand: OperandTemplate):
if not operand.is_instantiated():
if len(operand.permutation) == len(node.shape):
perm_map = {
s: operand.permutation[s]
for s in range(len(node.shape))
}
else:
perm_map = {s: s for s in range(len(node.shape))}
for j, s_ in enumerate(node.shape):
key = operand.shape_list[j]
s = node.shape[perm_map[j]]
operand.update_shape_symbols(key, s)
if key not in self.required_params:
self.add_required_param(key, s)
elif key in self.required_params:
if not self.required_params[key].is_set():
self.set_required_param(key, s)
elif self.required_params[key].value != s:
raise RuntimeError(
f"Inconsistent dimension sizes for operation {self.op_name}{self.instance_id}\n"
f"Key: {key}\n"
f"Size: {self.required_params[key].value}\n"
f"Node shape: {node.shape}\n"
f"Node name: {operand.name}\n"
f"Shape list: {operand.shape_list}")
if len(operand.shape_list) != len(
list(operand.shape_symbols.keys())):
raise RuntimeError(
f"All shape values were not set for node {node.name}, operand {operand.name}:\n"
f"Node shape: {node.shape}\n"
f"Operand shape variables: {operand.shape_list}")
def global_avg_pool(hag: ArchitectureNode):
#
data = OperandTemplate("data",
OP_DTYPES, ["N", "C", "IH", "IW"],
dtype=OP_DTYPES[2])
#
out = OperandTemplate("out",
OP_DTYPES, ["N", "C", "OH", "OW"],
dtype=OP_DTYPES[2])
# # TODO: Add option to create operand
with Codelet("global_avg_pool", [data], [out], hag) as cdlt:
cdlt.configure("start", "SIMD")
cdlt.configure("start", "IMM", immediate_value=0)
with Loop(0, "N") as n:
with Loop(0, "C") as c:
with Loop(0, "IH") as iy:
with Loop(0, "IW") as ix:
with Loop(0, "OH") as oy:
with Loop(0, "OW") as ox:
cdlt.transfer(data[n, c, iy + oy, ix + ox],
["DRAM", "VMEM1"])
cdlt.compute("MAX", [data, data], [out],
target="SIMD")
cdlt.transfer(out[n, c, oy, ox],
["VMEM1", "DRAM"])
return cdlt
def maxpool2d(hag: ArchitectureNode):
#
data = OperandTemplate("data",
OP_DTYPES, ["N", "C", "IH", "IW"],
dtype=OP_DTYPES[2])
#
out = OperandTemplate("out",
OP_DTYPES, ["N", "C", "OH", "OW"],
dtype=OP_DTYPES[2])
# # TODO: Add option to create operand
with Codelet("max_pool", [data], [out], hag) as cdlt:
cdlt.configure("start", "SIMD")
cdlt.configure("start", "IMM", immediate_value=0)
with Loop(0, "N") as n:
with Loop(0, "C") as c:
with Loop(0, "KH") as kh:
with Loop(0, "KW") as kw:
with Loop(0, "OH") as y:
with Loop(0, "OW") as x:
cdlt.transfer(
data[n, c, y * "sy" + kh, x * "sx" + kw],
["DRAM", "VMEM1"])
cdlt.compute("MAX", [data, data], [out],
target="SIMD")
cdlt.transfer(out[n, c, y, x],
["VMEM1", "DRAM"])
return cdlt
def conv2d_bias(hag: ArchitectureNode):
# TODO: Need to figure out how to change the memory layout
data = OperandTemplate("data",
OP_DTYPES, ["N", "IC", "IH", "IW"],
dtype=OP_DTYPES[0])
weight = OperandTemplate("weight",
OP_DTYPES, ["OC", "IC", "KH", "KW"],
dtype=OP_DTYPES[0])
bias = OperandTemplate("bias", OP_DTYPES, ["OC"], dtype=OP_DTYPES[2])
out = OperandTemplate("out",
OP_DTYPES, ["N", "OC", "OH", "OW"],
dtype=OP_DTYPES[2])
required_params = {}
with Codelet("conv_bias", [data, weight, bias], [out],
hag,
required_params=required_params) as cdlt:
cdlt.configure("start", "systolic_array")
cdlt.configure("start", "WBUF")
cdlt.configure("start", "BBUF")
cdlt.configure("start", "IBUF")
cdlt.configure("start", "OBUF")
with Loop(0, "OC") as oc:
with Loop(0, "N") as n:
with Loop(0, "IC") as ic:
with Loop(0, "KH") as kh:
with Loop(0, "KW") as kw:
with Loop(0, "OH") as y:
with Loop(0, "OW") as x:
cdlt.transfer(weight[oc, ic, kh, kw],
["DRAM", "WBUF"])
cdlt.transfer(bias[oc], ["DRAM", "BBUF"])
cdlt.transfer(
data[n, ic, y * "stride" + kh,
x * "stride" + kw],
["DRAM", "IBUF"])
cdlt.transfer(out[n, oc, y, x],
["DRAM", "OBUF"])
cdlt.compute("MVMUL", [data, weight, bias],
[out],
target="pe_array")
# cdlt.compute("MVMUL", [data[n, ic, y*"stride" + kh, x*"stride" + kw], weight[oc, ic, kh, kw], bias[oc]], [out[n, oc, y, x]], target="pe_array")
cdlt.transfer(out[n, oc, y, x],
["OBUF", "DRAM"])
# TODO: Add store off chip
cdlt.configure("end", "WBUF")
cdlt.configure("end", "BBUF")
cdlt.configure("end", "IBUF")
cdlt.configure("end", "OBUF")
cdlt.configure("end", "systolic_array")
return cdlt
def relu(hag: ArchitectureNode):
op1 = OperandTemplate("op1",
OP_DTYPES, ["N", "C", "H", "W"],
dtype=OP_DTYPES[2])
out = OperandTemplate("out",
OP_DTYPES, ["N", "C", "H", "W"],
dtype=OP_DTYPES[2])
with Codelet("relu", [op1], [out], hag) as cdlt:
cdlt.configure("start", "SIMD")
# cdlt.configure("start", "VMEM")
with Loop(0, "N") as n:
with Loop(0, "C") as c:
with Loop(0, "H") as h:
with Loop(0, "W") as w:
cdlt.transfer(op1[n, c, h, w], ["DRAM", "VMEM1"])
cdlt.compute("RELU", [op1], [out], target="SIMD")
cdlt.transfer(out[n, c, h, w], ["VMEM1", "DRAM"])
return cdlt
def maxpool2d_nchw(hag: ArchitectureNode):
# loop_order = ["n", "oc", "kh", "kh", "ow", "oh"]
#
data = OperandTemplate("data", OP_DTYPES, ["N", "C", "IH", "IW"])
#
out = OperandTemplate("out", OP_DTYPES, ["N", "C", "OH", "OW"])
# # TODO: Add option to create operand
# # 1. set IMM to negative infinity
with Codelet("max_pool", [data], [out], hag) as cdlt:
cdlt.configure("start", "SIMD")
cdlt.configure("start", "VMEM")
cdlt.configure("start", "IMM") # 1.
with Loop(0, "N") as n:
with Loop(0, "C") as c:
with Loop(0, "OH") as y:
with Loop(0, "OW") as x:
cdlt.transfer(data[n, c, y * "stride", x * "stride"],
["DRAM", "VMEM"])
cdlt.compute("MAX", [data, "IMM"], [out],
target="SIMD")
cdlt.transfer(out[n, c, y, x], ["SIMD", "VMEM"])
with Loop(0, "N") as n:
with Loop(0, "C") as c:
with Loop(0, "KH") as kh:
with Loop(0, "KW") as kw:
with Loop(0, "OH") as y:
with Loop(0, "OW") as x:
cdlt.transfer(
data[n, c, y * "stride" + kh,
x * "stride" + kw], ["DRAM", "VMEM"])
cdlt.transfer(out[n, c, y, x], ["VMEM"])
cdlt.compute("MAX", [data], [out[n]],
target="SIMD")
cdlt.transfer(out[n, c, y, x],
["pe_array", "OBUF", "DRAM"])
# TODO: Add store off chip
cdlt.configure("end", "WBUF")
cdlt.configure("end", "IBUF")
cdlt.configure("end", "OBUF")
def elem_add(hag: ArchitectureNode):
op1 = OperandTemplate("op1",
OP_DTYPES, ["N", "C", "H", "W"],
dtype=OP_DTYPES[2])
op2 = OperandTemplate("op2",
OP_DTYPES, ["N", "C", "H", "W"],
dtype=OP_DTYPES[2])
out = OperandTemplate("add_out",
OP_DTYPES, ["N", "C", "H", "W"],
dtype=OP_DTYPES[2])
with Codelet("elem_add", [op1, op2], [out], hag) as cdlt:
cdlt.configure("start", "SIMD")
with Loop(0, "N") as n:
with Loop(0, "C") as c:
with Loop(0, "H") as h:
with Loop(0, "W") as w:
cdlt.transfer(op1[n, c, h, w], ["DRAM", "VMEM1"])
cdlt.transfer(op2[n, c, h, w], ["DRAM", "VMEM2"])
cdlt.compute("ADD", [op1, op2], [out], target="SIMD")
cdlt.transfer(out[n, c, h, w], ["VMEM1", "DRAM"])
return cdlt
def gemm(hag: ArchitectureNode):
data = OperandTemplate("data", OP_DTYPES, ["M", "N"], dtype=OP_DTYPES[0])
weight = OperandTemplate("weight",
OP_DTYPES, ["N", "P"],
dtype=OP_DTYPES[0])
bias = OperandTemplate("bias", OP_DTYPES, ["P"], dtype=OP_DTYPES[2])
out = OperandTemplate("out", OP_DTYPES, ["M", "P"], dtype=OP_DTYPES[2])
required_params = {}
with Codelet("gemm", [data, weight, bias], [out],
hag,
required_params=required_params) as cdlt:
cdlt.configure("start", "systolic_array")
cdlt.configure("start", "WBUF")
cdlt.configure("start", "IBUF")
cdlt.configure("start", "BBUF")
cdlt.configure("start", "OBUF")
with Loop(0, "P") as p:
with Loop(0, "N") as n:
with Loop(0, "M") as m:
cdlt.transfer(data[m, n], ["DRAM", "IBUF"])
cdlt.transfer(weight[n, p], ["DRAM", "WBUF"])
cdlt.transfer(bias[p], ["DRAM", "BBUF"])
cdlt.transfer(out[m, p], ["DRAM", "OBUF"])
cdlt.compute("MVMUL", [data, weight, bias], [out],
target="pe_array")
cdlt.transfer(out[m, p], ["OBUF", "DRAM"])
# TODO: Add store off chip
cdlt.configure("end", "WBUF")
cdlt.configure("end", "IBUF")
cdlt.configure("end", "OBUF")
cdlt.configure("end", "BBUF")
cdlt.configure("end", "systolic_array")
return cdlt
