ane struct

ane/lib/ane.py

@@ -20,6 +20,54 @@ libane.ANE_TensorData.restype = POINTER(c_uint16)
 libane.ANE_Run.argtypes = [c_void_p]*3
 libane.ANE_Run.restype = c_int
 
+ANE_Struct = [
+# section @ 0x2C len 0xF4
+
+# section @ 0x128 len 0x3C (conv)
+  ("u16", 0x128, "InputWidth"),
+  ("u16", 0x12A, "InputHeight"),
+  ("u16", 0x12C, "InputDepth"),
+
+  ("u32", 0x130, "InputOutputType"),   # (OutputType * 0x10) | InputType
+
+  ("u32", 0x134, "InputChannels"),
+  ("u32", 0x138, "OutputChannels"),
+
+  ("u16", 0x13C, "OutputWidth"),
+  ("u16", 0x13E, "OutputHeight"),
+  ("u16", 0x140, "OutputDepth"),
+
+  ("u16", 0x144, "KernelSize"),        # 0xa000 | (KernelHeight * 0x20) | KernelWidth
+  ("u16", 0x146, "Padding"),           # 0x5000 | (PadTop * 0x40) | (PadLeft * 2)
+
+  ("u16", 0x14C, "BatchSize"),
+
+# section @ 0x16C len 0x6C (input)
+  # reloc 0x16c-0x174 = image
+  ("u32", 0x178, "InputRowStride"),
+  ("u32", 0x17C, "InputPlaneStride"),
+  ("u32", 0x180, "InputDepthStride"),
+  ("u32", 0x184, "InputBatchStride"),
+
+  ("u8",  0x1A7, "InputInterleave"),
+
+# section @ 0x1E0 len 0x44
+
+# section @ 0x22c len 0xC (scaling)
+  ("u16", 0x230, "BiasScalar"),
+  ("u16", 0x232, "ScaleScalar"),
+
+# section @ 0x240 len 0x10
+
+# section @ 0x258 len 0x18
+  ("u32", 0x260, "OutputRowStride"),
+  ("u32", 0x264, "OutputPlaneStride"),
+  ("u32", 0x268, "OutputDepthStride"),
+  ("u32", 0x26C, "OutputBatchStride"),
+
+  ("u8",  0x273, "OutputInterleave")]
+
+
 class ANETensor:
   def __init__(self, *shape):
     self.shape = shape
@@ -63,11 +111,12 @@ if __name__ == "__main__":
   toutd = tout.data()
 
   tind[0:4] = [-1,1,-2,2]
+  print("** before **")
   print(tind)
   print(toutd)
 
-  comp = ane.compile(open("../2_compile/model.hwx", "rb").read())
+  comp = ane.compile(open("../ops/relu.hwx", "rb").read())
   ret = ane.run(comp, tin, tout)
-
+  print("** after **")
   print(tind)
   print(toutd)

tinygrad/ops_ane.py

@@ -1,4 +1,4 @@
-from .tensor import Tensor, Function, register
+from .tensor import Device, Function, register
 from functools import lru_cache
 import struct
 
@@ -33,4 +33,4 @@ class ReLU(Function):
     ret = ctx.ane.tensor(input.shape)
     ctx.ane.run(compile_relu(ctx.ane, input.sz), input, ret)
     return ret
-register('relu', ReLU, device=Tensor.ANE)
+register('relu', ReLU, device=Device.ANE)