commit e8a5a78dd16ec979c7a7bb1f5bd87e9b2909c32d
author Louis Verhaard <louis.verhaard@arm.com> Mon Nov 02 18:04:27 2020 +0100
committer Louis Verhaard <louis.verhaard@arm.com> Fri Nov 13 14:10:33 2020 +0100
tree 0829808f5ce047b12e1813ca382ac73c3300da91
parent dda21afda93f3732491efdcf89af2b14396c683f

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>

ethosu/vela/weight_compressor.py

diff --git a/ethosu/vela/weight_compressor.py b/ethosu/vela/weight_compressor.py
index b0187b6..c07229f 100644
--- a/ethosu/vela/weight_compressor.py
+++ b/ethosu/vela/weight_compressor.py
@@ -20,6 +20,7 @@
 
 import numpy as np
 
+from .api import NpuBlockTraversal
 from .architecture_features import Accelerator
 from .architecture_features import ArchitectureFeatures
 from .data_type import DataType
@@ -53,7 +54,7 @@
     ifm_bitdepth: int,
     ofm_block_depth: int,
     is_depthwise: bool,
-    is_partkernel: bool,
+    block_traversal: NpuBlockTraversal,
 ):
     """
     Public facing API to use the ethosu weight encoding.
@@ -64,7 +65,7 @@
     :param ifm_bitdepth: the bitdepth of input feature map
     :param ofm_block_depth: the depth of blocks for ethosu processing
     :param is_depthwise: a boolean indicating these weights are used for a depthwise traversal
-    :param is_partkernel: a boolean indicating these weights are traversed on sub-kernal basis
+    :param block_traversal: indicates how these weights are traversed on sub-kernal basis
     :return: a bytearray of compressed weights
     """
 
@@ -75,13 +76,15 @@
     assert isinstance(ifm_bitdepth, int)
     assert isinstance(ofm_block_depth, int)
     assert isinstance(is_depthwise, bool)
-    assert isinstance(is_partkernel, bool)
+    assert isinstance(block_traversal, NpuBlockTraversal)
 
     # Checks for weight layout
     assert len(weights_volume.shape) == 4, "weights ndarray should have a shape of 4"
 
     # It cannot be both partkernel and depthwise
-    assert not (is_depthwise and is_partkernel), "encode_weights :: partkernel and depthwise are mutually exclusive"
+    assert not (
+        is_depthwise and block_traversal == NpuBlockTraversal.PART_KERNEL_FIRST
+    ), "encode_weights :: partkernel and depthwise are mutually exclusive"
 
     # Check valid values for dilation
     assert dilation_xy[0] in (1, 2), "encode_weights :: dilation x should be 1 or 2 not {}".format(dilation_xy[0])
@@ -95,7 +98,7 @@
         brick_weights=weights_volume,
         ofm_block_depth=ofm_block_depth,
         is_depthwise=is_depthwise,
-        is_partkernel=is_partkernel,
+        is_partkernel=block_traversal == NpuBlockTraversal.PART_KERNEL_FIRST,
         ifm_bitdepth=ifm_bitdepth,
         dilation=dilation_xy,
     )
@@ -335,7 +338,10 @@
             tens.block_traversal = TensorBlockTraversal.DepthFirst
 
     is_depthwise = tens.block_traversal == TensorBlockTraversal.DepthWise
-    is_partkernel = tens.block_traversal == TensorBlockTraversal.PartKernelFirst
+    if tens.block_traversal == TensorBlockTraversal.PartKernelFirst:
+        block_traversal = NpuBlockTraversal.PART_KERNEL_FIRST
+    else:
+        block_traversal = NpuBlockTraversal.DEPTH_FIRST
 
     if tens.consumer_list[0].type == Op.Conv2DBackpropInputSwitchedBias:
         # Transpose Convoluion, reverse weights in H and W axes
@@ -370,7 +376,7 @@
                     ifm_bitdepth=ifm_bitdepth,
                     ofm_block_depth=block_depth,
                     is_depthwise=is_depthwise,
-                    is_partkernel=is_partkernel,
+                    block_traversal=block_traversal,
                 )
             encoded_stream.extend(encoded_substream)
             substream_offsets.append(len(encoded_stream))