commit fd8b500085d1ac1cca54a71631d21713a3c21f09
author Rickard Bolin <rickard.bolin@arm.com> Mon May 16 09:11:06 2022 +0000
committer Rickard Bolin <rickard.bolin@arm.com> Mon May 16 15:20:20 2022 +0000
tree 4a8d1c7809dc1eb748f0f0b9ba2736e5d7bb5e69
parent 6f4cb0362a2f00b3045565de2c27f72997b2998b

MLBEDSW-6263: Use separate tensors for double buffering

Uses separate tensors for the individual weight buffers
in case of weight double buffering.

Each weight buffer tensor gets its own individual live range.

This patch is a clone of a previously reverted patch, but with some
additional bug fixes applied.

Signed-off-by: Rickard Bolin <rickard.bolin@arm.com>
Change-Id: I868c70d15821eb9f1399186f2da6e7345f6ee343

ethosu/vela/weight_compressor.py

diff --git a/ethosu/vela/weight_compressor.py b/ethosu/vela/weight_compressor.py
index 86b424a..78c4351 100644
--- a/ethosu/vela/weight_compressor.py
+++ b/ethosu/vela/weight_compressor.py
@@ -68,12 +68,19 @@
     def __init__(self, name):
         Tensor.__init__(self, None, None, name + "_npu_encoded_weights")
         self.buffer = []
-        self.max_range_bytes = 0
+        self.double_buffer_sizes = [0, 0]  # Required sizes if double buffering is used
         self.encoded_ranges = OrderedDict()
         self.hw_traversal = NpuBlockTraversal.DEPTH_FIRST
         self.dtype = DataType.uint8
         self.scale_compression_config = None
 
+    def max_range_bytes(self):
+        return max(self.double_buffer_sizes)
+
+    def double_buffer_size(self):
+        """Return total required size for double buffering"""
+        return sum(self.double_buffer_sizes)
+
 
 class CompressedWeightCache:
     """Global tensor weight compression cache"""
@@ -357,7 +364,7 @@
             weights = np.flip(weights, axis=(0, 1))
 
     encoded_stream = bytearray()
-    max_single_buffer_len = 0
+    double_buffer_sizes = [0, 0]
     is_depthwise = npu_block_type == NpuBlockType.ConvolutionDepthWise
 
     # Bias & scale
@@ -435,11 +442,11 @@
                 npu_tensor.encoded_ranges[key] = weight_range
 
         # Remember maximum encoded length for DoubleBuffering
-        max_single_buffer_len = max(max_single_buffer_len, len(encoded_stream) - buffer_start_offset)
+        double_buffer_sizes[idx % 2] = max(double_buffer_sizes[idx % 2], len(encoded_stream) - buffer_start_offset)
 
     # Attach buffer to tensor
     npu_tensor.buffer = encoded_stream
-    npu_tensor.max_range_bytes = max_single_buffer_len
+    npu_tensor.double_buffer_sizes = double_buffer_sizes
     npu_tensor.set_all_shapes([1, 1, 1, len(encoded_stream)])
     npu_tensor.format = TensorFormat.WeightsCompressed