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

1 files changed, 15 insertions, 23 deletions

diff --git a/ethosu/vela/high_level_command_to_npu_op.py b/ethosu/vela/high_level_command_to_npu_op.py
index 3a78d6fb..e6bfc1c4 100644
--- a/ethosu/vela/high_level_command_to_npu_op.py
+++ b/ethosu/vela/high_level_command_to_npu_op.py
@@ -68,7 +68,6 @@ from .tensor import MemType
 from .tensor import Tensor
 from .tensor import TensorFormat
 from .tensor import TensorPurpose
-from .tensor import TensorSubPurpose
 from .weight_compressor import NpuWeightTensor
 from .weight_compressor import WeightKey
 
@@ -202,9 +201,15 @@ def get_mem_limits_for_regions(arch: ArchitectureFeatures) -> Dict[int, int]:
     return mem_limits
 
 
-def get_double_buffer_offset(arch: ArchitectureFeatures, range_index: int, core: int) -> int:
-    """Returns 0 if the first half of a double buffer should be used, 1 if the second half should be used"""
-    return ((range_index - core) // arch.ncores) % 2
+def get_upscale(op: Operation) -> NpuResamplingMode:
+    upscale = NpuResamplingMode.NONE
+    if op.type == Op.ResizeBilinear:
+        # perform nearest neighbor upscale
+        upscale = NpuResamplingMode.NEAREST
+    elif op.type == Op.Conv2DBackpropInputSwitchedBias:
+        # perform insert zero upscale
+        upscale = NpuResamplingMode.TRANSPOSE
+    return upscale
 
 
 def get_ifm_depth(npu_block_type: NpuBlockType, ifm_box: Box, ofm_box: Box) -> int:
@@ -314,20 +319,13 @@ def create_weights(
         key = WeightKey(core, weight_box.start_coord[-1])
         if key in w_tensor_src.encoded_ranges:
             weight_range = w_tensor_src.encoded_ranges[key]
-            if weight_tensor.sub_purpose == TensorSubPurpose.DoubleBuffer:
-                assert weight_tensor != w_tensor_src
-                # Double buffered inside weight_tensor
+            if weight_tensor == w_tensor_src:
+                # Straight from source tensor
+                address = weight_tensor.address + weight_range.offset
+            else:
+                # Weight buffered tensor
                 address = weight_tensor.address + core_offset
-                address += get_double_buffer_offset(arch, weight_range.index, core) * w_tensor_src.max_range_bytes
                 core_offset += round_up(weight_range.total_bytes, 16)
-            else:
-                if weight_tensor == w_tensor_src:
-                    # Straight from source tensor
-                    address = weight_tensor.address + weight_range.offset
-                else:
-                    # Single buffered inside weight tensor
-                    address = weight_tensor.address + core_offset
-                    core_offset += round_up(weight_range.total_bytes, 16)
 
             # Location of weights in tensor
             addr_range = NpuAddressRange(
@@ -526,13 +524,7 @@ def create_dma_op(cmd: DMA, arch: ArchitectureFeatures) -> NpuDmaOperation:
                 if core == 0:
                     weight_range = cmd.in_tensor.encoded_ranges[key]
                     src_addr = cmd.in_tensor.address + weight_range.offset
-
-                    if cmd.out_tensor.sub_purpose == TensorSubPurpose.DoubleBuffer:
-                        dest_addr = cmd.out_tensor.address + cmd.in_tensor.max_range_bytes * (
-                            get_double_buffer_offset(arch, weight_range.index, core)
-                        )
-                    else:
-                        dest_addr = cmd.out_tensor.address
+                    dest_addr = cmd.out_tensor.address
     else:
         start_coord = cmd.box.start_coord
         src_addr = cmd.in_tensor.address_for_coordinate(start_coord)