diff options
Diffstat (limited to 'ethosu/vela/high_level_command_to_npu_op.py')
| -rw-r--r-- | ethosu/vela/high_level_command_to_npu_op.py | 49 |
1 files changed, 22 insertions, 27 deletions
diff --git a/ethosu/vela/high_level_command_to_npu_op.py b/ethosu/vela/high_level_command_to_npu_op.py index 77501210..812e8e9a 100644 --- a/ethosu/vela/high_level_command_to_npu_op.py +++ b/ethosu/vela/high_level_command_to_npu_op.py @@ -44,6 +44,7 @@ from .api import NpuRoundingMode from .api import NpuShape3D from .api import NpuTileBox from .architecture_features import ArchitectureFeatures +from .architecture_features import Block from .data_type import DataType from .high_level_command_stream import Box from .high_level_command_stream import Command @@ -148,22 +149,20 @@ def get_rounding_mode(op: Operation) -> NpuRoundingMode: def create_padding(cmd: NpuStripe, primary_op: Operation) -> NpuPadding: if primary_op.type.npu_block_type == NpuBlockType.VectorProduct: return NpuPadding(top=0, left=0, bottom=0, right=0) - explicit_padding = list(primary_op.attrs["explicit_padding"]) # (top, left, bottom, right) + top, left, bottom, right = primary_op.attrs["explicit_padding"] # Check if this is for horizontal ifm streaming if not (cmd.is_first_h_stripe and cmd.is_last_h_stripe): - explicit_padding[0] = cmd.pad_top - explicit_padding[2] = cmd.pad_bottom + top = cmd.pad_top + bottom = cmd.pad_bottom # Indexing from end since a 1x1 Avgpool might have been added with non 4-dimensional input/output, # because of activation function needed to be fused. if cmd.ifm_box.start_coord[-2] > 0: - explicit_padding[1] = 0 - if cmd.ifm_box.end_coord[-2] < cmd.ifm_tensor.shape[-2]: - explicit_padding[3] = 0 - return NpuPadding( - top=explicit_padding[0], left=explicit_padding[1], bottom=explicit_padding[2], right=explicit_padding[3] - ) + left = 0 + if cmd.ifm_box.end_coord[-2] < Block.from_shape(cmd.ifm_tensor.shape).width: + right = 0 + return NpuPadding(top=top, left=left, bottom=bottom, right=right) def get_region(tens: Tensor, arch: ArchitectureFeatures) -> int: @@ -197,10 +196,10 @@ def get_upscale(op: Operation) -> NpuResamplingMode: def get_ifm_depth(npu_block_type: NpuBlockType, ifm_box: Box, ofm_box: Box) -> int: if npu_block_type in (NpuBlockType.ConvolutionMxN, NpuBlockType.VectorProduct, NpuBlockType.ReduceSum): - shape = ifm_box.get_size_shape() + block = ifm_box.get_block() else: - shape = ofm_box.get_size_shape() - return shape[-1] + block = ofm_box.get_block() + return block.depth def use_zero_point_0(ps, tens: Tensor, is_ifm_tensor: bool) -> bool: @@ -335,22 +334,18 @@ def set_common_op_fields(npu_op: NpuBlockOperation, cmd: NpuStripe, arch: Archit """Sets common fields of the given operation""" ps = cmd.ps op = ps.primary_op - in_shape = cmd.ifm_box.get_size_shape() - out_shape = cmd.ofm_box.get_size_shape() - ofm_height = out_shape[-3] if len(out_shape) >= 4 else 1 - ofm_width = out_shape[-2] if len(out_shape) >= 2 else 1 - ofm_depth = out_shape[-1] if len(out_shape) >= 1 else 1 - ifm_height = in_shape[-3] if len(in_shape) >= 4 else 1 - if op.type.npu_block_type in (NpuBlockType.ConvolutionMxN, NpuBlockType.VectorProduct, NpuBlockType.ReduceSum): - ifm_depth = in_shape[-1] if len(in_shape) >= 1 else 1 - else: - ifm_depth = ofm_depth + + ifm_height = cmd.ifm_box.get_block().height + ifm_width = Block.from_shape(cmd.ifm_tensor.shape).width + ifm_depth = get_ifm_depth(op.type.npu_block_type, cmd.ifm_box, cmd.ofm_box) npu_op.ifm = create_feature_map(cmd.ifm_tensor, cmd.ifm_box, arch) - npu_op.ifm.shape = NpuShape3D(height=ifm_height, width=cmd.ifm_tensor.shape[-2], depth=ifm_depth) + npu_op.ifm.shape = NpuShape3D(height=ifm_height, width=ifm_width, depth=ifm_depth) npu_op.ifm.quantization = get_ifm_or_ifm2_quantization(ps, cmd.ifm_tensor) + + out_block = cmd.ofm_box.get_block() npu_op.ofm = create_feature_map(cmd.ofm_tensor, cmd.ofm_box, arch) - npu_op.ofm.shape = NpuShape3D(height=ofm_height, width=ofm_width, depth=ofm_depth) + npu_op.ofm.shape = NpuShape3D(height=out_block.height, width=out_block.width, depth=out_block.depth) npu_op.ofm.quantization = get_ofm_quantization(ps, cmd.ofm_tensor) if cmd.weight_tensor is not None: @@ -429,9 +424,9 @@ def create_npu_elementwise_op(cmd: NpuStripe, arch: ArchitectureFeatures) -> Npu npu_op.ifm2_scalar = cmd.ifm2_tensor.values.item(0) npu_op.ifm2.shape = NpuShape3D(height=0, width=0, depth=0) else: - box_shp = cmd.ifm2_box.get_size_shape() - height = box_shp[-3] if len(box_shp) >= 3 else 1 - npu_op.ifm2.shape = NpuShape3D(height=height, width=cmd.ifm2_tensor.shape[-2], depth=box_shp[-1]) + ifm2_blk = cmd.ifm2_box.get_block() + ifm2_width = Block.from_shape(cmd.ifm2_tensor.shape).width + npu_op.ifm2.shape = NpuShape3D(height=ifm2_blk.height, width=ifm2_width, depth=ifm2_blk.depth) set_common_op_fields(npu_op, cmd, arch) # Check if output scale needs to be overridden output_scale = None |