diff options
Diffstat (limited to 'ethosu/vela')
-rw-r--r-- | ethosu/vela/debug_database.py | 15 | ||||
-rw-r--r-- | ethosu/vela/graph_optimiser.py | 42 | ||||
-rw-r--r-- | ethosu/vela/high_level_command_stream.py | 122 | ||||
-rw-r--r-- | ethosu/vela/high_level_command_stream_generator.py | 32 | ||||
-rw-r--r-- | ethosu/vela/high_level_command_to_npu_op.py | 3 | ||||
-rw-r--r-- | ethosu/vela/nn_graph.py | 6 | ||||
-rw-r--r-- | ethosu/vela/npu_performance.py | 41 | ||||
-rw-r--r-- | ethosu/vela/operation.py | 28 | ||||
-rw-r--r-- | ethosu/vela/pass_packing.py | 11 | ||||
-rw-r--r-- | ethosu/vela/shape4d.py | 77 | ||||
-rw-r--r-- | ethosu/vela/shared_buffer_allocation.py | 20 | ||||
-rw-r--r-- | ethosu/vela/softmax.py | 4 | ||||
-rw-r--r-- | ethosu/vela/tensor.py | 17 | ||||
-rw-r--r-- | ethosu/vela/test/test_graph_optimiser.py | 5 | ||||
-rw-r--r-- | ethosu/vela/test/test_supported_operators.py | 2 | ||||
-rw-r--r-- | ethosu/vela/test/testutil.py | 6 |
16 files changed, 201 insertions, 230 deletions
diff --git a/ethosu/vela/debug_database.py b/ethosu/vela/debug_database.py index 77e13eb0..203503f2 100644 --- a/ethosu/vela/debug_database.py +++ b/ethosu/vela/debug_database.py @@ -23,7 +23,7 @@ import lxml.etree as xml from . import numeric_util from .operation import Operation -from .shape4d import Shape4D + UntypedDict = Dict[Any, Any] UntypedList = List[Any] @@ -79,18 +79,9 @@ class DebugDatabase: src_uid = cls._sourceUID[parent] uid = len(cls._optimisedUID) cls._optimisedUID[op] = (uid, src_uid) - ofm_shape = op.ofm_shapes[0] if op.ofm_shapes else Shape4D(op.outputs[0].shape) + ofm_shape = op.ofm_shapes[0] if op.ofm_shapes else numeric_util.full_shape(3, op.outputs[0].shape, 1) cls._optimisedTable.append( - [ - uid, - src_uid, - op.type, - op.kernel.width, - op.kernel.height, - ofm_shape.width, - ofm_shape.height, - ofm_shape.depth, - ] + [uid, src_uid, op.type, op.kernel.width, op.kernel.height, ofm_shape[-2], ofm_shape[-3], ofm_shape[-1]] ) @classmethod diff --git a/ethosu/vela/graph_optimiser.py b/ethosu/vela/graph_optimiser.py index 1128a311..fdb0fae0 100644 --- a/ethosu/vela/graph_optimiser.py +++ b/ethosu/vela/graph_optimiser.py @@ -37,7 +37,6 @@ from .operation import Op from .operation import Operation from .operation import Padding from .operation_util import create_avgpool_nop -from .shape4d import Shape4D from .softmax import SoftMax from .tensor import check_quantized_tens_scaling_equal from .tensor import create_const_tensor @@ -83,7 +82,6 @@ def rewrite_concat(tens, arch, nng): new_op.run_on_npu = True tens.ops.append(new_op) DebugDatabase.add_optimised(concat_op, new_op) - new_op.set_ifm_ofm_shapes() assert tens.shape[axis] == offset # If axis corresponds to C-dimension, NHCWB16 can only be used in the output if all the concat_start's are a @@ -123,8 +121,7 @@ def rewrite_split(tens, arch, nng): if out == tens: break axis_4D = axis + (4 - len(out.shape)) - - offset_start[axis_4D] += split_op.ofm_shapes[idx].get_dim(axis_4D) + offset_start[axis_4D] += split_op.ofm_shapes[idx][axis_4D] # If start offset is not a multiple of 16 in the C-dimension, NHCWB16 need to be avoided in the input if (offset_start[-1] % 16) != 0: @@ -135,7 +132,6 @@ def rewrite_split(tens, arch, nng): new_op.attrs["split_start"] = offset_start new_op.run_on_npu = True new_op.set_output_tensor(tens) - new_op.set_ifm_ofm_shapes() DebugDatabase.add_optimised(split_op, new_op) return tens @@ -193,7 +189,6 @@ def fixup_conv2d_backprop(op, arch, nng): if op.type == Op.Conv2DBackpropInput: # flip the inputs op.inputs[0], op.inputs[2] = op.inputs[2], op.inputs[0] - op.set_ifm_ofm_shapes() op.type = Op.Conv2DBackpropInputSwitchedBias # Update strides @@ -221,7 +216,8 @@ def convert_resizebilinear_1x1_to_add(op): # Set the add inputs op.inputs[1] = op.inputs[0] op.inputs[0] = tens - op.set_ifm_ofm_shapes() + op.ifm_shapes = [] + op.ofm_shapes = [] return op @@ -327,14 +323,14 @@ def convert_batched_fc_shape(op, arch, nng): ofm = op.outputs[0] # Check if the FC is 2D and first dimension indicates batching # TOD0 op.ifm_shape[0] > 1 is enough when refactory is complete - if len(ifm.shape) == len(ofm.shape) == 2 and ifm.shape[0] > 1 and op.ifm_shapes[0].batch > 1: + if len(ifm.shape) == len(ofm.shape) == 2 and ifm.shape[0] > 1 and op.ifm_shapes[0][0] > 1: n = ifm.shape[0] batching_split = {4: (2, 2), 8: (2, 4), 16: (4, 4)} h, w = batching_split.get(n, (1, n)) prev_op = ifm.ops[0] desired_shape = [1, h, w, ifm.shape[-1]] - op.ifm_shapes[0] = Shape4D(desired_shape) + op.ifm_shapes[0] = desired_shape if len(ifm.consumer_list) == 1 and prev_op is not None and prev_op.type == Op.Reshape: # There is a preceding Reshape @@ -360,7 +356,7 @@ def convert_batched_fc_shape(op, arch, nng): weight_tensor.set_all_shapes(list(weight_tensor.quant_values.shape)) desired_shape = [1, h, w, ofm.shape[-1]] - op.ofm_shapes[0] = Shape4D(desired_shape) + op.ofm_shapes[0] = desired_shape if ( len(ofm.consumer_list) == 1 @@ -399,7 +395,6 @@ def fixup_pack_input(op, arch, nng): reshape_op.attrs["new_shape"] = desired_shape reshape_op.inputs = [inp, new_shape_tens] reshape_op.set_output_tensor(reshape_out) - reshape_op.set_ifm_ofm_shapes() DebugDatabase.add_optimised(op, reshape_op) op.inputs[idx] = reshape_out @@ -418,7 +413,6 @@ def unfuse_activation_function(op, arch, nng): act_op.set_output_tensor(out_tens) act_op.add_input_tensor(intermediate_tens) op.set_output_tensor(intermediate_tens) - act_op.set_ifm_ofm_shapes() return op @@ -463,7 +457,7 @@ def fixup_stridedslice_output(tens, arch, nng): new_shape_tens = create_const_tensor(op.name + "_reshape_shape", [1], DataType.int32, tens.shape) for idx, out_tens in enumerate(op.outputs): - op.ofm_shapes[idx] = Shape4D(new_shape_tens.shape) + op.ofm_shapes[idx] = new_shape_tens reshape_in = out_tens.clone("_reshaped") reshape_in.set_all_shapes(reshape_input_shape) reshape_in.ops = [op] @@ -472,7 +466,6 @@ def fixup_stridedslice_output(tens, arch, nng): reshape_op.attrs["new_shape"] = reshape_input_shape reshape_op.inputs = [reshape_in, new_shape_tens] reshape_op.set_output_tensor(out_tens) - reshape_op.set_ifm_ofm_shapes() op.outputs[idx] = reshape_in @@ -500,7 +493,6 @@ def fixup_unpack_output(tens, arch, nng): reshape_op.attrs["new_shape"] = reshape_input_shape reshape_op.inputs = [reshape_in, new_shape_tens] reshape_op.set_output_tensor(out_tens) - reshape_op.set_ifm_ofm_shapes() DebugDatabase.add_optimised(op, reshape_op) op.outputs[idx] = reshape_in @@ -596,8 +588,7 @@ def convert_conv_to_fc(op, arch, nng): # caching/double buffering for the weights. # (Weights dont need to be reloaded for convs when IFM H and W are 1) if op.type == Op.Conv2DBias: - h = op.ifm_shapes[0].height - w = op.ifm_shapes[0].width + _, h, w, _ = op.ifm_shapes[0] kh, kw, _, _ = op.inputs[1].shape if h == 1 and w == 1 and kh == 1 and kw == 1: # Overwrite this op as a Fully Connected Op @@ -625,11 +616,9 @@ def convert_conv_to_fc(op, arch, nng): reshape_op.attrs["new_shape"] = orig_ofm_tensor.shape reshape_op.inputs = [fc_ofm_tensor, new_shape_tens] reshape_op.set_output_tensor(orig_ofm_tensor) - reshape_op.set_ifm_ofm_shapes() # Replace this ops OFM to point to the 2D tensor op.outputs[0] = fc_ofm_tensor - op.set_ifm_ofm_shapes() # Record optimisation in debug database DebugDatabase.add_optimised(op, reshape_op) DebugDatabase.add_optimised(op, op) @@ -660,7 +649,6 @@ def fixup_relus_with_differing_ifm_ofm_scaling(op, arch, nng): relu_fused_op.add_input_tensor(ifm) relu_fused_op.set_output_tensor(ofm) - relu_fused_op.set_ifm_ofm_shapes() op = relu_fused_op return op @@ -680,8 +668,8 @@ def fixup_act_reorder(op, arch, nng): act_op_out = act_op.inputs[0].clone("_acted") act_op_out.quantization = op.outputs[0].quantization.clone() act_op.set_output_tensor(act_op_out) - act_op.ifm_shapes[0] = Shape4D(prep_op.inputs[0].shape) - act_op.ofm_shapes[0] = Shape4D(act_op_out.shape) + act_op.ifm_shapes[0] = full_shape(4, prep_op.inputs[0].shape, 1) + act_op.ofm_shapes[0] = full_shape(4, act_op_out.shape, 1) # Update the consumer list act_op_out.consumer_list = op.outputs[0].consumer_list.copy() @@ -851,7 +839,6 @@ def convert_lrelu_to_mul_max(op, arch): mul_alpha.add_input_tensor(alpha_tens) fm_alpha = ofm.clone(op.name + "_alpha") mul_alpha.set_output_tensor(fm_alpha) - mul_alpha.set_ifm_ofm_shapes() DebugDatabase.add_optimised(op, mul_alpha) if check_quantized_tens_scaling_equal(ifm, ofm): @@ -873,7 +860,6 @@ def convert_lrelu_to_mul_max(op, arch): mul_identity.add_input_tensor(identity_tens) fm_id = ofm.clone(op.name + "_id") mul_identity.set_output_tensor(fm_id) - mul_identity.set_ifm_ofm_shapes() DebugDatabase.add_optimised(op, mul_identity) # Convert LeakyRelu to Max, add the results of the multiplication(s) as inputs @@ -904,7 +890,7 @@ def convert_to_lut(op, lut_values, lut_name): quantization.zero_point = 0 tens = create_const_tensor(op.inputs[0].name + "_scalar0", [], ifm.dtype, [0], np.uint8, quantization=quantization) op.add_input_tensor(tens) - op.ifm_shapes.append(Shape4D(tens.shape)) + op.ifm_shapes.append(full_shape(4, tens.shape, 1)) # The LUT must be applied without any preceding rescaling (the LUT itself performs the rescale), # so even if the OFM has a different scale than the IFM, the generated OFM scale instructions @@ -1172,7 +1158,11 @@ def optimise_graph_b(nng, arch, verbose_graph=False): for idx, sg in enumerate(nng.subgraphs): # combined rewrite graph pass nng.subgraphs[idx] = rewrite_graph.rewrite_graph_pre_order( - nng, sg, arch, [fixup_unpack_output, fixup_stridedslice_output, rewrite_concat, rewrite_split], [], + nng, + sg, + arch, + [fixup_unpack_output, fixup_stridedslice_output, rewrite_concat, rewrite_split], + [set_ifm_ofm_op_shapes], ) if verbose_graph: diff --git a/ethosu/vela/high_level_command_stream.py b/ethosu/vela/high_level_command_stream.py index 9cbda452..bb4f1424 100644 --- a/ethosu/vela/high_level_command_stream.py +++ b/ethosu/vela/high_level_command_stream.py @@ -15,14 +15,11 @@ # limitations under the License. # Description: # Contains classes that hold commands for the high-level command stream (one command per DMA or NPU stripe). -from typing import List - import numpy as np from .architecture_features import Block from .numeric_util import round_up_divide from .operation import NpuBlockType -from .shape4d import Shape4D class Box: @@ -35,15 +32,15 @@ class Box: def transform_with_strides_and_skirt( self, - strides: List[int], - skirt: List[int], - ifm_shape: Shape4D, - npu_block_type: NpuBlockType, - concat_axis: int = 0, - concat_offset: int = 0, - split_offset: int = None, - k_height: int = 1, - upscaling_factor: int = 1, + strides, + skirt, + ifm_shape, + npu_block_type, + concat_axis=0, + concat_offset=0, + split_offset=None, + k_height=1, + upscaling_factor=1, ): new_start_coord = list(self.start_coord) new_end_coord = list(self.end_coord) @@ -61,15 +58,15 @@ class Box: ): # these types of operations do a "dot product" or sum over the entire IFM new_start_coord[-1] = 0 - new_end_coord[-1] = ifm_shape.depth + new_end_coord[-1] = ifm_shape[-1] - if npu_block_type == NpuBlockType.ElementWise and len(new_end_coord) >= 1: - new_end_coord[-1] = min(new_end_coord[-1], ifm_shape.depth) - if len(new_end_coord) >= 2: - new_end_coord[-2] = min(new_end_coord[-2], ifm_shape.width * upscaling_factor) - if len(new_end_coord) >= 3: + if npu_block_type == NpuBlockType.ElementWise and min(len(new_end_coord), len(ifm_shape)) >= 1: + new_end_coord[-1] = min(new_end_coord[-1], ifm_shape[-1]) + if min(len(new_end_coord), len(ifm_shape)) >= 2: + new_end_coord[-2] = min(new_end_coord[-2], ifm_shape[-2] * upscaling_factor) + if min(len(new_end_coord), len(ifm_shape)) >= 3: original_end_coord = list(new_end_coord) - new_end_coord[-3] = min(new_end_coord[-3], ifm_shape.height * upscaling_factor) + new_end_coord[-3] = min(new_end_coord[-3], ifm_shape[-3] * upscaling_factor) pad_top = 0 pad_bottom = 0 @@ -77,7 +74,7 @@ class Box: if len(new_start_coord) >= 2: stride = strides[2] new_start_coord[-2] = max(new_start_coord[-2] * stride - skirt[1], 0) - new_end_coord[-2] = min(new_end_coord[-2] * stride + skirt[3], ifm_shape.width) + new_end_coord[-2] = min(new_end_coord[-2] * stride + skirt[3], ifm_shape[-2]) if len(new_start_coord) >= 3: stride = strides[1] @@ -89,20 +86,23 @@ class Box: pad_top = max(0, 0 - new_start_coord[-3]) + skirt_top_remainder new_start_coord[-3] = max(new_start_coord[-3], 0) - if (new_end_coord[-3] * stride + skirt[2]) > (ifm_shape.height * upscaling_factor): + while len(ifm_shape) < 3: + ifm_shape = [1] + ifm_shape + + if (new_end_coord[-3] * stride + skirt[2]) > (ifm_shape[-3] * upscaling_factor): # pad_bottom is calculated based the diff between the end position of the weight kernel, # after last stride and the ifm height. - if upscaling_factor != 1 and original_end_coord[-3] > ifm_shape.height * upscaling_factor: + if upscaling_factor != 1 and original_end_coord[-3] > ifm_shape[-3] * upscaling_factor: # Special case for Transpose Convolution with VALID padding. - pad_bottom = original_end_coord[-3] - (ifm_shape.height * upscaling_factor) + pad_bottom = original_end_coord[-3] - (ifm_shape[-3] * upscaling_factor) else: k_start = new_start_coord[-3] - pad_top - pad_bottom = max(0, k_start + total_stride + k_height - (ifm_shape.height * upscaling_factor)) + pad_bottom = max(0, k_start + total_stride + k_height - (ifm_shape[-3] * upscaling_factor)) # Adjust for upscaling new_start_coord[-3] = max(new_start_coord[-3] // upscaling_factor, 0) new_end_coord[-3] = new_end_coord[-3] * stride + skirt[2] + (skirt[2] % upscaling_factor) - new_end_coord[-3] = max(min(new_end_coord[-3] // upscaling_factor, ifm_shape.height), 1) + new_end_coord[-3] = max(min(new_end_coord[-3] // upscaling_factor, ifm_shape[-3]), 1) return Box(new_start_coord, new_end_coord), pad_top, pad_bottom @@ -197,7 +197,7 @@ class NpuStripe(Command): self.pad_top = pad_top self.pad_bottom = pad_bottom for i in range(len(self.ofm_box.end_coord)): - assert self.ofm_box.end_coord[i] <= ps.ofm_shapes[0].get_dim(i) + assert self.ofm_box.end_coord[i] <= ps.ofm_shapes[0][i] def is_npu_pass_command(self): return True @@ -251,6 +251,76 @@ class NpuStripe(Command): assert res >= 0 return res + def get_single_block_command(self, block_idx): + block_cfg = (self.block_config[0], self.block_config[1], self.block_config[3]) + dims = self.get_block_dimensions() + strides = dims[1] * dims[2], dims[2], 1 + coord = [] + idx_left = block_idx + for s in strides: + c = idx_left // s + idx_left -= c * s + coord.append(c) + + assert idx_left == 0 + + # put in dummy height/widths in case we're dealing with FC layers + ofm_start = list(self.ofm_box.start_coord) + ofm_end = list(self.ofm_box.end_coord) + + # cut out a nice block shape + for idx in (-1, -2, -3): + if len(ofm_start) >= -idx: + ofm_start[idx] += block_cfg[idx] * coord[idx] + ofm_end[idx] = min(ofm_end[idx], ofm_start[idx] + block_cfg[idx]) + + ps = self.ps + strides = None + skirt = None + if ps.primary_op is not None: + strides = ps.primary_op.attrs.get("strides", None) + skirt = ps.primary_op.attrs.get("skirt", None) + npu_block_type = ps.npu_block_type + + ofm_box = Box(ofm_start, ofm_end) + ifm_box, _, _ = ofm_box.transform_with_strides_and_skirt( + strides, skirt, self.ifm_tensor.shape, npu_block_type, self.concat_axis, self.concat_offset + ) + + weight_box = None + if self.weight_tensor is not None: + weight_oc_start = ofm_start[-1] + weight_oc_end = ofm_end[-1] + if self.concat_axis - len(self.weight_tensor.shape) == -1: + weight_oc_start -= self.concat_offset + weight_oc_end -= self.concat_offset + + weight_box = Box.make_weight_box( + self.weight_tensor.shape, + npu_block_type, + weight_oc_start, + weight_oc_end, + self.weight_tensor.weight_transpose_depthwise, + ) + + return NpuStripe( + self.ps, + self.block_config, + self.is_first, + self.is_last, + self.is_first_h_stripe, + self.is_last_h_stripe, + self.ifm_tensor, + ifm_box, + self.ofm_tensor, + ofm_box, + self.weight_tensor, + weight_box, + self.scale_tensor, + self.concat_axis, + self.concat_offset, + ) + class DMA(Command): def __init__(self, ps, in_tensor, out_tensor, box): diff --git a/ethosu/vela/high_level_command_stream_generator.py b/ethosu/vela/high_level_command_stream_generator.py index 60e62aa6..18a419c0 100644 --- a/ethosu/vela/high_level_command_stream_generator.py +++ b/ethosu/vela/high_level_command_stream_generator.py @@ -27,7 +27,6 @@ from .numeric_util import round_up_divide from .operation import create_activation_function from .operation import NpuBlockType from .operation import Op -from .shape4d import Shape4D from .tensor import TensorPurpose @@ -91,8 +90,8 @@ def generate_high_level_command_stream_for_pass(strat, passes, block_configs, id weight_tensor = ps.weight_tensor scale_tensor = ps.scale_tensor - ofm_start = [0, 0, 0, 0] - ofm_end = ofm_shape.as_list() + ofm_start = [0] * len(ofm_shape) + ofm_end = list(ofm_shape) strides = None skirt = None @@ -101,9 +100,9 @@ def generate_high_level_command_stream_for_pass(strat, passes, block_configs, id strides = ps.primary_op.attrs.get("strides", None) skirt = ps.primary_op.attrs.get("skirt", None) if ps.primary_op.type == Op.Conv2DBackpropInputSwitchedBias: - upscaling = ofm_shape.height // ifm_shape.height + upscaling = ofm_shape[-3] // ifm_shape[-3] elif ps.primary_op.type == Op.ResizeBilinear: - upscaling = round_up_divide(ofm_shape.height, ifm_shape.height) + upscaling = round_up_divide(ofm_shape[-3], ifm_shape[-3]) concat_axis = 0 concat_offset = 0 @@ -136,7 +135,14 @@ def generate_high_level_command_stream_for_pass(strat, passes, block_configs, id if ifm_shape is not None: ifm_box, _, _ = ofm_box.transform_with_strides_and_skirt( - strides, skirt, ifm_shape, npu_block_type, concat_axis, concat_offset, split_offsets[0], upscaling, + strides, + skirt, + ifm_tensor.shape, + npu_block_type, + concat_axis, + concat_offset, + split_offsets[0], + upscaling, ) else: ifm_box = Box([], []) @@ -157,7 +163,7 @@ def generate_high_level_command_stream_for_pass(strat, passes, block_configs, id intermediate_box, _, _ = ofm_box.transform_with_strides_and_skirt( strides, skirt, - Shape4D(intermediate.shape), + intermediate.shape, npu_block_type, concat_axis, concat_offset, @@ -206,7 +212,6 @@ def generate_high_level_command_stream_for_pass(strat, passes, block_configs, id ) elif strat == SchedulingStrategy.IfmStream: - assert ifm_shape is not None y_step = block_config[0] y_start = ofm_start[-3] y_dim = ofm_end[-3] @@ -217,7 +222,8 @@ def generate_high_level_command_stream_for_pass(strat, passes, block_configs, id prev_pass_gen = generate_high_level_command_stream_for_pass(strat, passes, block_configs, idx - 1) else: ifm_y_present = 1 - ifm_y_present = ifm_shape.height + if len(ifm_shape) >= 3: + ifm_y_present = ifm_shape[-3] prev_pass_gen = [] prev_pass = None @@ -270,7 +276,7 @@ def generate_high_level_command_stream_for_pass(strat, passes, block_configs, id intermediate_box, _, _ = ofm_box.transform_with_strides_and_skirt( strides, skirt, - Shape4D(intermediate.shape), + intermediate.shape, npu_block_type, concat_axis, concat_offset, @@ -374,13 +380,13 @@ def calc_allowed_ofm_ifm_overlap_for_pass_list(strat, passes, block_configs): if cmd.is_npu_pass_command(): if cmd.is_first: ifm_read = cmd.ifm_tensor.address_offset_for_coordinate( - cmd.ifm_box.start_coord, cmd.ps.ifm_shapes[0].as_list(), is_top_box=False + cmd.ifm_box.start_coord, shape=cmd.ps.ifm_shapes[0], is_top_box=False ) if ifm_read is None: return 0 if cmd.is_last: write_offset = cmd.ofm_tensor.address_offset_for_coordinate( - cmd.ofm_box.end_coord, cmd.ps.ofm_shapes[0].as_list(), is_top_box=True + cmd.ofm_box.end_coord, shape=cmd.ps.ofm_shapes[0], is_top_box=True ) if write_offset is None: return 0 @@ -393,7 +399,7 @@ def calc_allowed_ofm_ifm_overlap_for_pass_list(strat, passes, block_configs): if cmd.is_first: ifm_read = cmd.ifm_tensor.address_offset_for_coordinate( - cmd.ifm_box.end_coord, cmd.ps.ifm_shapes[0].as_list(), is_top_box=True + cmd.ifm_box.end_coord, shape=cmd.ps.ifm_shapes[0], is_top_box=True ) min_overlap = max(min_overlap, 0) diff --git a/ethosu/vela/high_level_command_to_npu_op.py b/ethosu/vela/high_level_command_to_npu_op.py index 07117025..9380374e 100644 --- a/ethosu/vela/high_level_command_to_npu_op.py +++ b/ethosu/vela/high_level_command_to_npu_op.py @@ -58,7 +58,6 @@ from .register_command_stream_generator import generate_command_stream from .register_command_stream_util import BASE_PTR_INDEX_MEM2MEM from .register_command_stream_util import to_npu_kernel from .register_command_stream_util import UNARY_ELEMWISE_OPS -from .shape4d import Shape4D from .tensor import MemType from .tensor import Tensor from .tensor import TensorBlockTraversal @@ -232,7 +231,7 @@ def get_ofm_quantization(ps, tens: Tensor) -> Optional[NpuQuantization]: return NpuQuantization(scale_f32=ofm_quant.scale_f32, zero_point=zero_point) -def create_feature_map(tens: Tensor, box: Box, arch: ArchitectureFeatures, fm_shape: Shape4D) -> NpuFeatureMap: +def create_feature_map(tens: Tensor, box: Box, arch: ArchitectureFeatures, fm_shape: List[int]) -> NpuFeatureMap: """Creates feature map with common fields populated""" fm = NpuFeatureMap() fm.region = get_region(tens, arch) diff --git a/ethosu/vela/nn_graph.py b/ethosu/vela/nn_graph.py index d2c848ad..67925176 100644 --- a/ethosu/vela/nn_graph.py +++ b/ethosu/vela/nn_graph.py @@ -21,10 +21,8 @@ # Subgraph - Holds a neural network subgraph, pointing at Tensors, Operations, Passes, and CascadedPasses. # Graph - A full neural network graph with one or more Subgraphs. import enum -from typing import List from .operation import Op -from .shape4d import Shape4D class PassPlacement(enum.Enum): @@ -60,8 +58,8 @@ class Pass: self.name = name self.cascade = None self.placement = placement - self.ifm_shapes: List[Shape4D] = [] - self.ofm_shapes: List[Shape4D] = [] + self.ifm_shapes = [] + self.ofm_shapes = [] # TODO: rename is_element_wise because it is not the same as an ElementWise operator. It is used by the tensor # allocation and requires that the OFM and IFM has the exact same address. Essentially complete overlap. diff --git a/ethosu/vela/npu_performance.py b/ethosu/vela/npu_performance.py index 4ca46831..c2ec4424 100644 --- a/ethosu/vela/npu_performance.py +++ b/ethosu/vela/npu_performance.py @@ -48,7 +48,7 @@ def rolling_buffer_dims_from_passes(arch, ps1, block_config_ps1, ps2, block_conf if ps2.npu_block_type in (NpuBlockType.ConvolutionMxN, NpuBlockType.VectorProduct): op = ps2.primary_op - ifm_block_depth = arch.calc_ifm_block_depth(op.ifm_shapes[0].depth, op.ifm.dtype.size_in_bits()) + ifm_block_depth = arch.calc_ifm_block_depth(op.ifm_shapes[0][-1], op.ifm.dtype.size_in_bits()) else: ifm_block_depth = block_config_ps2[-1] @@ -231,9 +231,9 @@ def estimate_conv_pooling_cycles( arch.config.ofm_ublock.height == 2 and npu_block_type in (NpuBlockType.ConvolutionMxN, NpuBlockType.ConvolutionDepthWise, NpuBlockType.VectorProduct) - and ofm_tens_shape.height == 1 + and ofm_tens_shape[1] == 1 # Optimisation only applies for even width tensors - and ofm_tens_shape.width % 2 == 0 + and ofm_tens_shape[2] % 2 == 0 and kernel_dims[0] == 1 ): ofm_ublock.width = 4 @@ -319,14 +319,14 @@ def estimate_conv_pooling_cycles( cycles_dpu_blk += delay_cycles if npu_block_type in (NpuBlockType.ConvolutionMxN, NpuBlockType.VectorProduct, NpuBlockType.ReduceSum): - cycles_dpu_blk *= numeric_util.round_up_divide(ifm_tens_shape.depth, ifm_block.depth) + cycles_dpu_blk *= numeric_util.round_up_divide(ifm_tens_shape[3], ifm_block.depth) cycles_dpu_blk /= arch.ncores num_ofm_blk = ( - numeric_util.round_up_divide(ofm_tens_shape.height, ofm_block.height) - * numeric_util.round_up_divide(ofm_tens_shape.width, ofm_block.width) - * numeric_util.round_up_divide(ofm_tens_shape.depth, ofm_block.depth) + numeric_util.round_up_divide(ofm_tens_shape[1], ofm_block.height) + * numeric_util.round_up_divide(ofm_tens_shape[2], ofm_block.width) + * numeric_util.round_up_divide(ofm_tens_shape[3], ofm_block.depth) ) cycles_output_blk = estimate_output_cycles( @@ -336,7 +336,7 @@ def estimate_conv_pooling_cycles( if scale_tensor: cycles_bias_blk = ( 10 - * min(ofm_block.depth, ofm_tens_shape.depth) + * min(ofm_block.depth, ofm_tens_shape[3]) * arch.memory_latency[scale_tensor.mem_area][BandwidthDirection.Read] / 256 ) @@ -420,8 +420,8 @@ def performance_metrics_for_pass(arch, ps, block_config=None, rewrite_list=None, npu_block_type = primary_op.type.npu_block_type ifm_tensor, _, weight_tensor, ofm_tensor = ps.get_primary_op_ifm_ifm2_weights_ofm() - ifm_tensor_shape = ps.primary_op.ifm_shapes[0].clone() - ofm_tensor_shape = ps.primary_op.ofm_shapes[0].clone() + ifm_tensor_shape = list(ps.primary_op.ifm_shapes[0]) + ofm_tensor_shape = list(ps.primary_op.ofm_shapes[0]) if npu_block_type == NpuBlockType.ReduceSum: block_traversal = TensorBlockTraversal.DepthFirst @@ -434,7 +434,7 @@ def performance_metrics_for_pass(arch, ps, block_config=None, rewrite_list=None, else: block_traversal = TensorBlockTraversal.Default ifm_block_depth = get_ifm_block_depth( - npu_block_type, ifm_tensor_shape.depth, ifm_tensor.dtype.size_in_bits(), block_traversal, ofm_block.depth + npu_block_type, ifm_tensor_shape[3], ifm_tensor.dtype.size_in_bits(), block_traversal, ofm_block.depth ) ifm_block = arch.get_ifm_block_size( ifm_block_depth, ofm_block, primary_op.kernel, ifm_resampling_mode=ifm_tensor.resampling_mode @@ -448,12 +448,11 @@ def performance_metrics_for_pass(arch, ps, block_config=None, rewrite_list=None, NpuBlockType.ReduceSum, ): # extent the ifm to full dimension - - batch_size = ifm_tensor_shape.batch + batch_size = ifm_tensor_shape[0] # add in padding - ifm_tensor_shape.height += explicit_padding[0] + explicit_padding[2] # height += top and bottom - ifm_tensor_shape.width += explicit_padding[1] + explicit_padding[3] # width += left and right + ifm_tensor_shape[1] += explicit_padding[0] + explicit_padding[2] # height += top and bottom + ifm_tensor_shape[2] += explicit_padding[1] + explicit_padding[3] # width += left and right if npu_block_type != NpuBlockType.Pooling: if npu_block_type == NpuBlockType.ReduceSum: @@ -469,9 +468,9 @@ def performance_metrics_for_pass(arch, ps, block_config=None, rewrite_list=None, weight_tensor_bandwidth_compression_scale = weight_tensor.bandwidth_compression_scale nn_ops = ( - int(ofm_tensor_shape.batch) - * int(ofm_tensor_shape.height) - * int(ofm_tensor_shape.width) + int(ofm_tensor_shape[0]) + * int(ofm_tensor_shape[1]) + * int(ofm_tensor_shape[2]) * int(weight_tensor_shape[0]) * int(weight_tensor_shape[1]) * int(weight_tensor_shape[2]) @@ -482,7 +481,7 @@ def performance_metrics_for_pass(arch, ps, block_config=None, rewrite_list=None, primary_op.attrs["ksize"][1], primary_op.attrs["ksize"][2], 1, - ifm_tensor_shape.depth, + ifm_tensor_shape[3], ] weight_tensor_bandwidth_shape = weight_tensor_shape weight_tensor_element_size = 0 @@ -505,8 +504,8 @@ def performance_metrics_for_pass(arch, ps, block_config=None, rewrite_list=None, replacement_read_bws[ifm_tensor] = ifm_tensor.bandwidth() * ifm_read_multiple weight_read_multiple = numeric_util.round_up_divide( - ofm_tensor_shape.height, ofm_block.height - ) * numeric_util.round_up_divide(ofm_tensor_shape.width, ofm_block.width) + ofm_tensor_shape[1], ofm_block.height + ) * numeric_util.round_up_divide(ofm_tensor_shape[2], ofm_block.width) replacement_read_bws[weight_tensor] = ( batch_size * shape_num_elements(weight_tensor_bandwidth_shape) diff --git a/ethosu/vela/operation.py b/ethosu/vela/operation.py index c80e18b5..be26a26b 100644 --- a/ethosu/vela/operation.py +++ b/ethosu/vela/operation.py @@ -26,7 +26,6 @@ from typing import TYPE_CHECKING from .errors import VelaError from .numeric_util import full_shape -from .shape4d import Shape4D if TYPE_CHECKING: @@ -373,7 +372,7 @@ def create_activation_function(op_type: Op) -> ActivationFunction: return act -def get_slice_offsets(input_shape: List[int], offset_tens: int, offset_mask: int, is_begin: bool = True): +def get_slice_offsets(input_shape, offset_tens, offset_mask, is_begin=True): # For strided slice operator: get start or end offsets offsets = len(input_shape) * [0] if is_begin else input_shape[:] for idx in range(len(input_shape)): @@ -428,8 +427,8 @@ class Operation: self.op_index = None # input network operator index self.activation_lut = None self._kernel = None - self.ifm_shapes: List[Shape4D] = [] - self.ofm_shapes: List[Shape4D] = [] + self.ifm_shapes = [] + self.ofm_shapes = [] def clone(self, suffix="_clone"): res = Operation(self.type, self.name + suffix) @@ -708,9 +707,6 @@ class Operation: raise VelaError("\n".join(lines)) def set_ifm_ofm_shapes(self): - self.ifm_shapes = [] - self.ofm_shapes = [] - ifm_tensor, ifm2_tensor, weight_tensor, ofm_tensor = self.get_ifm_ifm2_weights_ofm() # set all shapes to op, as 4D @@ -720,24 +716,24 @@ class Operation: batch_size = elms // n_in_elems assert batch_size * n_in_elems == elms - self.ifm_shapes.append(Shape4D([batch_size, 1, 1, n_in_elems])) - self.ofm_shapes.append(Shape4D(ofm_tensor.get_full_shape())) + self.ifm_shapes.append([batch_size, 1, 1, n_in_elems]) + self.ofm_shapes.append(ofm_tensor.get_full_shape()) elif self.type == Op.Softmax: - self.ifm_shapes.append(Shape4D(ifm_tensor.get_full_shape())) - self.ofm_shapes.append(Shape4D(ofm_tensor.get_full_shape())) + self.ifm_shapes.append(ifm_tensor.get_full_shape()) + self.ofm_shapes.append(ofm_tensor.get_full_shape()) elif self.type.is_split_op or self.type.is_concat_op(): for inp in self.inputs: if inp is not None: - self.ifm_shapes.append(Shape4D(full_shape(4, inp.shape, 1))) + self.ifm_shapes.append(full_shape(4, inp.shape, 1)) else: self.ifm_shapes.append(None) for out in self.outputs: if out is not None: - self.ofm_shapes.append(Shape4D(full_shape(4, out.shape, 1))) + self.ofm_shapes.append(full_shape(4, out.shape, 1)) else: self.ofm_shapes.append(None) else: - self.ifm_shapes.append(Shape4D(full_shape(4, ifm_tensor.shape, 1))) + self.ifm_shapes.append(full_shape(4, ifm_tensor.shape, 1)) if ifm2_tensor is not None: - self.ifm_shapes.append(Shape4D(full_shape(4, ifm2_tensor.shape, 1))) - self.ofm_shapes.append(Shape4D(full_shape(4, ofm_tensor.shape, 1))) + self.ifm_shapes.append(full_shape(4, ifm2_tensor.shape, 1)) + self.ofm_shapes.append(full_shape(4, ofm_tensor.shape, 1)) diff --git a/ethosu/vela/pass_packing.py b/ethosu/vela/pass_packing.py index 8f6660c2..095a78d4 100644 --- a/ethosu/vela/pass_packing.py +++ b/ethosu/vela/pass_packing.py @@ -231,9 +231,9 @@ def pack_into_passes(nng, arch, verbose_packing=False): ofm_tensor = op.ofm if ofm_tensor is None: ofm_tensor = op.outputs[0] - build_pass((op,), ofm_tensor, op.ofm_shapes[0].clone()) + build_pass((op,), ofm_tensor) - def build_pass(start_ops_to_process, ofm_tensor=None, ofm_shapes=None): + def build_pass(start_ops_to_process, ofm_tensor=None): reverse_ops_list = [] curr_flags = PassFlags.Empty npu_block_type = NpuBlockType.Default @@ -416,7 +416,8 @@ def pack_into_passes(nng, arch, verbose_packing=False): ps.ifm_shapes.append(ps.primary_op.ifm_shapes[0]) ps.ofm_tensor = ofm_tensor - ps.ofm_shapes.append(ofm_shapes) + if ps.primary_op is not None: + ps.ofm_shapes.append(ps.primary_op.ofm_shapes[0]) assert ps.placement != PassPlacement.Npu or ps.ofm_tensor is not None ps.weight_tensor = ps.get_primary_op_ifm_weights()[1] @@ -452,11 +453,11 @@ def pack_into_passes(nng, arch, verbose_packing=False): avgpool_out = inp.clone("_avgpooled") avgpool_out.consumer_list.append(op) avgpool_op.set_output_tensor(avgpool_out) - avgpool_op.set_ifm_ofm_shapes() + avgpool_op.ifm_shapes = op.ifm_shapes + avgpool_op.ofm_shapes = op.ofm_shapes op.inputs[0] = avgpool_out op_list.insert(0, avgpool_op) - op.set_ifm_ofm_shapes() DebugDatabase.add_optimised(op, avgpool_op) return avgpool_op diff --git a/ethosu/vela/shape4d.py b/ethosu/vela/shape4d.py deleted file mode 100644 index a1b4feaa..00000000 --- a/ethosu/vela/shape4d.py +++ /dev/null @@ -1,77 +0,0 @@ -# Copyright (C) 2020 Arm Limited or its affiliates. All rights reserved. -# -# SPDX-License-Identifier: Apache-2.0 -# -# Licensed under the Apache License, Version 2.0 (the License); you may -# not use this file except in compliance with the License. -# You may obtain a copy of the License at -# -# www.apache.org/licenses/LICENSE-2.0 -# -# Unless required by applicable law or agreed to in writing, software -# distributed under the License is distributed on an AS IS BASIS, WITHOUT -# WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. -# See the License for the specific language governing permissions and -# limitations under the License. -# Description: -# Defines the class Shape4D. -from .numeric_util import full_shape - - -class Shape4D: - """ - 4D Shape (in NHWC format) - """ - - def __init__(self, shape, base=1): - assert shape is not None - assert len(shape) <= 4 - self._shape4D = tuple(full_shape(4, shape, base)) - - def __str__(self): - return f"<Shape4D {self.as_list()}>" - - def __eq__(self, other): - return self._shape4D == other._shape4D - - def clone(self): - return Shape4D(self.as_list()) - - @property - def batch(self): - return self._shape4D[0] - - @property - def height(self): - return self._shape4D[1] - - @property - def width(self): - return self._shape4D[2] - - @property - def depth(self): - return self._shape4D[3] - - @batch.setter - def batch(self, new_batch): - self._shape4D = (new_batch, self._shape4D[1], self._shape4D[2], self._shape4D[3]) - - @height.setter - def height(self, new_height): - self._shape4D = (self._shape4D[0], new_height, self._shape4D[2], self._shape4D[3]) - - @width.setter - def width(self, new_width): - self._shape4D = (self._shape4D[0], self._shape4D[1], new_width, self._shape4D[3]) - - @depth.setter - def depth(self, new_depth): - self._shape4D = (self._shape4D[0], self._shape4D[1], self._shape4D[2], new_depth) - - def get_dim(self, dim): - assert -4 <= dim < 4 - return self._shape4D[dim] - - def as_list(self): - return list(self._shape4D) diff --git a/ethosu/vela/shared_buffer_allocation.py b/ethosu/vela/shared_buffer_allocation.py index d8faf369..1f027d60 100644 --- a/ethosu/vela/shared_buffer_allocation.py +++ b/ethosu/vela/shared_buffer_allocation.py @@ -32,7 +32,6 @@ from .operation import Kernel from .operation import NpuBlockType from .range_set import MemoryRangeSet from .register_command_stream_util import to_kernel -from .shape4d import Shape4D from .tensor import MemArea @@ -196,14 +195,14 @@ def shared_buffer_allocation_for_pass(arch, ps) -> SharedBufferAllocation: ifm_bits = ifm_tensor.dtype.size_in_bits() ifm_shape = ps.primary_op.ifm_shapes[0] - if ifm_tensor.shape != []: - ifm_depth = ifm_shape.depth + if ifm_shape != []: + ifm_depth = ifm_shape[-1] if is_elementwise: ifm_count = 2 if ifm_tensor.shape == []: # Scalar in ifm1 assert ifm2_tensor - ifm_depth = ps.primary_op.ifm_shapes[1].depth + ifm_depth = ps.primary_op.ifm_shapes[1][-1] ifm_count = 1 elif not ifm2_tensor or ifm2_tensor.shape == []: # Scalar in ifm2 ifm_count = 1 @@ -252,7 +251,7 @@ def shared_buffer_allocation_for_npu_op( ifm_bits=ifm_bits, ifm_depth=ifm_depth, ifm_count=ifm_count, - ofm_shape=Shape4D(ofm_shape), + ofm_shape=ofm_shape, ) @@ -266,9 +265,14 @@ def find_suitable_block_configs(arch, alloc: SharedBufferAllocation) -> List[Tup # Constrain the search space if the OFM is smaller than the max block size # - Add other block search constraints here if required - max_block_width = alloc.ofm_shape.width - max_block_height = alloc.ofm_shape.height - max_block_depth = alloc.ofm_shape.depth + if len(alloc.ofm_shape) <= 2: + max_block_height = max_block_width = alloc.ofm_shape[0] + else: + max_block_width = alloc.ofm_shape[-2] + max_block_height = alloc.ofm_shape[-3] + + # Common block depth + max_block_depth = alloc.ofm_shape[-1] # Constrain to valid ranges before search max_block_width = min(arch.ofm_block_max.width, max_block_width) diff --git a/ethosu/vela/softmax.py b/ethosu/vela/softmax.py index 3b4bace9..98496539 100644 --- a/ethosu/vela/softmax.py +++ b/ethosu/vela/softmax.py @@ -213,7 +213,7 @@ class SoftMax: ofm = self.op.outputs[0] # Reshape ifm/ofm (if needed) - full_shape = self.op.ifm_shapes[0].as_list() + full_shape = self.op.ifm_shapes[0] if full_shape[0] > 1: full_shape[1] *= full_shape[0] full_shape[0] = 1 @@ -414,7 +414,6 @@ class SoftMax: shr30_op.add_input_tensor(scaled_exp) shr30_op.add_input_tensor(right_shift) shr30_op.set_output_tensor(ofm) - shr30_op.set_ifm_ofm_shapes() DebugDatabase.add_optimised(self.op, shr30_op) return shr30_op @@ -536,7 +535,6 @@ class SoftMax: shr13_op.add_input_tensor(mul_ofm) shr13_op.add_input_tensor(reciprocal_right_shift) shr13_op.set_output_tensor(ofm) - shr13_op.set_ifm_ofm_shapes() DebugDatabase.add_optimised(self.op, shr13_op) return shr13_op diff --git a/ethosu/vela/tensor.py b/ethosu/vela/tensor.py index 093e8771..df8f8868 100644 --- a/ethosu/vela/tensor.py +++ b/ethosu/vela/tensor.py @@ -40,7 +40,6 @@ from .ethos_u55_regs.ethos_u55_regs import resampling_mode from .numeric_util import full_shape from .operation import Op from .operation import Operation -from .shape4d import Shape4D Shape = List @@ -305,7 +304,6 @@ def create_const_tensor( # Operator const_op = Operation(Op.Const, name) const_op.set_output_tensor(const_tensor) - const_op.set_ifm_ofm_shapes() return const_tensor @@ -325,7 +323,8 @@ def create_reshape_tensor(tens, shape, ifm_reshape=True): reshape_op.add_input_tensor(reshape_ifm) reshape_op.add_input_tensor(create_const_tensor(name + "_shape", [1], DataType.int32, shape)) reshape_op.set_output_tensor(reshape_ofm) - reshape_op.set_ifm_ofm_shapes() + reshape_op.ifm_shapes.append(full_shape(4, reshape_ifm.shape, 1)) + reshape_op.ofm_shapes.append(full_shape(4, reshape_ofm.shape, 1)) return reshape_ofm if ifm_reshape else reshape_ifm @@ -609,7 +608,7 @@ class Tensor: def consumers(self) -> List[Operation]: return self.consumer_list - def addresses_for_rolling_buffer(self, start_coord: Shape, end_coord: Shape, fm_shape: Shape4D) -> Tuple: + def addresses_for_rolling_buffer(self, start_coord: Shape, end_coord: Shape, fm_shape: Shape) -> Tuple: # returns ( box_height0, box_height1, box_width, [address_tl, address_tr, address_bl, address_br] ) if self.storage_shape == []: @@ -617,7 +616,7 @@ class Tensor: 1, 1, 1, - [self.address_for_coordinate(start_coord, shape=fm_shape.as_list()), None, None, None], + [self.address_for_coordinate(start_coord, shape=fm_shape), None, None, None], ) storage_shape_4D = full_shape(4, self.storage_shape, 1) @@ -631,20 +630,20 @@ class Tensor: box_width = crossing_x - start_coord[2] addresses: List = [None] * 4 - addresses[0] = self.address_for_coordinate(start_coord, shape=fm_shape.as_list()) + addresses[0] = self.address_for_coordinate(start_coord, shape=fm_shape) if end_coord[2] > crossing_x: addresses[1] = self.address_for_coordinate( - [start_coord[0], start_coord[1], crossing_x, start_coord[3]], shape=fm_shape.as_list() + [start_coord[0], start_coord[1], crossing_x, start_coord[3]], shape=fm_shape ) raise UnsupportedFeatureError("Striping in vertical direction is not supported") if end_coord[1] > crossing_y: addresses[2] = self.address_for_coordinate( - [start_coord[0], crossing_y, start_coord[2], start_coord[3]], shape=fm_shape.as_list() + [start_coord[0], crossing_y, start_coord[2], start_coord[3]], shape=fm_shape ) if end_coord[1] > crossing_y and end_coord[2] > crossing_x: addresses[3] = self.address_for_coordinate( - [start_coord[0], crossing_y, crossing_x, start_coord[3]], shape=fm_shape.as_list() + [start_coord[0], crossing_y, crossing_x, start_coord[3]], shape=fm_shape ) return box_height0, box_height0, box_width, addresses diff --git a/ethosu/vela/test/test_graph_optimiser.py b/ethosu/vela/test/test_graph_optimiser.py index 7fdc4bd8..45377417 100644 --- a/ethosu/vela/test/test_graph_optimiser.py +++ b/ethosu/vela/test/test_graph_optimiser.py @@ -21,7 +21,6 @@ import numpy as np from ethosu.vela.graph_optimiser import convert_batched_fc_shape from ethosu.vela.operation import Op from ethosu.vela.tensor import create_const_tensor -from ethosu.vela.tensor import Shape4D from ethosu.vela.tensor import Tensor from ethosu.vela.test import testutil @@ -36,8 +35,8 @@ def test_convert_batched_fc(): ifm.consumer_list.append(op) - op.ifm_shapes.append(Shape4D([4, 1, 1, 8])) - op.ofm_shapes.append(Shape4D([4, 1, 1, 8])) + op.ifm_shapes.append([4, 1, 1, 8]) + op.ofm_shapes.append([4, 1, 1, 8]) prev_op = op.clone() prev_op.ifm_shapes = op.ifm_shapes diff --git a/ethosu/vela/test/test_supported_operators.py b/ethosu/vela/test/test_supported_operators.py index 973b820d..583821a2 100644 --- a/ethosu/vela/test/test_supported_operators.py +++ b/ethosu/vela/test/test_supported_operators.py @@ -62,7 +62,7 @@ def test_constraint_tens_input_scalar(): def test_constraint_tens_shape_size(): # Tensors cannot be > 4D - op = testutil.create_op_with_quant_tensors(Op.Relu, [1, 1, 8, 8, 8], [1, 1, 8, 8, 8], set_ifm_ofm_shapes=False) + op = testutil.create_op_with_quant_tensors(Op.Relu, [1, 1, 8, 8, 8], [1, 1, 8, 8, 8]) assert not support.is_operator_supported(op) diff --git a/ethosu/vela/test/testutil.py b/ethosu/vela/test/testutil.py index c3459501..63f841b4 100644 --- a/ethosu/vela/test/testutil.py +++ b/ethosu/vela/test/testutil.py @@ -75,7 +75,7 @@ def create_elemwise_op( def create_op_with_quant_tensors( - op_type, ifm_shape, ofm_shape, weights_shape=None, bias_shape=None, datatype=DataType.uint8, set_ifm_ofm_shapes=True + op_type, ifm_shape, ofm_shape, weights_shape=None, bias_shape=None, datatype=DataType.uint8 ): ifm = Tensor(ifm_shape, datatype, "in") ifm.quantization = default_quant_params() @@ -107,9 +107,7 @@ def create_op_with_quant_tensors( bias = create_const_tensor("bias", bias_shape, DataType.int32, np.zeros(bias_shape), np.int32, quantization=qp) op.add_input_tensor(bias) - if set_ifm_ofm_shapes: - op.set_ifm_ofm_shapes() - + op.set_ifm_ofm_shapes() return op |