diff options
Diffstat (limited to 'ethosu/vela/tosa_graph_optimiser.py')
| -rw-r--r-- | ethosu/vela/tosa_graph_optimiser.py | 98 |
1 files changed, 72 insertions, 26 deletions
diff --git a/ethosu/vela/tosa_graph_optimiser.py b/ethosu/vela/tosa_graph_optimiser.py index fe18ce35..44e0f8ec 100644 --- a/ethosu/vela/tosa_graph_optimiser.py +++ b/ethosu/vela/tosa_graph_optimiser.py @@ -19,21 +19,38 @@ from . import rewrite_graph from .api import NpuRoundingMode from .data_type import DataType from .debug_database import DebugDatabase +from .graph_optimiser_util import calc_explicit_padding from .graph_optimiser_util import needed_total_padding from .graph_optimiser_util import set_ifm_ofm_op_shapes from .graph_optimiser_util import set_tensor_equivalence from .operation import ExplicitScaling from .operation import NpuBlockType from .operation import Op -from .operation import Padding +from .operation_util import create_avgpool_nop -def calc_padding_and_skirt(padding_type, kernel, input_shape, explicit_padding): +def replace_rescale_with_avg_pool(rescale_op): + assert rescale_op.type == Op.Rescale + + avgpool_op = create_avgpool_nop(rescale_op.name + "_avgpool") + rescale_op_clone = rescale_op.clone() + op = rescale_op + op.attrs = avgpool_op.attrs.copy() + op.type = Op.AvgPool + DebugDatabase.add_optimised(rescale_op_clone, op) + + return op + + +def calc_skirt(kernel, input_shape, explicit_padding): k_w, k_h = kernel.dilated_wh() s_x, s_y = kernel.stride ypad = needed_total_padding(int(input_shape.height), int(s_y), int(k_h)) xpad = needed_total_padding(int(input_shape.width), int(s_x), int(k_w)) - left_pad, right_pad, top_pad, bottom_pad = explicit_padding + + top, left, bottom, right = explicit_padding + top_pad, bottom_pad = calc_explicit_padding(int(input_shape.height), int(s_y), int(k_h), int(top), int(bottom)) + left_pad, right_pad = calc_explicit_padding(int(input_shape.width), int(s_x), int(k_w), int(left), int(right)) padding = (top_pad, left_pad, bottom_pad, right_pad) skirt = (top_pad, left_pad, ypad - top_pad, xpad - left_pad) @@ -42,16 +59,14 @@ def calc_padding_and_skirt(padding_type, kernel, input_shape, explicit_padding): def add_padding_fields(op, arch, nng): if op.run_on_npu: - if "padding" in op.attrs: + if "explicit_padding" in op.attrs: input_shape = op.ifm_shapes[0] if op.type == Op.Conv2DBackpropInputSwitchedBias: # TODO not yet supported, but there will be need for separate handling assert False else: - padding, skirt = calc_padding_and_skirt( - Padding.EXPLICIT, op.kernel, input_shape, op.attrs.get("padding"), - ) + padding, skirt = calc_skirt(op.kernel, input_shape, op.attrs.get("explicit_padding")) op.attrs["explicit_padding"] = padding op.attrs["skirt"] = skirt @@ -104,7 +119,6 @@ def rewrite_rescale(op, arch, nng): prev_op = ifm.ops[0] # TODO currently not supported - assert prev_op.type not in (Op.Placeholder, Op.SubgraphInput, Op.Const) assert len(ifm.consumer_list) == 1 input_zp = op.attrs["input_zp"] @@ -126,27 +140,26 @@ def rewrite_rescale(op, arch, nng): print("Error (fuse_rescale): zp of tensors producer/consumer differs unexpectedidly ") assert False ifm.quantization.zero_point = input_zp - - if not scale32: - double_round = False + ofm.quantization.zero_point = output_zp + for s, m in zip(shift, multiplier): + # TODO these are the TOSA limitations + assert m >= 0 + assert 2 <= s <= 62 + # TODO these are the HW limitations + assert 0 <= s < (1 << 6) + explicit_scaling = ExplicitScaling(per_channel, shift, multiplier) + + if double_round and scale32: + rounding_mode = NpuRoundingMode.TFL + else: + rounding_mode = NpuRoundingMode.NATURAL if prev_op.type.is_depthwise_conv2d_op() or prev_op.type.is_conv2d_op() or prev_op.type == Op.FullyConnected: assert len(multiplier) == len(shift) == len(prev_op.bias.values) if ifm.dtype == DataType.int32 and per_channel: - for s, m in zip(shift, multiplier): - # TODO these are the TOSA limitations - assert m >= 0 - assert 2 <= s <= 62 - # TODO these are the HW limitations - assert 0 <= s < (1 << 6) - prev_op.explicit_scaling = ExplicitScaling(per_channel, shift, multiplier) - ofm.quantization.zero_point = output_zp - - if double_round: - prev_op.rounding_mode = NpuRoundingMode.TFL - else: - prev_op.rounding_mode = NpuRoundingMode.NATURAL + prev_op.explicit_scaling = explicit_scaling + prev_op.rounding_mode = rounding_mode # Bypass op prev_op.set_output_tensor(ofm) @@ -155,13 +168,42 @@ def rewrite_rescale(op, arch, nng): else: print("Warning, unsupported fusing of TOSA Rescale previous operator is of type:", prev_op.type) assert False - + # TODO which are the cases we need to and can do standalone Rescale? + # TODO should we try to identify a conversion uint8<->int8 accomplished by 2 RESCALE ops? + # origin might be TFLite op QUANTIZE, should we look to see if they can be translated to QUANTIZE? + # limited to these at the moment: + elif ( + (ifm.dtype == DataType.int8 and ofm.dtype == DataType.int8) + or (ifm.dtype == DataType.uint8 and ofm.dtype == DataType.int8) + or (ifm.dtype == DataType.int8 and ofm.dtype == DataType.uint8) + ): + # Create NOP performing the RESCALE + avgpool_op = replace_rescale_with_avg_pool(op) + avgpool_op.rounding_mode = rounding_mode + + if per_channel: + # TODO + avgpool_op.explicit_scaling = explicit_scaling + print("Warning, unsupported TOSA Rescale") + assert False + else: + avgpool_op.explicit_scaling = explicit_scaling else: print("Warning, unsupported fusing of TOSA Rescale previous operator is of type:", prev_op.type) assert False return op +def fixup_quantization(op, arch, nng): + if op.ifm and op.ifm.quantization.zero_point is None: + op.ifm.quantization.zero_point = 0 + if op.ifm2 and op.ifm2.quantization.zero_point is None: + op.ifm.quantization.zero_point = 0 + if op.ofm and op.ofm.quantization.zero_point is None: + op.ofm.quantization.zero_point = 0 + return op + + def supported_operator_check(op, arch, nng): op.run_on_npu = arch.tosa_supported_operators.is_operator_supported(op) return op @@ -187,10 +229,14 @@ def tosa_optimise_graph(nng, arch): nng, sg, arch, [], op_rewrite_list, rewrite_unsupported=False, ) - # Post-processing step + # Post-processing step 1 for idx, sg in enumerate(nng.subgraphs): nng.subgraphs[idx] = rewrite_graph.rewrite_graph_pre_order( nng, sg, arch, [], [rewrite_activation, add_padding_fields], ) + # Post-processing step 2 + for idx, sg in enumerate(nng.subgraphs): + nng.subgraphs[idx] = rewrite_graph.rewrite_graph_pre_order(nng, sg, arch, [], [fixup_quantization],) + return nng |