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author | Michael McGeagh <michael.mcgeagh@arm.com> | 2020-08-07 11:54:28 +0100 |
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committer | Fredrik Knutsson <fredrik.knutsson.hunnebo@gmail.com> | 2020-08-12 06:30:46 +0000 |
commit | c5b549b599ff459a29115a48e8f067eaa5891638 (patch) | |
tree | 62b22dfda83f4665b10f549d9f9d7ddaedff15a1 /ethosu/vela/graph_optimiser.py | |
parent | 5778ffdab61a46369c73c91f2c6289ba9833e3a3 (diff) | |
download | ethos-u-vela-c5b549b599ff459a29115a48e8f067eaa5891638.tar.gz |
MLBEDSW-2637 Utilise new tensor and operator funcs
add_input_tensor, set_output_tensor, create_const_tensor and
create_reshape_tensor have recently been added.
This replaces all found existing instances with these new helper
functions
Signed-off-by: Michael McGeagh <michael.mcgeagh@arm.com>
Change-Id: If33be8dbf237b2087b562b03cdeb51da1f99a786
Diffstat (limited to 'ethosu/vela/graph_optimiser.py')
-rw-r--r-- | ethosu/vela/graph_optimiser.py | 82 |
1 files changed, 24 insertions, 58 deletions
diff --git a/ethosu/vela/graph_optimiser.py b/ethosu/vela/graph_optimiser.py index a9d5cce5..582924c4 100644 --- a/ethosu/vela/graph_optimiser.py +++ b/ethosu/vela/graph_optimiser.py @@ -28,6 +28,8 @@ from .numeric_util import full_shape from .operation import NpuBlockType from .operation import Operation from .softmax import SoftMax +from .tensor import create_const_tensor +from .tensor import create_reshape_tensor from .tensor import QuantizationParameters from .tensor import Tensor @@ -84,7 +86,6 @@ def rewrite_split(tens, arch): tens.ops = [] new_op = Operation("SplitSliceRead", split_op.name) new_op.inputs = [inp] - new_op.outputs = [tens] # For Split the offset cannot be extracted from the tensor so it has to # be calculated from the index of the output tensor @@ -102,7 +103,7 @@ def rewrite_split(tens, arch): new_op.attrs["split_start"] = offset_start new_op.attrs["split_end"] = offset_end new_op.run_on_npu = True - tens.ops.append(new_op) + new_op.set_output_tensor(tens) return tens @@ -168,14 +169,12 @@ def fixup_conv2d_backprop(op, arch): if len(op.inputs) < 4: # Add bias/scale tensor filled with zeros - scale_op = Operation("Const", op.name + "_bias") scale_tens = Tensor([weight_sets], DataType.int32, op.name + "_bias_tens") scale_tens.values = [0] * weight_sets scale_tens.quant_values = [0] * weight_sets - scale_tens.ops = [scale_op] - scale_op.outputs = [scale_tens] - scale_tens.consumer_list = [op] - op.inputs.append(scale_tens) + scale_op = Operation("Const", op.name + "_bias") + scale_op.set_output_tensor(scale_tens) + op.add_input_tensor(scale_tens) # Update strides op.attrs.update({"stride_w": 1, "stride_h": 1, "strides": (1, 1, 1, 1)}) @@ -199,8 +198,7 @@ def convert_resizebilinear_1x1_to_add(op): tens.quantization.zero_point = 0 tens.consumer_list = [op] tens_op = op.inputs[1].ops[0] - tens_op.outputs = [tens] - tens.ops = [tens_op] + tens_op.set_output_tensor(tens) # Set the add inputs op.inputs[1] = op.inputs[0] op.inputs[0] = tens @@ -233,22 +231,7 @@ def fixup_fully_connected_input(op, arch): desired_shape = [batch_size, n_in_elems] if inp.shape != desired_shape: # mismatch, insert a reshape to fix this. - reshape_name = op.name + "_reshape" - new_shape_tens = Tensor([1], DataType.int32, reshape_name + "_shape") - new_shape_tens.values = np.array(desired_shape) - new_shape_tens_const = Operation("Const", new_shape_tens.name + "_const") - new_shape_tens.ops = [new_shape_tens_const] - new_shape_tens_const.outputs = [new_shape_tens] - - reshape_op = Operation("Reshape", reshape_name) - reshape_op.inputs = [inp, new_shape_tens] - reshape_op.attrs["new_shape"] = desired_shape - reshape_out = inp.clone("_reshaped") - reshape_out.set_all_shapes(desired_shape) - reshape_out.ops = [reshape_op] - reshape_op.outputs = [reshape_out] - - op.inputs[0] = reshape_out + op.inputs[0] = create_reshape_tensor(inp, desired_shape) return op @@ -261,22 +244,16 @@ def fixup_pack_input(op, arch): desired_shape = op.inputs[0].shape[:axis] + [1] + op.inputs[0].shape[axis:] # Construct 1 shape tensor to be used by all inserted reshape ops - new_shape_name = op.name + "_reshape_shape" - new_shape_tens = Tensor([1], DataType.int32, new_shape_name) - new_shape_tens.values = np.array(desired_shape) - new_shape_tens_const = Operation("Const", new_shape_tens.name + "_const") - new_shape_tens.ops = [new_shape_tens_const] - new_shape_tens_const.outputs = [new_shape_tens] + new_shape_tens = create_const_tensor(op.name + "_reshape_shape", [1], DataType.int32, desired_shape) for idx, inp in enumerate(op.inputs): - reshape_name = op.name + str(idx) + "_reshape" - reshape_op = Operation("Reshape", reshape_name) - reshape_op.inputs = [inp, new_shape_tens] - reshape_op.attrs["new_shape"] = desired_shape reshape_out = inp.clone("_reshaped") reshape_out.set_all_shapes(desired_shape) - reshape_out.ops = [reshape_op] - reshape_op.outputs = [reshape_out] + + reshape_op = Operation("Reshape", "{}{}_reshape".format(op.name, idx)) + reshape_op.attrs["new_shape"] = desired_shape + reshape_op.inputs = [inp, new_shape_tens] + reshape_op.set_output_tensor(reshape_out) op.inputs[idx] = reshape_out @@ -335,22 +312,17 @@ def fixup_unpack_output(tens, arch): reshape_input_shape = tens.shape[:axis] + [1] + tens.shape[axis:] # Construct 1 shape tensor to be used by all inserted reshape ops - new_shape_name = op.name + "_reshape_shape" - new_shape_tens = Tensor([1], DataType.int32, new_shape_name) - new_shape_tens.values = np.array(tens.shape) - new_shape_tens_const = Operation("Const", new_shape_tens.name + "_const") - new_shape_tens.ops = [new_shape_tens_const] - new_shape_tens_const.outputs = [new_shape_tens] + new_shape_tens = create_const_tensor(op.name + "_reshape_shape", [1], DataType.int32, tens.shape) for idx, out_tens in enumerate(op.outputs): - reshape_name = op.name + str(idx) + "_reshape" - reshape_op = Operation("Reshape", reshape_name) - reshape_op.outputs = [out_tens] reshape_in = out_tens.clone("_reshaped") reshape_in.set_all_shapes(reshape_input_shape) reshape_in.ops = [op] - out_tens.ops = [reshape_op] + + reshape_op = Operation("Reshape", "{}{}_reshape".format(op.name, idx)) + reshape_op.attrs["new_shape"] = reshape_input_shape reshape_op.inputs = [reshape_in, new_shape_tens] + reshape_op.set_output_tensor(out_tens) op.outputs[idx] = reshape_in @@ -517,17 +489,12 @@ def convert_conv_to_fc(op, arch): fc_ofm_tensor.set_all_shapes([1, fc_ofm_tensor.shape[-1]]) fc_ofm_tensor.ops = [op] # Add a reshape after the new OFM to convert it back to the original 4D shape - reshape_name = op.name + "_reshape_post" - new_shape_tens = Tensor([1], DataType.int32, reshape_name + "_shape") - new_shape_tens.values = np.array(orig_ofm_tensor.shape) - new_shape_tens_const = Operation("Const", new_shape_tens.name + "_const") - new_shape_tens.ops = [new_shape_tens_const] - new_shape_tens_const.outputs = [new_shape_tens] + reshape_name = op.name + "_reshape" + new_shape_tens = create_const_tensor(reshape_name + "_shape", [1], DataType.int32, orig_ofm_tensor.shape) reshape_op = Operation("Reshape", reshape_name) - reshape_op.inputs = [fc_ofm_tensor, new_shape_tens] reshape_op.attrs["new_shape"] = orig_ofm_tensor.shape - orig_ofm_tensor.ops = [reshape_op] - reshape_op.outputs = [orig_ofm_tensor] + reshape_op.inputs = [fc_ofm_tensor, new_shape_tens] + reshape_op.set_output_tensor(orig_ofm_tensor) # Replace this ops OFM to point to the 2D tensor op.outputs[0] = fc_ofm_tensor return op @@ -542,8 +509,7 @@ def fixup_act_reorder(op, arch): act_op.inputs = [prep_op.inputs[0]] act_op_out = act_op.inputs[0].clone("_acted") act_op_out.quantization = op.outputs[0].quantization.clone() - act_op_out.ops = [act_op] - act_op.outputs = [act_op_out] + act_op.set_output_tensor(act_op_out) prep_op.inputs[0] = act_op_out prep_op.outputs[0].quantization = act_op_out.quantization.clone() |