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| author | Fredrik Svedberg <fredrik.svedberg@arm.com> | 2020-06-03 15:43:31 +0200 |
|---|---|---|
| committer | Louis Verhaard <louis.verhaard@arm.com> | 2020-08-05 16:26:04 +0200 |
| commit | a0c3624899edc601525a589643c802469003f89d (patch) | |
| tree | 4fc52db04cd29901b3e5d4a7425a7a641e9647fb /ethosu/vela/operation.py | |
| parent | 9a03fdff316662be69a1adc4e391e43bc6519b08 (diff) | |
| download | ethos-u-vela-a0c3624899edc601525a589643c802469003f89d.tar.gz | |
[MLBEDSW-2335] SoftMax int16
Added graph rewrite of Softmax for int16.
Change-Id: Id7885af6056a23e8b8362fb61ae94283251eb398
Signed-off-by: Fredrik Svedberg <fredrik.svedberg@arm.com>
Diffstat (limited to 'ethosu/vela/operation.py')
| -rw-r--r-- | ethosu/vela/operation.py | 27 |
1 files changed, 23 insertions, 4 deletions
diff --git a/ethosu/vela/operation.py b/ethosu/vela/operation.py index 448d8382..7134fd82 100644 --- a/ethosu/vela/operation.py +++ b/ethosu/vela/operation.py @@ -25,13 +25,25 @@ class NpuBlockType(enum.Enum): Pooling = 3 ConvolutionDepthWise = 4 ElementWise = 5 + ReduceSum = 6 class Operation: """Class representing a Neural Network operation. Has a name, a type, input and output tensors, as well as an attribute dictionary.""" - __slots__ = "type", "name", "op_index", "attrs", "inputs", "outputs", "flops", "scheduled_pass", "run_on_npu" + __slots__ = ( + "type", + "name", + "op_index", + "attrs", + "inputs", + "outputs", + "flops", + "scheduled_pass", + "run_on_npu", + "activation_lut", + ) def __init__(self, op_type, name): self.type = op_type @@ -43,6 +55,7 @@ input and output tensors, as well as an attribute dictionary.""" self.run_on_npu = True self.scheduled_pass = None self.op_index = None # input network operator index + self.activation_lut = None def clone(self, suffix="_clone"): res = Operation(self.type, self.name + suffix) @@ -80,7 +93,7 @@ input and output tensors, as well as an attribute dictionary.""" elif self.type == "Conv2DBackpropInputSwitchedBias": bias_idx = 3 - elif npu_block_type == NpuBlockType.Pooling: + elif npu_block_type in (NpuBlockType.Pooling, NpuBlockType.ReduceSum): ifm_idx = 0 ofm_idx = 0 elif npu_block_type == NpuBlockType.VectorProduct: @@ -102,8 +115,8 @@ input and output tensors, as well as an attribute dictionary.""" ifm2_idx = 1 ofm_idx = 0 - # LeakyRelu and Abs have a single IFM - if self.type in set(("LeakyRelu", "Abs")): + # LeakyRelu, Abs and CLZ have a single IFM + if self.type in set(("LeakyRelu", "Abs", "CLZ")): ifm2_idx = -1 elif self.type == "Conv2DBackpropInput": @@ -292,3 +305,9 @@ input and output tensors, as well as an attribute dictionary.""" assert False return input_tens, outputs, axis, offset_start, offset_end + + def set_activation_lut(self, lut_tensor): + lut_tensor.consumer_list.append(self) + self.attrs["fused_activation_function"] = "LUT" + self.activation_lut = lut_tensor + self.inputs.append(lut_tensor) |