MLBEDSW-5102 Update removal of memory only operators
Memory only operators such as Reshape, Squeeze and ExpandDims are
removed in the graph optimiser step.
- Added semantic check that memory only operators have same
quantisation parameters on ifm/ofm.
- Added support for the ExpandDims operator.
- Addition and cleanup of related unit tests.
- Removed TOSA from the generated SUPPORTED_OPS.md documentation.
Signed-off-by: Jonas Ohlsson <jonas.ohlsson@arm.com>
Change-Id: If848d8afc58c18806e10997ed94e4dae83f30879
diff --git a/ethosu/vela/test/test_graph_optimiser.py b/ethosu/vela/test/test_graph_optimiser.py
index 2f96572..b8655c9 100644
--- a/ethosu/vela/test/test_graph_optimiser.py
+++ b/ethosu/vela/test/test_graph_optimiser.py
@@ -21,7 +21,6 @@
from ethosu.vela.data_type import DataType
from ethosu.vela.graph_optimiser import optimise_graph
-from ethosu.vela.nn_graph import Graph
from ethosu.vela.nn_graph import NetworkType
from ethosu.vela.operation import Op
from ethosu.vela.operation import Padding
@@ -323,27 +322,23 @@
assert pool_op.attrs["padding"] == Padding.VALID
-# Setup network to test removal of op with op_type Op.Reshape or Op.Squeeze
-# op_type should be Op.Reshape or Op.Squeeze
-def setup_network(op_type):
- assert op_type == Op.Reshape or op_type == Op.Squeeze
- if op_type == Op.Reshape:
- op_str = "reshape"
- elif op_type == Op.Squeeze:
- op_str = "squeeze"
+def test_remove_reshape():
+ """
+ Test that the expected reshape are removed in graph_optimisation
+ """
+ # Create tensors and operators Test1
quant = testutil.default_quant_params()
+
# create reshape1 op
ifm_shape = [64, 16]
reshape1_ofm_shape = [1, 4, 16, 16]
- reshape1_ifm = create_const_tensor(f"{op_str}1_in", ifm_shape, DataType.uint8, np.zeros(ifm_shape))
+ reshape1_ifm = create_const_tensor("reshape1_in", ifm_shape, DataType.uint8, np.zeros(ifm_shape))
reshape1_ifm.quantization = quant
- reshape1_ofm = create_const_tensor(
- f"{op_str}1_out", reshape1_ofm_shape, DataType.uint8, np.zeros(reshape1_ofm_shape)
- )
+ reshape1_ofm = create_const_tensor("reshape1_out", reshape1_ofm_shape, DataType.uint8, np.zeros(reshape1_ofm_shape))
reshape1_ofm.quantization = quant
- shape_tens = create_const_tensor(f"{op_str}1_shape", [1], DataType.int32, reshape1_ofm_shape)
- reshape1_op = testutil.create_op(op_type, [reshape1_ifm, shape_tens], reshape1_ofm, set_ifm_ofm_shapes=False)
+ shape_tens = create_const_tensor("reshape1_shape", [1], DataType.int32, reshape1_ofm_shape)
+ reshape1_op = testutil.create_op(Op.Reshape, [reshape1_ifm, shape_tens], reshape1_ofm, set_ifm_ofm_shapes=False)
reshape1_op.attrs["new_shape"] = reshape1_ofm_shape
reshape1_op.run_on_npu = True
@@ -365,43 +360,42 @@
# create reshape2 op
ofm_shape = [8, 8, 16]
- reshape2_ofm = create_const_tensor(f"{op_str}2_out", ofm_shape, DataType.uint8, np.zeros(ofm_shape))
+ reshape2_ofm = create_const_tensor("reshape2_out", ofm_shape, DataType.uint8, np.zeros(ofm_shape))
reshape2_ofm.quantization = quant
- shape_tens = create_const_tensor(f"{op_str}2_shape", [1], DataType.int32, ofm_shape)
- reshape2_op = testutil.create_op(op_type, [conv_ofm, shape_tens], reshape2_ofm, set_ifm_ofm_shapes=False)
+ shape_tens = create_const_tensor("reshape2_shape", [1], DataType.int32, ofm_shape)
+ reshape2_op = testutil.create_op(Op.Reshape, [conv_ofm, shape_tens], reshape2_ofm, set_ifm_ofm_shapes=False)
reshape2_op.attrs["new_shape"] = ofm_shape
reshape2_op.run_on_npu = True
- nng = Graph()
- sg = testutil.create_subgraph([reshape1_op, conv2d_op, reshape2_op])
- nng.subgraphs.append(sg)
-
- return nng, reshape1_op, conv2d_op, reshape2_op
-
-
-def test_remove_reshape():
- """
- Tests that the expected reshape are removed in graph_optimisation
- """
# Test1 no Reshape op is expected to remain in the NPU subgrapgh
# but first one will be put on CPU
# Network is Reshape-Conv-Reshape
# Result is Conv
- nng, reshape1_op, conv2d_op, reshape2_op = setup_network(Op.Reshape)
+ nng = testutil.create_graph([reshape1_op, conv2d_op, reshape2_op])
arch = testutil.create_arch()
assert verify_graph_health(nng)
- nng = optimise_graph(nng, arch, NetworkType.TFLite)
+ nng = optimise_graph(nng, arch, NetworkType.TFLite, True)
assert verify_graph_health(nng)
- # Test2 reshape1 with different quantisation, this Reshape op is expected to remain
- # Network is Reshape-Conv-Reshape
- # expected is Reshape-Conv
- nng, reshape1_op, conv2d_op, reshape2_op = setup_network(Op.Reshape)
- quant_zp32 = testutil.default_quant_params()
- quant_zp32.zero_point = 32
- reshape1_op.ofm.quantization = quant_zp32
+ # Create tensors and operator Test2
+ # create reshape op
+ reshape_ifm = create_const_tensor("reshape_in", ifm_shape, DataType.uint8, np.zeros(ifm_shape))
+ reshape_ifm.quantization = quant
+ reshape_ofm = create_const_tensor("reshape1_out", reshape1_ofm_shape, DataType.uint8, np.zeros(reshape1_ofm_shape))
+ reshape_ofm.quantization = quant
+ shape_tens = create_const_tensor("reshape1_shape", [1], DataType.int32, reshape1_ofm_shape)
+ reshape_op = testutil.create_op(Op.Reshape, [reshape_ifm, shape_tens], reshape_ofm, set_ifm_ofm_shapes=False)
+ reshape_op.attrs["new_shape"] = reshape1_ofm_shape
+ reshape_op.run_on_npu = True
+
+ # Test2 Reshape ifm/ofm is sg input/output.
+ # Reshape op is expected to be replaced by a AvgPool 'NOP'.
+ #
+ # Network is Reshape
+ # expected is AvgPool
+ nng = testutil.create_graph([reshape_op])
assert verify_graph_health(nng)
- nng = optimise_graph(nng, arch, NetworkType.TFLite)
+ nng = optimise_graph(nng, arch, NetworkType.TFLite, True)
assert verify_graph_health(nng)
@@ -410,23 +404,132 @@
Tests that the expected squeeze are removed in graph_optimisation
"""
+ # Create tensors and operators Test1
+ quant = testutil.default_quant_params()
+
+ # create conv op
+ ifm_shape = [1, 1, 1, 1024]
+ conv_ifm = create_const_tensor("conv_in", ifm_shape, DataType.uint8, np.zeros(ifm_shape))
+ conv_ifm.quantization = quant
+ conv_ofm = Tensor([1, 1, 1, 1001], DataType.uint8, "output")
+ conv_ofm.quantization = quant.clone()
+ weight_tens = Tensor([1, 1, 1024, 1001], DataType.uint8, "weights")
+ weight_tens.values = np.zeros(weight_tens.shape, np.uint8)
+ weight_tens.quantization = quant.clone()
+ bias_tens = Tensor([1001], DataType.int32, "biases")
+
+ attrs = {"padding": Padding.SAME, "stride_w": 1, "stride_h": 1, "dilation_w_factor": 1, "dilation_h_factor": 1}
+ attrs["strides"] = (1, attrs["stride_h"], attrs["stride_w"], 1)
+
+ conv2d_op = testutil.create_op(
+ Op.Conv2D, [conv_ifm, weight_tens, bias_tens], conv_ofm, attrs=attrs, set_ifm_ofm_shapes=False
+ )
+ conv2d_op.run_on_npu = True
+
+ # create squeeze op
+ ofm_shape = [1, 1001]
+ squeeze_ofm = create_const_tensor("squeeze_out", ofm_shape, DataType.uint8, np.zeros(ofm_shape))
+ squeeze_ofm.quantization = quant.clone()
+ squeeze_op = testutil.create_op(Op.Squeeze, [conv_ofm], squeeze_ofm, set_ifm_ofm_shapes=False)
+ squeeze_op.attrs["squeeze_dims"] = [1, 2]
+ squeeze_op.run_on_npu = True
+
# Test1 no Squeeze op is expected to remain in the NPU subgrapgh
- # but first one will be put on CPU
- # Network is Squeeze-Conv-Squeeze
+ #
+ # Network is Conv-Squeeze
# Result is Conv
- nng, squeeze1_op, conv2d_op, squeeze2_op = setup_network(Op.Squeeze)
+ nng = testutil.create_graph([conv2d_op, squeeze_op])
arch = testutil.create_arch()
assert verify_graph_health(nng)
- nng = optimise_graph(nng, arch, NetworkType.TFLite)
+ nng = optimise_graph(nng, arch, NetworkType.TFLite, True)
assert verify_graph_health(nng)
- # Test2 squeeze1 with different quantisation, this Squeeze op is expected to remain
- # Network is Squeeze-Conv-Squeeze
- # expected is Squeeze-Conv
- nng, squeeze1_op, conv2d_op, squeeze2_op = setup_network(Op.Squeeze)
- quant_zp32 = testutil.default_quant_params()
- quant_zp32.zero_point = 32
- squeeze1_op.ofm.quantization = quant_zp32
+ # Create tensors and operator Test2
+ # create squeeze op
+ ifm_shape = [1, 1, 1, 1001]
+ squeeze_ifm = create_const_tensor("squeeze_in", ifm_shape, DataType.uint8, np.zeros(ifm_shape))
+ squeeze_ifm.quantization = quant
+ squeeze_ofm = create_const_tensor("squeeze_out", ofm_shape, DataType.uint8, np.zeros(ofm_shape))
+ squeeze_ofm.quantization = quant.clone()
+ squeeze_op = testutil.create_op(Op.Squeeze, [squeeze_ifm], squeeze_ofm, set_ifm_ofm_shapes=False)
+ squeeze_op.attrs["squeeze_dims"] = [1, 2]
+ squeeze_op.run_on_npu = True
+
+ # Test2 Squeeze ifm/ofm is sg input/output.
+ # Squeeze op is expected to be replaced by a AvgPool 'NOP'.
+ #
+ # Network is Squeeze
+ # expected is AvgPool
+ nng = testutil.create_graph([squeeze_op])
assert verify_graph_health(nng)
- nng = optimise_graph(nng, arch, NetworkType.TFLite)
+ nng = optimise_graph(nng, arch, NetworkType.TFLite, True)
+ assert verify_graph_health(nng)
+
+
+def test_remove_expand_dims():
+ """
+ Tests that the expected ExpandDims are removed in graph_optimisation
+ """
+
+ # Create tensors and operators Test1
+ quant = testutil.default_quant_params()
+
+ # create ExpandDims op
+ ifm_shape = [4, 16, 16]
+ ofm_shape = [1, 4, 16, 16]
+ expand_dims_ifm = create_const_tensor("expand_dims_in", ifm_shape, DataType.uint8, np.zeros(ifm_shape))
+ expand_dims_ifm.quantization = quant
+ expand_dims_ofm = create_const_tensor("expand_dims_out", ofm_shape, DataType.uint8, np.zeros(ofm_shape))
+ expand_dims_ofm.quantization = quant.clone()
+ dim_tens = create_const_tensor("dim_tens", [], DataType.uint8, 1)
+ expand_dims_op = testutil.create_op(
+ Op.ExpandDims, [expand_dims_ifm, dim_tens], expand_dims_ofm, set_ifm_ofm_shapes=False
+ )
+ expand_dims_op.run_on_npu = True
+
+ # create conv op
+ conv_ofm = Tensor([1, 8, 8, 16], DataType.uint8, "output")
+ conv_ofm.quantization = quant.clone()
+ weight_tens = Tensor([1, 1, 16, 16], DataType.uint8, "weights")
+ weight_tens.values = np.zeros(weight_tens.shape, np.uint8)
+ weight_tens.quantization = quant.clone()
+ bias_tens = Tensor([16], DataType.int32, "biases")
+
+ attrs = {"padding": Padding.SAME, "stride_w": 1, "stride_h": 1, "dilation_w_factor": 1, "dilation_h_factor": 1}
+ attrs["strides"] = (1, attrs["stride_h"], attrs["stride_w"], 1)
+
+ conv2d_op = testutil.create_op(
+ Op.Conv2D, [expand_dims_ofm, weight_tens, bias_tens], conv_ofm, attrs=attrs, set_ifm_ofm_shapes=False
+ )
+ conv2d_op.run_on_npu = True
+
+ # Test1 no ExpandDims op is expected to remain in the NPU subgrapgh
+ #
+ # Network is ExpandDims-Conv
+ # Result is Conv
+ nng = testutil.create_graph([expand_dims_op, conv2d_op])
+ arch = testutil.create_arch()
+ assert verify_graph_health(nng)
+ nng = optimise_graph(nng, arch, NetworkType.TFLite, True)
+ assert verify_graph_health(nng)
+
+ # create ExpandDims op
+ expand_dims_ifm = create_const_tensor("expand_dims_in", ifm_shape, DataType.uint8, np.zeros(ifm_shape))
+ expand_dims_ifm.quantization = quant
+ expand_dims_ofm = create_const_tensor("expand_dims_out", ofm_shape, DataType.uint8, np.zeros(ofm_shape))
+ expand_dims_ofm.quantization = quant.clone()
+ dim_tens = create_const_tensor("dim_tens", [], DataType.uint8, 1)
+ expand_dims_op = testutil.create_op(
+ Op.ExpandDims, [expand_dims_ifm, dim_tens], expand_dims_ofm, set_ifm_ofm_shapes=False
+ )
+ expand_dims_op.run_on_npu = True
+
+ # Test2 ExpandDims ifm/ofm is sg input/output.
+ # ExpandDims op is expected to be replaced by a AvgPool 'NOP'.
+ #
+ # Network is ExpandDims
+ # expected is AvgPool
+ nng = testutil.create_graph([expand_dims_op])
+ assert verify_graph_health(nng)
+ nng = optimise_graph(nng, arch, NetworkType.TFLite, True)
assert verify_graph_health(nng)