MLBEDSW-3774 Removed ConcatSliceWrite
-Removed ConcatSliceWrite from the optimised graph.
Always executed as avgpool, which is equivalent with
before the patch.
-Added copy op to enable more removal of reshapes.
Sg input/outputs need to remain. When Reshape input and
outut, are sg input/outputs a copy op is needed to
be inserted, in order to remove the reshape.
Signed-off-by: Patrik Gustavsson <patrik.gustavsson@arm.com>
Change-Id: Id7be9966673ae34499e8518a5544104493fe326b
diff --git a/ethosu/vela/test/test_graph_optimiser.py b/ethosu/vela/test/test_graph_optimiser.py
index 4281d31..40b8cd5 100644
--- a/ethosu/vela/test/test_graph_optimiser.py
+++ b/ethosu/vela/test/test_graph_optimiser.py
@@ -178,17 +178,6 @@
reshape1_op.attrs["new_shape"] = reshape1_ofm_shape
reshape1_op.run_on_npu = True
- # create reshape2 op
- reshape2_ofm_shape = [1, 8, 8, 16]
- reshape2_ofm = create_const_tensor(
- "reshape2_out", reshape2_ofm_shape, DataType.uint8, np.zeros(reshape2_ofm_shape)
- )
- reshape2_ofm.quantization = quant
- shape_tens = create_const_tensor("reshape2_shape", [1], DataType.int32, reshape2_ofm_shape)
- reshape2_op = testutil.create_op(Op.Reshape, [reshape1_ofm, shape_tens], reshape2_ofm, set_ifm_ofm_shapes=False)
- reshape2_op.attrs["new_shape"] = reshape2_ofm_shape
- reshape2_op.run_on_npu = True
-
# create conv op
conv_ofm = Tensor([1, 8, 8, 16], DataType.uint8, "output")
conv_ofm.quantization = quant.clone()
@@ -206,40 +195,37 @@
)
conv2d_op.run_on_npu = True
- # create reshape3 op
+ # create reshape2 op
ofm_shape = [8, 8, 16]
- reshape3_ofm = create_const_tensor("reshape3_out", ofm_shape, DataType.uint8, np.zeros(ofm_shape))
- reshape3_ofm.quantization = quant
- shape_tens = create_const_tensor("reshape3_shape", [1], DataType.int32, ofm_shape)
- reshape3_op = testutil.create_op(Op.Reshape, [conv_ofm, shape_tens], reshape3_ofm, set_ifm_ofm_shapes=False)
- reshape3_op.attrs["new_shape"] = ofm_shape
- reshape3_op.run_on_npu = True
+ reshape2_ofm = create_const_tensor("reshape2_out", ofm_shape, DataType.uint8, np.zeros(ofm_shape))
+ reshape2_ofm.quantization = quant
+ 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, reshape2_op, conv2d_op, reshape3_op])
+ sg = testutil.create_subgraph([reshape1_op, conv2d_op, reshape2_op])
nng.subgraphs.append(sg)
- return nng, reshape1_op, reshape2_op, conv2d_op, reshape3_op
+ return nng, reshape1_op, conv2d_op, reshape2_op
# Test1 no Reshape op is expected to remain in the NPU subgrapgh
# but first one will be put on CPU
- # Network is Reshape-Reshape-Conv-Reshape
- # Result is cpu_Reshape-Conv
- nng, reshape1_op, reshape2_op, conv2d_op, reshape3_op = setup_network()
+ # Network is Reshape-Conv-Reshape
+ # Result is Conv
+ nng, reshape1_op, conv2d_op, reshape2_op = setup_network()
arch = testutil.create_arch()
assert verify_graph_health(nng)
nng = optimise_graph_a(nng, arch)
assert verify_graph_health(nng)
- assert conv2d_op.ifm == reshape1_op.ofm
- assert conv2d_op.ofm == reshape3_op.ofm
- # Test2 reshape2 with different quantisation, this Reshape op is expected to remain
- # Network is Reshape-Reshape-Conv-Reshape
- # expected is cpu_Reshape-Reshape-Conv
- nng, reshape1_op, reshape2_op, conv2d_op, reshape3_op = setup_network()
+ # 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()
quant_zp32 = testutil.default_quant_params()
quant_zp32.zero_point = 32
- reshape2_op.ofm.quantization = quant_zp32
+ reshape1_op.ofm.quantization = quant_zp32
assert verify_graph_health(nng)
nng = optimise_graph_a(nng, arch)
assert verify_graph_health(nng)
- assert conv2d_op.ofm == reshape3_op.ofm