MLBEDSW-3772 Reshape removal
-Removed reshapes in the original graph
-Removed the addition of reshapes to the
optimized graph
-Reshapes with different ifm/ofm quantisation will remain
Signed-off-by: Patrik Gustavsson <patrik.gustavsson@arm.com>
Change-Id: I94862be53dac0d7434815e2aee5ca678228495f8
diff --git a/ethosu/vela/test/test_graph_optimiser.py b/ethosu/vela/test/test_graph_optimiser.py
index b3938bc..b01b07c 100644
--- a/ethosu/vela/test/test_graph_optimiser.py
+++ b/ethosu/vela/test/test_graph_optimiser.py
@@ -20,10 +20,12 @@
from ethosu.vela.data_type import DataType
from ethosu.vela.graph_optimiser import convert_batched_fc_shape
+from ethosu.vela.graph_optimiser import optimise_graph_a
from ethosu.vela.graph_optimiser import optimise_pad
from ethosu.vela.nn_graph import Graph
from ethosu.vela.operation import Op
from ethosu.vela.operation import Padding
+from ethosu.vela.rewrite_graph import verify_graph_health
from ethosu.vela.tensor import create_const_tensor
from ethosu.vela.tensor import Shape4D
from ethosu.vela.tensor import Tensor
@@ -32,50 +34,49 @@
def test_convert_batched_fc():
"""Tests shape conversion of batched fully connected"""
- shape = [4, 8]
- ifm = create_const_tensor("test_in", shape, np.uint8, np.zeros(shape))
- weights = create_const_tensor("weight_in", shape, np.uint8, np.zeros(shape))
+ ifm_shape = [4, 8]
+ ifm = create_const_tensor("test_in", ifm_shape, np.uint8, np.zeros(ifm_shape))
+ w_shape = [8, 4]
+ weights = create_const_tensor("weight_in", w_shape, np.uint8, np.zeros(w_shape))
ofm = Tensor(ifm.shape, np.uint8, "test_out")
op = testutil.create_op(Op.FullyConnected, [ifm, weights], ofm)
ifm.consumer_list.append(op)
- op.ifm_shapes.append(Shape4D([4, 1, 1, 8]))
- op.ofm_shapes.append(Shape4D([4, 1, 1, 8]))
-
prev_op = op.clone()
- prev_op.ifm_shapes = op.ifm_shapes
- prev_op.ofm_shapes = op.ofm_shapes
-
- conv_op = convert_batched_fc_shape(op, None, None)
-
- assert conv_op.ifm != prev_op.ifm
- assert conv_op.ofm != prev_op.ofm
- assert conv_op.type == Op.FullyConnected
- assert len(conv_op.ifm.shape) == 4
- assert conv_op.ifm.shape == conv_op.ofm.shape
- assert conv_op.ifm.ops[0].type == Op.Reshape
-
- shape = [1, 8]
- ifm.shape = shape
- weights.shape = shape
- ofm.shape = shape
- op = testutil.create_op(Op.FullyConnected, [ifm, weights], ofm)
- ifm.consumer_list.append(op)
-
- op.ifm_shapes.append([1, 1, 1, 8])
- op.ofm_shapes.append([1, 1, 1, 8])
-
- prev_op = op.clone()
- prev_op.ifm_shapes = op.ifm_shapes
- prev_op.ofm_shapes = op.ofm_shapes
+ prev_op.ifm_shapes = op.ifm_shapes.copy()
+ prev_op.ofm_shapes = op.ofm_shapes.copy()
conv_op = convert_batched_fc_shape(op, None, None)
assert conv_op.ifm == prev_op.ifm
assert conv_op.ofm == prev_op.ofm
+ assert op.ifm_shapes[0] == Shape4D([1, 2, 2, 8])
+ assert op.ofm_shapes[0] == Shape4D([1, 2, 2, 8])
assert conv_op.type == Op.FullyConnected
assert len(conv_op.ifm.shape) == 2
+ assert len(conv_op.ofm.shape) == 2
+ assert conv_op.ifm.shape == conv_op.ofm.shape
+
+ ifm.shape = [1, 8]
+ weights.shape = [8, 1]
+ ofm.shape = [1, 8]
+ op = testutil.create_op(Op.FullyConnected, [ifm, weights], ofm)
+ ifm.consumer_list.append(op)
+
+ prev_op = op.clone()
+ prev_op.ifm_shapes = op.ifm_shapes.copy()
+ prev_op.ofm_shapes = op.ofm_shapes.copy()
+
+ conv_op = convert_batched_fc_shape(op, None, None)
+
+ assert conv_op.ifm == prev_op.ifm
+ assert conv_op.ofm == prev_op.ofm
+ assert op.ifm_shapes[0] == prev_op.ifm_shapes[0]
+ assert op.ofm_shapes[0] == prev_op.ofm_shapes[0]
+ assert conv_op.type == Op.FullyConnected
+ assert len(conv_op.ifm.shape) == 2
+ assert len(conv_op.ofm.shape) == 2
assert conv_op.ifm.shape == conv_op.ofm.shape
@@ -118,3 +119,91 @@
assert op.attrs["explicit_padding"] == (2, 1, 1, 1)
assert op.ifm.shape == [1, 76, 75, 64]
assert pad_op not in op.ifm.ops
+
+
+def test_remove_reshape():
+ """
+ Tests that the expected reshape are removed in graph_optimisation
+ """
+
+ def setup_network():
+ quant = testutil.default_quant_params()
+ # create reshape1 op
+ ifm_shape = [64, 16]
+ reshape1_ofm_shape = [1, 4, 16, 16]
+ reshape1_ifm = create_const_tensor("reshape1_in", ifm_shape, DataType.uint8, np.zeros(ifm_shape))
+ reshape1_ifm.quantization = quant
+ 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("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
+
+ # 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()
+ weight_tens = Tensor([1, 1, 16, 16], DataType.uint8, "weights")
+ weight_tens.values = np.zeros(weight_tens.shape)
+ weight_tens.quant_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, [reshape1_ofm, weight_tens, bias_tens], conv_ofm, attrs=attrs, set_ifm_ofm_shapes=False
+ )
+ conv2d_op.run_on_npu = True
+
+ # create reshape3 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
+ nng = Graph()
+ sg = testutil.create_subgraph([reshape1_op, reshape2_op, conv2d_op, reshape3_op])
+ nng.subgraphs.append(sg)
+
+ return nng, reshape1_op, reshape2_op, conv2d_op, reshape3_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()
+ 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()
+ quant_zp32 = testutil.default_quant_params()
+ quant_zp32.zero_point = 32
+ reshape2_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