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

1 files changed, 110 insertions, 21 deletions

diff --git a/ethosu/vela/test/test_graph_optimiser.py b/ethosu/vela/test/test_graph_optimiser.py
index b3938bcc..b01b07c3 100644
--- a/ethosu/vela/test/test_graph_optimiser.py
+++ b/ethosu/vela/test/test_graph_optimiser.py
@@ -20,10 +20,12 @@ import numpy as np
 
 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 @@ from ethosu.vela.test import testutil
 
 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
+    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 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) == 4
+    assert len(conv_op.ifm.shape) == 2
+    assert len(conv_op.ofm.shape) == 2
     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
+    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)
 
-    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] == 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 @@ def test_optimise_pad():
     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