diff --git a/.github/workflows/pre-commit.yml b/.github/workflows/pre-commit.yml
index 2fbb9265beb49644f08a2c6e916ab9c23d4bd339..20f5b48f7acc65ab18702ef2509e9791f919b825 100644
--- a/.github/workflows/pre-commit.yml
+++ b/.github/workflows/pre-commit.yml
@@ -13,10 +13,10 @@ jobs:
steps:
- name: Checkout
- uses: actions/checkout@v2
+ uses: actions/checkout@v3
- name: Setup Python
- uses: actions/setup-python@v2
+ uses: actions/setup-python@v3
- name: Run Lint
- uses: pre-commit/action@v2.0.0
+ uses: pre-commit/action@v3.0.0
diff --git a/.github/workflows/quicktest-dev-pr.yml b/.github/workflows/quicktest-dev-pr.yml
index d188007465cd27662ffadfb3ece0d8bf2e8e28be..ec92c84665d868b8a4376c82ecdf72395f1367a8 100644
--- a/.github/workflows/quicktest-dev-pr.yml
+++ b/.github/workflows/quicktest-dev-pr.yml
@@ -17,37 +17,9 @@ jobs:
- name: checkout
uses: actions/checkout@v2
- - name: set up Docker Buildx
- uses: docker/setup-buildx-action@v1
-
- - name: cache Docker layers
- uses: actions/cache@v2
- with:
- path: /tmp/.buildx-cache
- key: ${{ runner.os }}-buildx-${{ github.sha }}
- restore-keys: |
- ${{ runner.os }}-buildx-
-
- - name: Build and push
- uses: docker/build-push-action@v2
- with:
- file: docker/Dockerfile.finn
- context: .
- push: false
- load: true
- tags: finn_gha
- cache-from: type=local,src=/tmp/.buildx-cache
- cache-to: type=local,dest=/tmp/.buildx-cache-new
- -
- # Temp fix
- # https://github.com/docker/build-push-action/issues/252
- # https://github.com/moby/buildkit/issues/1896
- name: Move cache
- run: |
- rm -rf /tmp/.buildx-cache
- mv /tmp/.buildx-cache-new /tmp/.buildx-cache
-
-
- name: DockerRunQuicktest
run: |
- docker run --init --hostname finn_gha -w $(pwd) -v $(pwd):$(pwd) -e FINN_BUILD_DIR=/tmp/finn_gha -e FINN_INST_NAME=finn_gha finn_gha quicktest.sh
+ export FINN_ROOT=$(pwd)
+ export FINN_BUILD_DIR=/tmp/finn_gha
+ export FINN_INST_NAME=finn_gha
+ ./run-docker.sh quicktest
diff --git a/docker/quicktest.sh b/docker/quicktest.sh
index f625f2b1ef722f386180a8409a9eb9e759a2f3b6..b4ad37232fa69754a86e9064d7592d7474e8617e 100755
--- a/docker/quicktest.sh
+++ b/docker/quicktest.sh
@@ -2,7 +2,7 @@
: ${PYTEST_PARALLEL=auto}
-cd $FINN_ROOT/finn
+cd $FINN_ROOT
# check if command line argument is empty or not present
if [ -z $1 ]; then
echo "Running quicktest: not (vivado or slow or board) with pytest-xdist"
diff --git a/src/finn/builder/build_dataflow.py b/src/finn/builder/build_dataflow.py
index 238083f653d410772a81115ff12dd987835d1f32..d6864994a70a0ea4c24567155ff7c0599bc0fb6f 100644
--- a/src/finn/builder/build_dataflow.py
+++ b/src/finn/builder/build_dataflow.py
@@ -155,12 +155,14 @@ def build_dataflow_cfg(model_filename, cfg: DataflowBuildConfig):
% (step_name, step_num, len(build_dataflow_steps))
)
# redirect output to logfile
- sys.stdout = stdout_logger
- sys.stderr = stderr_logger
- print(
- "Running step: %s [%d/%d]"
- % (step_name, step_num, len(build_dataflow_steps))
- )
+ if not cfg.verbose:
+ sys.stdout = stdout_logger
+ sys.stderr = stderr_logger
+ # also log current step name to logfile
+ print(
+ "Running step: %s [%d/%d]"
+ % (step_name, step_num, len(build_dataflow_steps))
+ )
# run the step
step_start = time.time()
model = transform_step(model, cfg)
diff --git a/src/finn/builder/build_dataflow_config.py b/src/finn/builder/build_dataflow_config.py
index 09e9ec3a564dc2b459cd1ea3205e541f922b1af0..92263bd82ce291833c6868847876ac7e3b68e6f8 100644
--- a/src/finn/builder/build_dataflow_config.py
+++ b/src/finn/builder/build_dataflow_config.py
@@ -285,6 +285,10 @@ class DataflowBuildConfig:
#: Whether pdb postmortem debuggig will be launched when the build fails
enable_build_pdb_debug: Optional[bool] = True
+ #: When True, all warnings and compiler output will be printed in stdout.
+ #: Otherwise, these will be suppressed and only appear in the build log.
+ verbose: Optional[bool] = False
+
#: If given, only run the steps in the list. If not, run default steps.
#: See `default_build_dataflow_steps` for the default list of steps.
#: When specified:
diff --git a/src/finn/transformation/qonnx/fold_quant_weights.py b/src/finn/transformation/qonnx/fold_quant_weights.py
index 80b6042d03ea11a45493011288133ed3a6f57c8d..e8339ae24472fa238e5c5da176b1316611218a54 100644
--- a/src/finn/transformation/qonnx/fold_quant_weights.py
+++ b/src/finn/transformation/qonnx/fold_quant_weights.py
@@ -126,10 +126,20 @@ class FoldQuantWeights(Transformation):
model.set_tensor_datatype(node_out, new_dtype)
# Reshape scale for Conv if required
+ target_output_shape = model.get_tensor_shape(
+ target_node.output[0]
+ )
if target_node.op_type == "Conv" and len(scale.shape) > 0:
- bias_shape = [1] * len(scale.shape)
- bias_shape[1] = -1
- scale = scale.reshape(bias_shape)
+ conv_out_shape = [1] * len(target_output_shape)
+ # only support per-output channel scaling
+ # (i.e. all scale shape elems besides 0th must be 1s)
+ if len(scale.shape) > 1:
+ assert (
+ np.prod(scale.shape[1:]) == 1
+ ), "Can't fold scale beyond per-out-channel granularity"
+ # collect all scaling in channels dim (since we constrain)
+ conv_out_shape[1] = -1
+ scale = scale.reshape(conv_out_shape)
if scale.shape == (1,):
scale = scale[0]
diff --git a/src/finn/transformation/qonnx/quant_act_to_multithreshold.py b/src/finn/transformation/qonnx/quant_act_to_multithreshold.py
index c52d69b0f09d306c5b076bb6ef1775f38977241a..77025ecdf57d5a422992d4163d05c740454986bb 100644
--- a/src/finn/transformation/qonnx/quant_act_to_multithreshold.py
+++ b/src/finn/transformation/qonnx/quant_act_to_multithreshold.py
@@ -110,11 +110,6 @@ class ConvertQuantActToMultiThreshold(Transformation):
predecessor_op_type = predecessor[0].op_type
else:
predecessor_op_type = predecessor
- if model.is_fork_node(n):
- raise ValueError(
- "Forking Quant/BipolarQuant nodes are currently "
- "not supported by FINN."
- )
if n.op_type == "Quant" and not model.get_initializer(n.input[2]) == 0:
raise ValueError(
"Only Quant nodes with zero-point == 0 are currently supported."
diff --git a/src/finn/transformation/streamline/absorb.py b/src/finn/transformation/streamline/absorb.py
index 0299c4f4d89d1fdd94434db77c77a0e529c86d26..a983e67750a0a860eeeb4b429f7d6b181fc84fe3 100644
--- a/src/finn/transformation/streamline/absorb.py
+++ b/src/finn/transformation/streamline/absorb.py
@@ -473,7 +473,7 @@ class AbsorbConsecutiveTransposes(Transformation):
"""Remove (Transpose -> Transpose) patterns when the input and output
of the pattern have the same layout."""
- def Are_opposite_permutations(self, perms1, perms2):
+ def are_opposite_permutations(self, perms1, perms2):
if len(perms1) != len(perms2):
return False
assert 0 <= max(perms2) < len(perms2), "invalid permutation"
@@ -488,72 +488,40 @@ class AbsorbConsecutiveTransposes(Transformation):
def apply(self, model):
graph = model.graph
graph_modified = False
- for n in graph.node:
- if n.op_type == "Transpose":
- if model.is_fork_node(n):
- next_nodes = model.find_direct_successors(n)
- perms1 = list(get_by_name(n.attribute, "perm").ints)
-
- # check if all nodes after fork are opposite transposes
- all_opposite_transposes = True
- for next_node in next_nodes:
- if next_node is not None and next_node.op_type == "Transpose":
- perms2 = list(get_by_name(next_node.attribute, "perm").ints)
- if not self.Are_opposite_permutations(perms1, perms2):
- all_opposite_transposes = False
- break
- else:
- all_opposite_transposes = False
- break
-
- if not all_opposite_transposes:
- continue
-
- prod = model.find_producer(n.input[0])
- for next_node in next_nodes:
- # connect next_node's consumer input to n's producer output
- # TODO implement this to allow for forks as producers and
- # joins as consumers
- cons = model.find_consumer(next_node.output[0])
- cons.input[0] = prod.output[0]
-
- # remove consumer transpose
- graph.node.remove(next_node)
-
- # remove producer transpose
- graph.node.remove(n)
- graph_modified = True
-
- else:
- next_node = model.find_consumer(n.output[0])
+ for node in graph.node:
+ if node.op_type == "Transpose":
+ next_nodes = model.find_consumers(node.output[0])
+ perms1 = list(get_by_name(node.attribute, "perm").ints)
+ # check if all nodes after fork are opposite transposes
+ all_opposite_transposes = True
+ for next_node in next_nodes:
if next_node is not None and next_node.op_type == "Transpose":
- perms1 = list(get_by_name(n.attribute, "perm").ints)
perms2 = list(get_by_name(next_node.attribute, "perm").ints)
- if self.Are_opposite_permutations(perms1, perms2):
-
- # connect next_node's consumer input to n's producer output
- # TODO implement this to allow for forks as producers
- consumers = model.find_direct_successors(next_node)
- prod = model.find_producer(n.input[0])
- if prod is not None:
- for cons in consumers:
- for cons_in in cons.input:
- if cons_in == next_node.output[0]:
- prod.output[0] = cons_in
- break
- else:
- # n.input[0] is top-level graph input
- # wire consumers directly to that
- for cons in consumers:
- for i, iname in enumerate(cons.input):
- if iname == next_node.output[0]:
- cons.input[i] = n.input[0]
-
- # remove both transposes
- graph.node.remove(n)
- graph.node.remove(next_node)
+ if not self.are_opposite_permutations(perms1, perms2):
+ all_opposite_transposes = False
+ break
+ else:
+ all_opposite_transposes = False
+ break
+ if not all_opposite_transposes:
+ continue
+ source_tensor = node.input[0]
+ for next_node in next_nodes:
+ # connect next_node's consumers' appropriate input to n's input
+ # TODO how to handle top-level outputs if any?
+ nextnode_out = next_node.output[0]
+ assert nextnode_out not in [x.name for x in model.graph.output]
+ consumers = model.find_consumers(nextnode_out)
+ for cons in consumers:
+ for i, iname in enumerate(cons.input):
+ if iname == nextnode_out:
+ cons.input[i] = source_tensor
+ # remove consumer transpose
+ graph.node.remove(next_node)
+ # remove producer transpose
+ graph.node.remove(node)
+ graph_modified = True
- graph_modified = True
if graph_modified:
model = model.transform(InferDataTypes())
return (model, graph_modified)
diff --git a/src/finn/transformation/streamline/reorder.py b/src/finn/transformation/streamline/reorder.py
index 9ff8a2173ce81e2a19c56bbd20a326759c3b9df2..3e815c1537353cc2be970a2068d4ded30cc48bc8 100644
--- a/src/finn/transformation/streamline/reorder.py
+++ b/src/finn/transformation/streamline/reorder.py
@@ -553,6 +553,8 @@ class MoveLinearPastEltwiseAdd(Transformation):
# Other transform should handle that
if prod0 is None or prod1 is None or (prod0 == prod1):
continue
+ if len(prod0.input) < 2 or len(prod1.input) < 2:
+ continue
init0 = model.get_initializer(prod0.input[1])
init1 = model.get_initializer(prod1.input[1])
# if either initializer is None, skip
@@ -728,9 +730,10 @@ class MoveOpPastFork(Transformation):
can be merged with nodes in the branches
"""
- def __init__(self, op_name_list):
+ def __init__(self, op_name_list, get_attrs_fxn=lambda x: {}):
super().__init__()
self.ops_to_move = op_name_list
+ self.get_attrs_fxn = get_attrs_fxn
def apply(self, model):
graph = model.graph
@@ -747,9 +750,10 @@ class MoveOpPastFork(Transformation):
# Restrict this transform to operations with constant parameters
# Assuming parameters is in input 1
- op_init_param = model.get_initializer(n.input[1])
- if op_init_param is None:
- continue
+ if len(n.input) > 1:
+ op_init_param = model.get_initializer(n.input[1])
+ else:
+ op_init_param = None
# Check case when branches are empty and go
# to the same node
@@ -766,16 +770,20 @@ class MoveOpPastFork(Transformation):
for consumer_node in consumers[1:]:
# create new node
- new_param_name = model.make_new_valueinfo_name()
new_output_tensor_name = model.make_new_valueinfo_name()
+ if op_init_param is None:
+ new_inp_list = [n.input[0]]
+ else:
+ new_param_name = model.make_new_valueinfo_name()
+ new_inp_list = [n.input[0], new_param_name]
+ model.set_initializer(new_param_name, op_init_param)
+ attrs = self.get_attrs_fxn(n)
+ # TODO use copy of original node instead to get attrs?
new_node = oh.make_node(
- n.op_type,
- [n.input[0], new_param_name],
- [new_output_tensor_name],
+ n.op_type, new_inp_list, [new_output_tensor_name], **attrs
)
graph.node.insert(node_ind, new_node)
node_ind += 1
- model.set_initializer(new_param_name, op_init_param)
# change consumer input tensor
graph.node.remove(consumer_node)
@@ -811,6 +819,13 @@ class MoveLinearPastFork(MoveOpPastFork):
super().__init__(["Add", "Mul"])
+class MoveTransposePastFork(MoveOpPastFork):
+ def __init__(self):
+ super().__init__(
+ ["Transpose"], lambda x: {"perm": get_by_name(x.attribute, "perm").ints}
+ )
+
+
class MoveMaxPoolPastMultiThreshold(Transformation):
"""Move MaxPool nodes past MultiThreshold nodes on linear segments of the graph."""
diff --git a/tests/brevitas/test_brevitas_relu_act_export.py b/tests/brevitas/test_brevitas_relu_act_export.py
index b0c3d6088c27291f1f49dd2f1ee746b65ca0a737..3dc46ec31e49d7115b19b3373d54be6ddc29bb80 100644
--- a/tests/brevitas/test_brevitas_relu_act_export.py
+++ b/tests/brevitas/test_brevitas_relu_act_export.py
@@ -41,6 +41,7 @@ from brevitas.nn import QuantReLU
from qonnx.core.modelwrapper import ModelWrapper
from qonnx.transformation.infer_shapes import InferShapes
from qonnx.util.cleanup import cleanup as qonnx_cleanup
+from torch import nn
import finn.core.onnx_exec as oxe
from finn.transformation.qonnx.convert_qonnx_to_finn import ConvertQONNXtoFINN
@@ -179,3 +180,83 @@ scaling_impl.learned_value": rand_tensor.type(
assert np.isclose(produced, expected, atol=1e-3).all()
os.remove(export_onnx_path)
+
+
+class PyTorchTestModel(nn.Module):
+ def __init__(self, abits):
+ super(PyTorchTestModel, self).__init__()
+ out_channels = 32
+ self.b_act = QuantReLU(
+ bit_width=abits,
+ quant_type=QuantType.INT,
+ scaling_impl_type=ScalingImplType.PARAMETER,
+ scaling_per_channel=True,
+ restrict_scaling_type=RestrictValueType.LOG_FP,
+ scaling_min_val=2e-16,
+ max_val=6.0,
+ return_quant_tensor=False,
+ per_channel_broadcastable_shape=(1, out_channels, 1, 1),
+ )
+
+ def forward(self, x):
+ act_out = self.b_act(x)
+ y0 = act_out * 2.0
+ y1 = act_out * -1.0
+ y = y0 + y1
+ return y
+
+
+@pytest.mark.brevitas_export
+@pytest.mark.parametrize("abits", [2, 4, 8])
+@pytest.mark.parametrize("max_val", [1.0, 1.5, 1 - 2 ** (-7)])
+@pytest.mark.parametrize("scaling_per_channel", [True])
+@pytest.mark.parametrize("QONNX_export", [True])
+def test_brevitas_act_export_relu_forking(
+ abits, max_val, scaling_per_channel, QONNX_export
+):
+ out_channels = 32
+ ishape = (1, out_channels, 1, 1)
+ min_val = -1.0
+ model_pyt = PyTorchTestModel(abits)
+
+ rand_tensor = (2) * torch.rand((1, out_channels, 1, 1))
+
+ checkpoint = {
+ "b_act.act_quant_proxy.fused_activation_quant_proxy."
+ "tensor_quant.scaling_impl.learned_value": rand_tensor.type(torch.FloatTensor)
+ }
+ model_pyt.load_state_dict(checkpoint)
+
+ if QONNX_export:
+ m_path = export_onnx_path
+ BrevitasONNXManager.export(model_pyt, ishape, m_path)
+ qonnx_cleanup(m_path, out_file=m_path)
+ model = ModelWrapper(m_path)
+ model = model.transform(ConvertQONNXtoFINN())
+ model.save(m_path)
+
+ model = ModelWrapper(export_onnx_path)
+ model = model.transform(InferShapes())
+ inp_tensor = np.random.uniform(low=min_val, high=max_val, size=ishape).astype(
+ np.float32
+ )
+ idict = {model.graph.input[0].name: inp_tensor}
+ odict = oxe.execute_onnx(model, idict, True)
+ produced = odict[model.graph.output[0].name]
+ inp_tensor = torch.from_numpy(inp_tensor).float()
+ model_pyt.eval()
+ expected = model_pyt.forward(inp_tensor).detach().numpy()
+ if not np.isclose(produced, expected, atol=1e-3).all():
+ print(abits, max_val)
+ print("scale: ", model_pyt.quant_act_scale().type(torch.FloatTensor).detach())
+ if abits < 5:
+ print(
+ "thres:",
+ ", ".join(["{:8.4f}".format(x) for x in model_pyt.export_thres[0]]),
+ )
+ print("input:", ", ".join(["{:8.4f}".format(x) for x in inp_tensor[0]]))
+ print("prod :", ", ".join(["{:8.4f}".format(x) for x in produced[0]]))
+ print("expec:", ", ".join(["{:8.4f}".format(x) for x in expected[0]]))
+
+ assert np.isclose(produced, expected, atol=1e-3).all()
+ os.remove(export_onnx_path)
diff --git a/tests/fpgadataflow/test_fpgadataflow_checksum.py b/tests/fpgadataflow/test_fpgadataflow_checksum.py
index 5e79ea2dad2aa4200f998fd8953672b9f49b2b86..495fcd10b6a977c6b0917ac37b58ec5595185c25 100644
--- a/tests/fpgadataflow/test_fpgadataflow_checksum.py
+++ b/tests/fpgadataflow/test_fpgadataflow_checksum.py
@@ -133,6 +133,7 @@ def create_two_fc_model():
return model
+@pytest.mark.vivado
@pytest.mark.fpgadataflow
def test_fpgadataflow_checksum():
# use a graph consisting of two fc layers to test
diff --git a/tests/fpgadataflow/test_fpgadataflow_concat.py b/tests/fpgadataflow/test_fpgadataflow_concat.py
index dddc470ec2ed88faf078f19bd0d2a7a4a6b5b6cd..8488a34dff52d39c28fbea25275c9a4b59c37f80 100644
--- a/tests/fpgadataflow/test_fpgadataflow_concat.py
+++ b/tests/fpgadataflow/test_fpgadataflow_concat.py
@@ -144,6 +144,5 @@ def test_fpgadataflow_concat_stitchedip():
)
model.set_metadata_prop("exec_mode", "rtlsim")
model.set_metadata_prop("rtlsim_trace", "trace.vcd")
- model.save("dbg.onnx")
ret_sim = execute_onnx(model, inp_dict)
assert (exp_out == ret_sim[oname]).all()
diff --git a/tests/transformation/streamline/test_absorb_opposite_transposes.py b/tests/transformation/streamline/test_absorb_opposite_transposes.py
index 51ea5edfc420bf935de3e196df1b150934782a91..6d8d2b9f0cd4ad28c3ea0922d69b9b963a0deb08 100644
--- a/tests/transformation/streamline/test_absorb_opposite_transposes.py
+++ b/tests/transformation/streamline/test_absorb_opposite_transposes.py
@@ -29,8 +29,7 @@
import pytest
import numpy as np
-import onnx.helper as oh
-from onnx import TensorProto
+import onnx.parser as oprs
from qonnx.core.modelwrapper import ModelWrapper
from qonnx.transformation.infer_shapes import InferShapes
@@ -41,39 +40,42 @@ from finn.transformation.streamline.absorb import AbsorbConsecutiveTransposes
@pytest.mark.streamline
def test_absorb_opposite_transposes():
np.random.seed(0)
- input_shape = [1, 3, 4, 2]
- top_in = oh.make_tensor_value_info("top_in", TensorProto.FLOAT, input_shape)
- top_out = oh.make_tensor_value_info("top_out", TensorProto.FLOAT, input_shape)
- value_info = [oh.make_tensor_value_info("add_param_0", TensorProto.FLOAT, [1])]
- value_info += [oh.make_tensor_value_info("add_param_1", TensorProto.FLOAT, [1])]
- value_info += [oh.make_tensor_value_info("mul_param_0", TensorProto.FLOAT, [1])]
- modelproto = oh.make_model(
- oh.make_graph(
- name="test",
- inputs=[top_in],
- outputs=[top_out],
- value_info=value_info,
- nodes=[
- oh.make_node("Add", ["top_in", "add_param_0"], ["t0"]),
- oh.make_node("Transpose", ["t0"], ["t1"], perm=[0, 2, 3, 1]),
- oh.make_node("Transpose", ["t1"], ["t2"], perm=[0, 3, 1, 2]),
- oh.make_node("Add", ["t2", "add_param_1"], ["t3"]),
- oh.make_node("Transpose", ["t3"], ["t4"], perm=[0, 2, 3, 1]),
- oh.make_node("Transpose", ["t4"], ["t5"], perm=[0, 3, 1, 2]),
- oh.make_node("Add", ["t5", "t2"], ["t6"]),
- oh.make_node("Mul", ["t6", "mul_param_0"], ["top_out"]),
- ],
- )
- )
- model = ModelWrapper(modelproto)
+ shp = [1, 3, 4, 2]
+ shp_str = str(shp)
+ input = f"""
+ <
+ ir_version: 7,
+ opset_import: ["" : 9]
+ >
+ agraph (float{shp_str} in0) => (float{shp_str} out0)
+ <
+ float[1] add0_param = {{1.0}},
+ float[1] add1_param = {{3.0}},
+ float[1] mul0_param = {{2.0}}
+ >
+ {{
+ add0_out = Add(in0, add0_param)
+ t0_out = Transpose<perm=[0,2,3,1]>(add0_out)
+ t1_out = Transpose<perm=[0,3,1,2]>(t0_out)
+ add1_out = Add(t1_out, add1_param)
+ t2_out = Transpose<perm=[0,2,3,1]>(add1_out)
+ t3_out = Transpose<perm=[0,3,1,2]>(t2_out)
+ add2_out = Add(t1_out, t3_out)
+ t4_out = Transpose<perm=[0,2,3,1]>(add2_out)
+ t5_out = Transpose<perm=[0,3,1,2]>(t4_out)
+ t6_out = Transpose<perm=[0,3,1,2]>(t4_out)
+ m0_out = Mul(t5_out, mul0_param)
+ m1_out = Mul(t6_out, mul0_param)
+ out0 = Mul(m0_out, m1_out)
+ }}
+ """
+ model = oprs.parse_model(input)
+ model = ModelWrapper(model)
model = model.transform(InferShapes())
- model.set_initializer("add_param_0", np.asarray([1], dtype=np.float32))
- model.set_initializer("add_param_1", np.asarray([3], dtype=np.float32))
- model.set_initializer("mul_param_0", np.asarray([2], dtype=np.float32))
new_model = model.transform(AbsorbConsecutiveTransposes())
new_model = new_model.transform(InferShapes())
- inp_dict = {"top_in": np.random.rand(*input_shape).astype(np.float32)}
+ inp_dict = {"top_in": np.random.rand(*shp).astype(np.float32)}
assert ox.compare_execution(model, model, inp_dict)
- assert len(new_model.graph.node) == 4
+ assert len(new_model.graph.node) == 6
for n in new_model.graph.node:
assert new_model.graph.node[0].op_type != "Transpose"
diff --git a/tests/transformation/streamline/test_move_past_fork.py b/tests/transformation/streamline/test_move_past_fork.py
index 5064fa3fca869a245c87cf0c1680d1357e5de60b..7e77d7f9b3502429f08c40558e330b6261d0dbad 100644
--- a/tests/transformation/streamline/test_move_past_fork.py
+++ b/tests/transformation/streamline/test_move_past_fork.py
@@ -28,80 +28,113 @@
import pytest
import numpy as np
-from onnx import TensorProto, helper
+import onnx.parser as oprs
from qonnx.core.modelwrapper import ModelWrapper
+from qonnx.transformation.general import GiveUniqueNodeNames
from qonnx.transformation.infer_shapes import InferShapes
+from qonnx.util.basic import get_by_name
import finn.core.onnx_exec as oxe
-from finn.transformation.streamline.reorder import MoveLinearPastFork
+from finn.transformation.streamline.reorder import (
+ MoveLinearPastFork,
+ MoveTransposePastFork,
+)
+
+
+@pytest.mark.streamline
+def test_move_past_fork_transpose():
+ shp = [1, 3, 32, 32]
+ shp_str = str(shp)
+ input = f"""
+ <
+ ir_version: 7,
+ opset_import: ["" : 9]
+ >
+ agraph (float{shp_str} in0) => (float{shp_str} out0)
+ {{
+ t0_out = Transpose<perm=[0,2,3,1]>(in0)
+ t1_out = Transpose<perm=[0,3,1,2]>(t0_out)
+ t2_out = Transpose<perm=[0,3,1,2]>(t0_out)
+ out0 = Add(t1_out, t2_out)
+ }}
+ """
+ model = oprs.parse_model(input)
+ model = ModelWrapper(model)
+ model = model.transform(InferShapes())
+ new_model = model.transform(MoveTransposePastFork())
+ new_model = new_model.transform(GiveUniqueNodeNames())
+ nodes = new_model.graph.node
+ assert oxe.compare_execution(
+ model, new_model, {"in0": np.random.rand(*shp).astype(np.float32)}
+ )
+ assert len(nodes) == 5
+ assert not new_model.is_fork_node(get_by_name(nodes, "Transpose_0"))
@pytest.mark.streamline
@pytest.mark.parametrize("ch", [64, 1])
# ifmdim
@pytest.mark.parametrize("ifmdim", [-1, 7])
-def test_move_past_fork(ch, ifmdim):
- # generate test vectors of correct shape
+def test_move_past_fork_linear(ch, ifmdim):
if ifmdim == -1:
- input_shape = (1, ch)
+ shp = [1, ch]
else:
- input_shape = (1, ch, ifmdim, ifmdim)
+ shp = [1, ch, ifmdim, ifmdim]
+ shp_str = str(shp)
+ input = f"""
+ <
+ ir_version: 7,
+ opset_import: ["" : 9]
+ >
+ agraph (float{shp_str} in0) => (float{shp_str} out0)
+ <
+ float{shp_str} add0_param,
+ float{shp_str} mul_shared_param,
+ float{shp_str} add2_param,
+ float{shp_str} mul2_param,
+ float{shp_str} add3_param,
+ float{shp_str} add4_param,
+ float{shp_str} mul3_param,
+ float{shp_str} add6_param
+ >
+ {{
- top_in = helper.make_tensor_value_info("top_in", TensorProto.FLOAT, input_shape)
- top_out = helper.make_tensor_value_info("top_out", TensorProto.FLOAT, input_shape)
-
- num_of_params = 8
- value_info = []
- for i in range(num_of_params):
- value_info += [
- helper.make_tensor_value_info("p" + str(i), TensorProto.FLOAT, input_shape)
- ]
-
- add_1_to_move = helper.make_node("Add", ["top_in", "p0"], ["fork1"])
- mul_1_to_move = helper.make_node("Mul", ["t5", "p4"], ["fork2"])
- add_2_to_move = helper.make_node("Add", ["fork2", "p5"], ["t6"])
- mul_1_not_to_move = helper.make_node("Mul", ["t8", "p7"], ["fork3"])
- modelproto = helper.make_model(
- helper.make_graph(
- name="test",
- inputs=[top_in],
- outputs=[top_out],
- value_info=value_info,
- nodes=[
- # fork1
- add_1_to_move,
- helper.make_node("Mul", ["fork1", "p1"], ["t2"]),
- helper.make_node("Mul", ["fork1", "p2"], ["t3"]),
- helper.make_node("Add", ["t2", "t3"], ["t4"]),
- helper.make_node("Add", ["t4", "p3"], ["t5"]),
- # fork2
- mul_1_to_move,
- add_2_to_move,
- helper.make_node("Add", ["fork2", "p6"], ["t7"]),
- helper.make_node("Add", ["t6", "t7"], ["t8"]),
- # empty branches: do nothing
- mul_1_not_to_move,
- helper.make_node("Add", ["fork3", "fork3"], ["top_out"]),
- ],
- )
- )
- model = ModelWrapper(modelproto)
+ add0_out = Add(in0, add0_param)
+ mul0_out = Mul(add0_out, mul_shared_param)
+ mul1_out = Mul(add0_out, mul_shared_param)
+ add1_out = Add(mul0_out, mul1_out)
+ add2_out = Add(add1_out, add2_param)
+ mul2_out = Mul(add2_out, mul2_param)
+ add3_out = Add(mul2_out, add3_param)
+ add4_out = Add(mul2_out, add4_param)
+ add5_out = Add(add3_out, add4_out)
+ mul3_out = Mul(add5_out, mul3_param)
+ out0 = Add(mul3_out, add6_param)
+ }}
+ """
+ model = oprs.parse_model(input)
+ model = ModelWrapper(model)
model = model.transform(InferShapes())
np.random.seed(0)
- for i in range(num_of_params):
- model.set_initializer(
- "p" + str(i), np.random.rand(*input_shape).astype(np.float32)
- )
-
+ for tensor_name in model.get_all_tensor_names():
+ if tensor_name.endswith("_param"):
+ pshape = model.get_tensor_shape(tensor_name)
+ model.set_initializer(
+ tensor_name, np.random.rand(*pshape).astype(np.float32)
+ )
+ model = model.transform(GiveUniqueNodeNames())
# Transform
new_model = model.transform(MoveLinearPastFork())
- inp_dict = {"top_in": np.random.rand(*input_shape).astype(np.float32)}
-
+ new_model = new_model.transform(GiveUniqueNodeNames())
+ inp_dict = {"top_in": np.random.rand(*shp).astype(np.float32)}
# Test
assert oxe.compare_execution(model, new_model, inp_dict)
- assert not new_model.is_fork_node(add_1_to_move)
- assert not new_model.is_fork_node(mul_1_to_move)
- assert not new_model.is_fork_node(add_2_to_move)
- assert new_model.is_fork_node(mul_1_not_to_move)
+ nodes = new_model.graph.node
+ assert len(new_model.get_nodes_by_op_type("Add")) == 9
+ assert len(new_model.get_nodes_by_op_type("Mul")) == 5
+ assert not new_model.is_fork_node(get_by_name(nodes, "Add_0"))
+ assert new_model.is_join_node(get_by_name(nodes, "Add_2"))
+ assert not new_model.is_fork_node(get_by_name(nodes, "Mul_2"))
+ assert not new_model.is_join_node(get_by_name(nodes, "Add_5"))
assert len(new_model.graph.node) == 14
diff --git a/tests/transformation/test_qonnx_to_finn.py b/tests/transformation/test_qonnx_to_finn.py
index 43055f6704732866569ac4770202f1b4ff6bfb22..7e438b4b8ba9d9befca79100bb9727735afa27d3 100644
--- a/tests/transformation/test_qonnx_to_finn.py
+++ b/tests/transformation/test_qonnx_to_finn.py
@@ -94,6 +94,9 @@ def analysis_testing_for_no_quant_nodes(model):
@pytest.mark.parametrize("wbits", [1, 2])
@pytest.mark.parametrize("model_name", ["TFC", "SFC", "LFC", "CNV", "mobilenet"])
def test_QONNX_to_FINN(model_name, wbits, abits):
+ if model_name == "mobilenet":
+ pytest.xfail("MobileNet test is temporarily excluded from QONNX testing.")
+
if wbits > abits:
pytest.skip("No wbits > abits cases at the moment")
if model_name == "LFC" and wbits == 2 and abits == 2: