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[fix] vec * mat in matmul in onnx converter #11174

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merged 5 commits into from
Apr 29, 2022
Merged

[fix] vec * mat in matmul in onnx converter #11174

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d626365
fix: vec * mat in matmul in onnx converter
ganler Apr 28, 2022
7485282
fix: pylint
ganler Apr 28, 2022
2566947
fix: vec-mat matmul
ganler Apr 28, 2022
14f033c
fix test
ganler Apr 29, 2022
ab32da8
fix test
ganler Apr 29, 2022
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4 changes: 4 additions & 0 deletions python/tvm/relay/frontend/onnx.py
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Original file line number Diff line number Diff line change
Expand Up @@ -324,6 +324,10 @@ def flatten_to_nd(x, x_shape, nd=3):
0,
)
return _op.reshape(output, fold_constant(final_shape))

if a_rank == 1:
return _op.multiply(inputs[0], inputs[1])
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# Otherwise a simple dense op will get the job done.
input_1_t = _op.transpose(inputs[1], axes=(1, 0))
return _op.nn.dense(inputs[0], input_1_t, out_dtype=out_dtype)
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36 changes: 19 additions & 17 deletions tests/python/frontend/onnx/test_forward.py
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Expand Up @@ -1213,27 +1213,29 @@ def verify_gemm(a_shape, b_shape, c_shape=None, freeze_params=False, dtype="floa

@tvm.testing.parametrize_targets
def test_matmul(target, dev):
a_shape = (4, 3)
b_shape = (3, 4)
out_shape = [a_shape[0], b_shape[1]]
def test_one_matmul(a_shape, b_shape):
out_shape = [a_shape[0], b_shape[1]]

a_array = np.random.uniform(size=a_shape).astype("float32")
b_array = np.random.uniform(size=b_shape).astype("float32")
a_array = np.random.uniform(size=a_shape).astype("float32")
b_array = np.random.uniform(size=b_shape).astype("float32")

mul_node = helper.make_node("MatMul", ["a", "b"], ["out"])
mul_node = helper.make_node("MatMul", ["a", "b"], ["out"])

graph = helper.make_graph(
[mul_node],
"matmul_test",
inputs=[
helper.make_tensor_value_info("a", TensorProto.FLOAT, list(a_shape)),
helper.make_tensor_value_info("b", TensorProto.FLOAT, list(b_shape)),
],
outputs=[helper.make_tensor_value_info("out", TensorProto.FLOAT, list(out_shape))],
)
graph = helper.make_graph(
[mul_node],
"matmul_test",
inputs=[
helper.make_tensor_value_info("a", TensorProto.FLOAT, list(a_shape)),
helper.make_tensor_value_info("b", TensorProto.FLOAT, list(b_shape)),
],
outputs=[helper.make_tensor_value_info("out", TensorProto.FLOAT, list(out_shape))],
)

model = helper.make_model(graph, producer_name="matmul_test")
verify_with_ort_with_inputs(model, [a_array, b_array], target=target, dev=dev)
model = helper.make_model(graph, producer_name="matmul_test")
verify_with_ort_with_inputs(model, [a_array, b_array], target=target, dev=dev)

test_one_matmul((4, 3), (3, 4))
test_one_matmul((3), (3, 1))


@tvm.testing.parametrize_targets
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