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[Relay][Frontend][Onnx] Fix bug with non-fp32 gemm in onnx frontend. #8011

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May 11, 2021
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[Relay][Frontend][Onnx] Fix bug with non-fp32 gemm in onnx frontend. #8011

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5 changes: 3 additions & 2 deletions python/tvm/relay/frontend/onnx.py
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Original file line number Diff line number Diff line change
Expand Up @@ -618,6 +618,7 @@ def _impl_v1(cls, inputs, attr, params):
assert len(inputs) == 3 or len(inputs) == 2, "Gemm op take 2 or 3 inputs, {} given".format(
len(inputs)
)
dtype = infer_type(inputs[0]).checked_type.dtype
# Y = alpha * A * B + beta * C
alpha = float(attr.get("alpha", 1.0))
beta = float(attr.get("beta", 1.0))
Expand All @@ -631,10 +632,10 @@ def _impl_v1(cls, inputs, attr, params):
inputs[1] = _op.transpose(inputs[1], axes=(1, 0))
inputs[0] = _op.nn.batch_flatten(inputs[0])
if alpha != 1.0:
inputs[0] *= _expr.const(alpha)
inputs[0] *= _expr.const(alpha, dtype=dtype)
out = _op.nn.dense(inputs[0], inputs[1], units=channels)
if len(inputs) == 3:
out = out + _expr.const(beta) * inputs[2]
out = out + _expr.const(beta, dtype=dtype) * inputs[2]
return out


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20 changes: 11 additions & 9 deletions tests/python/frontend/onnx/test_forward.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1055,20 +1055,21 @@ def test_onehot():
tvm.testing.assert_allclose(out_np, tvm_out, rtol=1e-5, atol=1e-5)


def verify_gemm(a_shape, b_shape, c_shape=None, freeze_params=False):
def verify_gemm(a_shape, b_shape, c_shape=None, freeze_params=False, dtype="float32"):
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(dtype)
b_array = np.random.uniform(size=b_shape).astype(dtype)
input_names = ["a", "b"]
ONNX_DTYPE = mapping.NP_TYPE_TO_TENSOR_TYPE[np.dtype(dtype)]
input_nodes = [
helper.make_tensor_value_info("a", TensorProto.FLOAT, list(a_shape)),
helper.make_tensor_value_info("b", TensorProto.FLOAT, list(b_shape)),
helper.make_tensor_value_info("a", ONNX_DTYPE, list(a_shape)),
helper.make_tensor_value_info("b", ONNX_DTYPE, list(b_shape)),
]
input_values = [a_array, b_array]
if c_shape is not None:
c_array = np.random.uniform(size=c_shape).astype("float32")
c_array = np.random.uniform(size=c_shape).astype(dtype)
input_names.append("c")
input_nodes.append(helper.make_tensor_value_info("c", TensorProto.FLOAT, list(c_shape)))
input_nodes.append(helper.make_tensor_value_info("c", ONNX_DTYPE, list(c_shape)))
input_values.append(c_array)

gemm_node = helper.make_node("Gemm", input_names, ["out"])
Expand All @@ -1077,18 +1078,19 @@ def verify_gemm(a_shape, b_shape, c_shape=None, freeze_params=False):
[gemm_node],
"gemm_test",
inputs=input_nodes,
outputs=[helper.make_tensor_value_info("out", TensorProto.FLOAT, list(out_shape))],
outputs=[helper.make_tensor_value_info("out", ONNX_DTYPE, list(out_shape))],
)

model = helper.make_model(graph, producer_name="gemm_test")
verify_with_ort_with_inputs(model, input_values, freeze_params=freeze_params)
verify_with_ort_with_inputs(model, input_values, freeze_params=freeze_params, dtype=dtype)


@tvm.testing.uses_gpu
def test_gemm():
verify_gemm(a_shape=(4, 3), b_shape=(3, 4))
verify_gemm(a_shape=(4, 3), b_shape=(3, 4), c_shape=(4,))
verify_gemm(a_shape=(4, 3), b_shape=(3, 4), c_shape=(4,), freeze_params=True)
verify_gemm(a_shape=(4, 3), b_shape=(3, 4), c_shape=(4,), freeze_params=True, dtype="float16")


@tvm.testing.uses_gpu
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