anwang2009 commented on a change in pull request #8952:
URL: https://github.com/apache/tvm/pull/8952#discussion_r715835129
##########
File path: python/tvm/relay/frontend/onnx.py
##########
@@ -3462,6 +3462,66 @@ def _impl_v10(cls, inputs, attr, params):
return _qnn.op.quantize(out, c_scale, c_zero_point, out_dtype=dtype)
+class QLinearMatMul(OnnxOpConverter):
+ """Operator converter for QLinearMatMul from Microsoft onnxruntime contrib
opset."""
+
+ @classmethod
+ def _impl_v10(cls, inputs, attr, params):
+ def get_scalar(x, dtype="float32"):
+ if isinstance(x, _expr.Var) and x.name_hint in params:
+ return _op.const(params[x.name_hint].numpy(), dtype)
+ rank = len(infer_shape(x))
+ assert rank <= 1, "QLinearMul scale and zero_point input must be
scalars"
+ if rank == 1:
+ x = _op.squeeze(x, [0])
+ return _op.cast(x, dtype)
+
+ import pdb
+ pdb.set_trace()
Review comment:
remove
##########
File path: python/tvm/relay/frontend/onnx.py
##########
@@ -3462,6 +3462,66 @@ def _impl_v10(cls, inputs, attr, params):
return _qnn.op.quantize(out, c_scale, c_zero_point, out_dtype=dtype)
+class QLinearMatMul(OnnxOpConverter):
+ """Operator converter for QLinearMatMul from Microsoft onnxruntime contrib
opset."""
+
+ @classmethod
+ def _impl_v10(cls, inputs, attr, params):
+ def get_scalar(x, dtype="float32"):
+ if isinstance(x, _expr.Var) and x.name_hint in params:
+ return _op.const(params[x.name_hint].numpy(), dtype)
+ rank = len(infer_shape(x))
+ assert rank <= 1, "QLinearMul scale and zero_point input must be
scalars"
+ if rank == 1:
+ x = _op.squeeze(x, [0])
+ return _op.cast(x, dtype)
+
+ import pdb
+ pdb.set_trace()
+
+ a = inputs[0]
+ a_scale = get_scalar(inputs[1])
+ a_zero_point = get_scalar(inputs[2], "int32")
+
+ b = inputs[3]
+ b_scale = get_scalar(inputs[4])
+ b_zero_point = get_scalar(inputs[5], "int32")
+
+ y_scale = fold_constant(get_scalar(inputs[6]))
+ y_zero_point = get_scalar(inputs[7], "int32")
+
+ dtype = infer_type(a).checked_type.dtype
+
+ a_rank = len(infer_shape(a))
+ b_rank = len(infer_shape(b))
+
+ assert (a_rank == 2) and (
+ b_rank == 2
+ ), "QLinearMatMul importer currently requires both 'a' and 'b' tensors
to be 2D, but rank(a)={}, rank(b)={}".format(
+ a_rank, b_rank
+ )
+
+ ## Note: The ONNX documentation for this op is fairly clear about
acceptable overflow
+ ## behavior during the matmul operation:
+ ## - The scalar multiplication ops MAY NOT overflow.
+ ## - The scalar addition ops, which sum the results of the scalar
multiplication,
+ ## MAY overflow, but if they do so, it must behave as one would
expect during
+ ## 32-bit integer-addition overflow.
+
+ ## As of this writing, Relay's nn.matmul operator doesn't expose a way
for us to
+ ## express these constraints. So to ensure correct behavior, we'll
play it safe by
+ ## converting the input tensors to int32 prior before performing
matmul.
+
+ a_int32 = _op.cast(a, "int32")
+ b_int32 = _op.cast(b, "int32")
+ matmul_int32 = _op.nn.matmul(a_int32, b_int32)
+
+ a = _qnn.op.dequantize(inputs[0], a_scale, a_zero_point)
+ b = _qnn.op.dequantize(inputs[3], b_scale, b_zero_point)
+ out =
Review comment:
finish thought here. Also, a and b assignments above are never read
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