gemini-code-assist[bot] commented on code in PR #19993:
URL: https://github.com/apache/tvm/pull/19993#discussion_r3569127368
##########
python/tvm/relax/frontend/onnx/onnx_frontend.py:
##########
@@ -380,6 +452,47 @@ def _impl_v13(cls, bb, inputs, attr, params):
zp = relax.const(0, x.ty.dtype.dtype)
return relax.op.dequantize(x, scale, zp, axis=axis,
out_dtype="float32")
+ @classmethod
+ def _impl_v19(cls, bb, inputs, attr, params):
+ x, scale = inputs[0], inputs[1]
+ zp = inputs[2] if len(inputs) > 2 and inputs[2] is not None else None
+ axis = attr.get("axis", 1)
+ if hasattr(x.ty, "ndim") and x.ty.ndim <= 1 and axis == 1:
+ axis = 0
+ out_dtype = scale.ty.dtype.dtype
+ if zp is None:
+ zp = relax.const(0, x.ty.dtype.dtype)
+ return relax.op.dequantize(x, scale, zp, axis=axis,
out_dtype=out_dtype)
+
+ @classmethod
+ def _impl_v21(cls, bb, inputs, attr, params):
+ if attr.get("block_size", 0) != 0:
+ raise ValueError("DequantizeLinear blocked quantization is not
supported yet.")
+ x, scale = inputs[0], inputs[1]
+ zp = inputs[2] if len(inputs) > 2 and inputs[2] is not None else None
+ axis = attr.get("axis", 1)
+ if hasattr(x.ty, "ndim") and x.ty.ndim <= 1 and axis == 1:
+ axis = 0
+ out_dtype = scale.ty.dtype.dtype
+ if zp is None:
+ zp = relax.const(0, x.ty.dtype.dtype)
+ return relax.op.dequantize(x, scale, zp, axis=axis,
out_dtype=out_dtype)
Review Comment:
The implementation of `_impl_v21` in `DequantizeLinear` is identical to
`_impl_v19` except for the `block_size` check. You can reduce code duplication
by checking `block_size` and then delegating to `_impl_v19`.
```suggestion
@classmethod
def _impl_v21(cls, bb, inputs, attr, params):
if attr.get("block_size", 0) != 0:
raise ValueError("DequantizeLinear blocked quantization is not
supported yet.")
return cls._impl_v19(bb, inputs, attr, params)
```
##########
python/tvm/relax/frontend/onnx/onnx_frontend.py:
##########
@@ -356,6 +356,78 @@ def _impl_v13(cls, bb, inputs, attr, params):
zp = relax.const(0, out_dtype)
return relax.op.quantize(x, scale, zp, axis=axis, out_dtype=out_dtype)
+ @classmethod
+ def _impl_v19(cls, bb, inputs, attr, params):
+ x, scale = inputs[0], inputs[1]
+ zp = inputs[2] if len(inputs) > 2 and inputs[2] is not None else None
+ axis = attr.get("axis", 1)
+ if hasattr(x.ty, "ndim") and x.ty.ndim <= 1 and axis == 1:
+ axis = 0
+ out_dtype = "uint8" if zp is None else zp.ty.dtype.dtype
+ if attr.get("saturate", 1) != 1 and
str(out_dtype).startswith("float8"):
+ raise ValueError(
+ "QuantizeLinear float8 quantization with saturate=0 is not
supported yet."
+ )
+ if zp is None:
+ zp = relax.const(0, out_dtype)
+ return relax.op.quantize(x, scale, zp, axis=axis, out_dtype=out_dtype)
+
+ @classmethod
+ def _impl_v21(cls, bb, inputs, attr, params):
+ if attr.get("block_size", 0) != 0:
+ raise ValueError("QuantizeLinear blocked quantization is not
supported yet.")
+ x, scale = inputs[0], inputs[1]
+ zp = inputs[2] if len(inputs) > 2 and inputs[2] is not None else None
+ axis = attr.get("axis", 1)
+ if hasattr(x.ty, "ndim") and x.ty.ndim <= 1 and axis == 1:
+ axis = 0
+ output_dtype = attr.get("output_dtype", 0)
+ out_dtype = (
+ get_type(output_dtype) if output_dtype else "uint8" if zp is None
else zp.ty.dtype.dtype
+ )
+ if attr.get("saturate", 1) != 1 and
str(out_dtype).startswith("float8"):
+ raise ValueError(
+ "QuantizeLinear float8 quantization with saturate=0 is not
supported yet."
+ )
+ if zp is not None and zp.ty.dtype.dtype != out_dtype:
+ raise ValueError(
+ "QuantizeLinear output_dtype must match the zero-point dtype, "
+ f"but got {out_dtype} and {zp.ty.dtype.dtype}."
+ )
+ if zp is None:
+ zp = relax.const(0, out_dtype)
+ return relax.op.quantize(x, scale, zp, axis=axis, out_dtype=out_dtype)
+
+ @classmethod
+ def _impl_v23(cls, bb, inputs, attr, params):
+ if attr.get("block_size", 0) != 0:
+ raise ValueError("QuantizeLinear blocked quantization is not
supported yet.")
+ if attr.get("precision", 0) != 0:
+ raise ValueError(
+ "QuantizeLinear with a non-default precision attribute is not
supported yet."
+ )
+ x, scale = inputs[0], inputs[1]
+ zp = inputs[2] if len(inputs) > 2 and inputs[2] is not None else None
+ axis = attr.get("axis", 1)
+ if hasattr(x.ty, "ndim") and x.ty.ndim <= 1 and axis == 1:
+ axis = 0
+ output_dtype = attr.get("output_dtype", 0)
+ out_dtype = (
+ get_type(output_dtype) if output_dtype else "uint8" if zp is None
else zp.ty.dtype.dtype
+ )
+ if attr.get("saturate", 1) != 1 and
str(out_dtype).startswith("float8"):
+ raise ValueError(
+ "QuantizeLinear float8 quantization with saturate=0 is not
supported yet."
+ )
+ if zp is not None and zp.ty.dtype.dtype != out_dtype:
+ raise ValueError(
+ "QuantizeLinear output_dtype must match the zero-point dtype, "
+ f"but got {out_dtype} and {zp.ty.dtype.dtype}."
+ )
+ if zp is None:
+ zp = relax.const(0, out_dtype)
+ return relax.op.quantize(x, scale, zp, axis=axis, out_dtype=out_dtype)
Review Comment:
There is significant code duplication between `_impl_v21` and `_impl_v23` in
`QuantizeLinear`. Since `_impl_v23` is identical to `_impl_v21` except for the
additional `precision` attribute check, you can simplify `_impl_v23` by
checking `precision` and then delegating directly to `_impl_v21`.
```suggestion
@classmethod
def _impl_v23(cls, bb, inputs, attr, params):
if attr.get("precision", 0) != 0:
raise ValueError(
"QuantizeLinear with a non-default precision attribute is
not supported yet."
)
return cls._impl_v21(bb, inputs, attr, params)
```
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