masahi commented on a change in pull request #6782:
URL: https://github.com/apache/incubator-tvm/pull/6782#discussion_r513775708
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File path: python/tvm/relay/frontend/qnn_torch.py
##########
@@ -826,6 +826,74 @@ def _impl(inputs, _):
return _impl
+def _linear_dynamic():
+ def _calculate_qparam(inp):
+ # reference ATen/native/quantized/cpu/qlinear_dynamic.cpp
+ # ChooseQuantizationParams function
+ mn = _op.min(inp)
+ mx = _op.max(inp)
+
+ # Ensure that the interval contains 0
+ mn = _op.minimum(mn, _op.const(0.0, dtype="float32"))
+ mx = _op.maximum(mx, _op.const(0.0, dtype="float32"))
+
+ qmax = 255
+
+ # reduce_range became True in v1.6
+ if is_version_greater_than("1.5.1"):
+ qmax = 127
+
+ scale = (mx - mn) / _expr.const(qmax, dtype="float32")
+
+ zero_point_from_min = -(mn / scale)
+ zero_point = _op.cast(_op.round(_op.clip(zero_point_from_min, 0.0,
qmax)), "int32")
+
+ return scale, zero_point
+
+ def _impl(inputs, _):
+ weight = inputs[1][0]
+ weight_scale = inputs[1][1]
+ weight_zero_point = inputs[1][2]
+
+ inp = inputs[0]
+
+ input_scale, input_zero_point = _calculate_qparam(inp)
+ qinp = relay.qnn.op.quantize(inp, input_scale, input_zero_point,
out_dtype="uint8")
+
+ data_shape = infer_shape(inp)
+
+ if len(data_shape) > 2:
+ qinp = _op.reverse_reshape(qinp, [-1, 0])
+
+ weight_shape = infer_shape(weight)
+ units = weight_shape[0]
+ dense = relay.qnn.op.dense(
+ qinp,
+ weight,
+ input_zero_point,
+ weight_zero_point,
+ input_scale,
+ weight_scale,
+ units=units,
+ )
+ bias_var = inputs[1][3]
+
+ dequant_scale = input_scale * weight_scale
+ dense_out = _op.cast(dense, "float32") * dequant_scale
Review comment:
why better? I did try dequantize but it didn't matter in terms of
accuracy
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