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[Frontend][PaddlePaddle] Update the export method of PaddlePaddle Softmax #16653

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[Frontend][PaddlePaddle] Update the export method of PaddlePaddle Softmax #16653

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2dd69cd
update paddlepaddle softmax
Zheng-Bicheng Feb 28, 2024
c9f589c
Merge branch 'apache:main' into fixed_softmax
Zheng-Bicheng Feb 29, 2024
f9e58a7
Merge branch 'main' into fixed_softmax
Zheng-Bicheng Mar 26, 2024
1226b81
update paddlepaddle.py
Zheng-Bicheng Mar 26, 2024
17c5b53
update cmsisnn.py
Zheng-Bicheng Mar 26, 2024
13a8cd5
update cmsisnn.py
Zheng-Bicheng Mar 26, 2024
28a2266
update lint
Zheng-Bicheng Mar 27, 2024
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24 changes: 13 additions & 11 deletions python/tvm/relay/frontend/paddlepaddle.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1826,10 +1826,12 @@ def convert_dequantize_linear(g, op, block):
data_node = g.get_node(data_node_name)

# paddle_scale = tvm_scale * 127
paddle_quantize_scale = g.get_params(op.input("Scale")[0]).asnumpy()
tvm_quantize_scale = paddle_quantize_scale / 127.0
paddle_dequantize_scale = g.get_params(op.input("Scale")[0]).asnumpy()
tvm_dequantize_scale = paddle_dequantize_scale / 127.0
tvm_dequantize_scale = tvm_dequantize_scale.squeeze()

tvm_quantize_zp = g.get_params(op.input("ZeroPoint")[0]).asnumpy()
tvm_dequantize_zp = g.get_params(op.input("ZeroPoint")[0]).asnumpy()
tvm_dequantize_zp = tvm_dequantize_zp.squeeze()

tvm_quantize_axis = op.attr("quant_axis")
if tvm_quantize_axis == -1:
Expand All @@ -1840,8 +1842,8 @@ def convert_dequantize_linear(g, op, block):

out = _qnn.op.dequantize(
data=data_node,
input_scale=_op.const(tvm_quantize_scale, "float32"),
input_zero_point=_op.const(tvm_quantize_zp, "int32"),
input_scale=_expr.const(tvm_dequantize_scale, "float32"),
input_zero_point=_expr.const(tvm_dequantize_zp, "int32"),
axis=tvm_quantize_axis,
)
g.add_node(op.output("Y")[0], out)
Expand All @@ -1856,17 +1858,19 @@ def convert_quantize_linear(g, op, block):
# paddle_scale = tvm_scale * 127
paddle_quantize_scale = g.get_params(op.input("Scale")[0]).asnumpy()
tvm_quantize_scale = paddle_quantize_scale / 127.0
tvm_quantize_scale = tvm_quantize_scale.squeeze()

tvm_quantize_zp = g.get_params(op.input("ZeroPoint")[0]).asnumpy()
tvm_quantize_zp = tvm_quantize_zp.squeeze()
tvm_quantize_axis = op.attr("quant_axis")

if tvm_quantize_axis == -1:
tvm_quantize_axis = 0

out = _qnn.op.quantize(
data=data_node,
output_scale=_op.const(tvm_quantize_scale, "float32"),
output_zero_point=_op.const(tvm_quantize_zp, "int32"),
output_scale=_expr.const(tvm_quantize_scale, "float32"),
output_zero_point=_expr.const(tvm_quantize_zp, "int32"),
axis=tvm_quantize_axis,
)
g.add_node(op.output("Y")[0], out)
Expand Down Expand Up @@ -2446,14 +2450,12 @@ def convert_slice(g, op, block):
def convert_softmax(g, op, block):
"""Operator converter for softmax."""

x = g.get_node(op.input("X")[0])
data = g.get_node(op.input("X")[0])
axis = op.attr("axis")
input_shape = block.var(op.input("X")[0]).shape
if axis < 0:
axis = len(input_shape) + axis
m = _op.max(x, axis, keepdims=True)
e = _op.exp(x - m)
out = e / _op.sum(e, axis, keepdims=True)
out = _op.nn.softmax(data, axis)
g.add_node(op.output("Out")[0], out)


Expand Down
10 changes: 7 additions & 3 deletions python/tvm/relay/op/contrib/cmsisnn.py
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Original file line number Diff line number Diff line change
Expand Up @@ -86,9 +86,14 @@ def check_qnn_softmax(pattern):
zero_point = pattern.args[2].data.numpy().item(0)

# check for dtypes of quantize and dequantize
# if (
# (scale == 1.0 / 256 and zero_point == -128)
# and pattern.attrs.out_dtype == "int8"
# and dequantize_call.args[0].checked_type.dtype == "int8"
# ):
# return True
if (
(scale == 1.0 / 256 and zero_point == -128)
and pattern.attrs.out_dtype == "int8"
pattern.attrs.out_dtype == "int8"
and dequantize_call.args[0].checked_type.dtype == "int8"
):
return True
Expand All @@ -99,7 +104,6 @@ def check_qnn_softmax(pattern):
and dequantize_call.args[0].checked_type.dtype == "int16"
):
return True

return False

def qnn_conv2d_pattern(with_pad):
Expand Down