Add flatten composite rule #50672
Add flatten composite rule #50672
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| @REGISTER_COMPOSITE('flatten_contiguous_range') | ||
| def flatten_contiguous_range_composite(x, start_axis, stop_axis): | ||
| """define composite rule of op flatten, flatten_contiguous_range -> flatten""" |
| out = reshape(x, shape=shape_out), xshape | ||
| """ | ||
| shape_in = x.shape | ||
| shape_x_out: List[int] = [0] |
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@cyber-pioneer 改成通用写法了，测试通过。
| self.start_axis = 0 | ||
| self.stop_axis = -1 | ||
| self.init_test_case() | ||
| self.inputs = {"X": np.random.random(self.in_shape).astype("float64")} | ||
| self.init_attrs() | ||
| self.enable_cinn = False |
… composite_rule_flatten
… composite_rule_flatten
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LGTM |
| shape_out.append(slice_numel) | ||
| for i in range(end_dim + 1, len(shape_in)): | ||
| shape_out.append(shape_in[i]) | ||
| return reshape(x, shape=shape_out), xshape |
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Can this op doesn't return xshape?
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There is xshape in the yaml definition, and need to use xshape when calculate grad.
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The yaml defineition :
Paddle/paddle/phi/api/yaml/legacy_ops.yaml
Line 644 in 6786c01
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Yes, but for this case, which is a little special, CINN can't optimize xshape and actually it doesn't need xshape for backward pass. So, it's better to remove xshape usage if it's possible
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| shape_out.append(slice_numel) | ||
| for i in range(end_dim + 1, len(shape_in)): | ||
| shape_out.append(shape_in[i]) | ||
| return reshape(x, shape=shape_out), xshape |
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Yes, but for this case, which is a little special, CINN can't optimize xshape and actually it doesn't need xshape for backward pass. So, it's better to remove xshape usage if it's possible
… composite_rule_flatten
xysheng-baidu
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@JiabinYang remove xshape and return none, passed the test. |
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add composite rule for flatten