[Prim] Add instance_norm composite rule (#52203)

* fix

* fix

* fix

* fix

* fix

* fix

* fix

* fix

* fix

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* fix

* fix

* fix

* fix

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* isamp

* gpu

* cpu

* noamp

* fix instance_norm

* fix

* fix unit test

* fix unit test

* add unit test

* fix

* add big data tests

* fix

* fix

* fix

* fix

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* add test case

* fix

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* remove amp test

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Co-authored-by: Nheyanru01 <429520051@qq.com>

python/paddle/incubate/autograd/composite_rules.py

@@ -178,6 +178,36 @@ def layernorm_composite(x, scale, bias, epsilon, begin_norm_axis):
     return out, mean_, variance
 
 
+@REGISTER_COMPOSITE('instance_norm')
+def instancenorm_composite(x, scale, bias, epsilon):
+    """
+    define composite rule of op instance_norm
+    out = (x - mean(x)) / sqrt(var + epsilon))
+    var = mean((x-mean(x))^2)
+    """
+    n, c, h, w = x.shape
+    axis = tuple(range(2, len(x.shape)))
+    mean_ = mean(x, axis=axis, keepdim=True)
+    difference = x - mean_
+    var_tmp1 = difference * difference
+    variance = mean(var_tmp1, axis=axis, keepdim=True)
+    var_tmp3 = variance + epsilon
+    sqrt_var = pow(var_tmp3, full([], 0.5, dtype=var_tmp3.dtype))
+    out = difference / sqrt_var
+
+    if scale is not None:
+        scale_tile = reshape(scale, [1, c, 1, 1])
+        out = out * scale_tile
+    if bias is not None:
+        bias_tile = reshape(bias, [1, c, 1, 1])
+        out = out + bias_tile
+
+    mean_ = reshape(mean_, [-1])
+    saved_variance = 1 / sqrt_var
+    saved_variance = reshape(saved_variance, [-1])
+    return out, mean_, saved_variance
+
+
 @REGISTER_COMPOSITE('gelu')
 def gelu_composite(x, approximate):
     """define composite rule of op gelu"""