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implemented std_grad in backward.mojo #16

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implemented std_grad in backward.mojo #16

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918d380
implemented std_grad in backward.mojo
ManishAradwad Nov 6, 2023
9d45939
correction in std_grad
ManishAradwad Nov 7, 2023
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22 changes: 21 additions & 1 deletion infermo/operators/backward.mojo
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Original file line number Diff line number Diff line change
Expand Up @@ -503,7 +503,27 @@ fn variance_grad(b: Tensor, inout a: Tensor):

@always_inline
fn std_grad(b: Tensor, inout a: Tensor):
pass
let dim_len: Int = b.other_params.load(0)

var b_dims = DynamicVector[Int](0)
for d in range(b.num_dims):
b_dims.push_back(b.shape[d])
var mean_output = Tensor(b_dims)
mean_output.other_params = b.other_params

# Calculate the gradient of the variance first
variance_grad(b, a)

@parameter
fn _std_grad[_nelts: Int](idx_b: Int, idx_a: Int):
# Chain rule to calculate gradient
let std_derivative = 0.5 / sqrt(b.data.load(idx_b)) # 1/2 * 1/sqrt(variance)
a.grad.simd_store[_nelts](
idx_a, a.grad.simd_load[_nelts](idx_a) + std_derivative * b.grad.load(idx_b)
)

expand_unary_operation_iterator[_std_grad](b, a)



# elementwise operators #####################################################################################################
Expand Down