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Add silu and mul grad #502

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Add silu and mul grad #502

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20 changes: 20 additions & 0 deletions src/flag_gems/fused/silu_and_mul.py
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Original file line number Diff line number Diff line change
Expand Up @@ -15,12 +15,32 @@ def silu_and_mul_kernel(x, y):
return x_silu * y


@pointwise_dynamic(
promotion_methods=[(0, 1, 2, "DEFAULT"), (0, 1, 2, "DEFAULT")], num_outputs=2
)
@triton.jit
def silu_and_mul_grad_kernel(x, y, dgrad):
x_fp32 = x.to(tl.float32)
sig = 1 / (1 + tl.exp(-x_fp32))
x_silu = x_fp32 * sig
d_x_silu = sig * (1 + x_fp32 * (1 - sig))
dx = d_x_silu * dgrad * y
dy = dgrad * x_silu
return dx, dy


class SiluAndMul(torch.autograd.Function):
@staticmethod
def forward(ctx, A, B):
ctx.save_for_backward(A, B)
logging.debug("GEMS SILU AND MUL FORWARD")
return silu_and_mul_kernel(A, B)

def backward(ctx, grad_output):
A, B = ctx.saved_tensors
grad_A, grad_B = silu_and_mul_grad_kernel(A, B, grad_output)
return grad_A, grad_B


def silu_and_mul(A, B):
return SiluAndMul.apply(A, B)
17 changes: 15 additions & 2 deletions tests/test_binary_pointwise_ops.py
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Expand Up @@ -1353,16 +1353,29 @@ def test_accuracy_rsub(shape, alpha, dtype):
@pytest.mark.parametrize("shape", POINTWISE_SHAPES)
@pytest.mark.parametrize("dtype", FLOAT_DTYPES)
def test_accuracy_silu_and_mul(shape, dtype):
inp1 = torch.randn(shape, dtype=dtype, device=flag_gems.device)
inp2 = torch.randn(shape, dtype=dtype, device=flag_gems.device)
inp1 = torch.randn(shape, dtype=dtype, device=flag_gems.device, requires_grad=True)
inp2 = torch.randn(shape, dtype=dtype, device=flag_gems.device, requires_grad=True)
ref_inp1 = to_reference(inp1, True)
ref_inp2 = to_reference(inp2, True)

ref_out = torch.mul(torch.nn.functional.silu(ref_inp1), ref_inp2)
with flag_gems.use_gems():
res_out = flag_gems.silu_and_mul(inp1, inp2)

out_grad = torch.randn_like(res_out)
ref_grad = to_reference(out_grad, True)

(ref_inp1_grad, ref_inp2_grad) = torch.autograd.grad(
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I recommend splitting out the backward test, so that the input data received by gems function could be the same as the reference function

ref_out, (ref_inp1, ref_inp2), ref_grad
)

(res_inp1_grad, res_inp2_grad) = torch.autograd.grad(
res_out, (inp1, inp2), out_grad
)

gems_assert_close(res_out, ref_out, dtype)
gems_assert_close(res_inp1_grad, ref_inp1_grad, dtype)
gems_assert_close(res_inp2_grad, ref_inp2_grad, dtype)


@pytest.mark.sub
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