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@@ -19,8 +19,8 @@ typedef enum{ |
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template <typename T, typename GRAD_T> |
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__global__ void adam_cuda_kernel( |
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- T* __restrict__ p, |
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- GRAD_T* __restrict__ p_copy, // For mixed precision training, pass NULL if not needed |
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+ GRAD_T* __restrict__ p, |
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+ T* __restrict__ p_copy, // For mixed precision training, pass NULL if not needed |
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T* __restrict__ m, |
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T* __restrict__ v, |
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const GRAD_T * __restrict__ g, |
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@@ -50,7 +50,7 @@ __global__ void adam_cuda_kernel( |
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else // Mode 1 |
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denom = sqrtf(v[j]) + eps; |
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float update = (m[j]/denom) + (decay*p[j]); |
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- p[j] = p[j] - (step_size*update); |
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+ p[j] = (GRAD_T) (p[j] - (step_size*update)); |
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if (p_copy != NULL) p_copy[j] = (GRAD_T) p[j]; |
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} |
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} |
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@@ -93,14 +93,14 @@ void fused_adam_cuda( |
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if (g.scalar_type() == at::ScalarType::Half) { |
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//all other values should be fp32 for half gradients |
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- AT_ASSERTM(p.scalar_type() == at::ScalarType::Float, "expected parameter to be of float type"); |
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+// AT_ASSERTM(p.scalar_type() == at::ScalarType::Float, "expected parameter to be of float type"); |
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//dispatch is done on the gradient type |
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using namespace at; // prevents "toString is undefined" errors |
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DISPATCH_FLOAT_AND_HALF(g.scalar_type(), 0, "adam_cuda_kernel", |
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using accscalar_t = at::acc_type<scalar_t_0, true>; |
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adam_cuda_kernel<accscalar_t, scalar_t_0><<<blocks,threadsPerBlock, 0, stream>>>( |
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- p.data<accscalar_t>(), |
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- p_copy.numel() ? p_copy.data<scalar_t_0>() : NULL, |
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+ p.data<scalar_t_0>(), |
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+ NULL, //don't output p_copy for fp32, it's wasted write |
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m.data<accscalar_t>(), |
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v.data<accscalar_t>(), |
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g.data<scalar_t_0>(), |
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