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Fixed accidential deletion of limits in kernel.

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This commit is contained in:
Tim Dettmers 2023-07-10 14:24:33 -07:00
parent 2221f4cee0
commit 306f6b2362
2 changed files with 17 additions and 6 deletions
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10
csrc/kernels.cu
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@ -3595,7 +3595,7 @@ template <typename T, int THREADS, int BITS> __global__ void kgemm_4bit_inferenc
#endif
}
if(inner_idx+num_values_4bit)
if(inner_idx+num_values_4bit < K)
{
if(BITS==16)
{
@ -3619,11 +3619,17 @@ template <typename T, int THREADS, int BITS> __global__ void kgemm_4bit_inferenc
}
else
for(int k = 0; k < num_values_4bit; k++)
local_A[k] = A[inner_idx +k];
if(inner_idx + k < K)
local_A[k] = A[inner_idx + k];
else
local_A[k] = T(0.0f);
#pragma unroll
for(int k = 0; k < num_values_4bit; k++)
{
if((float)local_A[k] < -10.0f || (float)local_B[k] < -10.0f || local_C > 10.0f)
printf("%f %f = %f\n", (float)local_A[k], (float)local_B[k], local_C);
#if __CUDA_ARCH__ >= 800
local_C += (float)(local_A[k]*local_B[k]);
#else

13
tests/test_functional.py
View File

@ -2378,8 +2378,8 @@ def test_gemv_4bit(dtype, storage_type, double_quant):
#A = torch.rand(1, 4096, dtype=dtype, device='cuda')
#B = torch.rand(4*4096, 4096, dtype=dtype, device='cuda')
A = torch.randn(1, dim, dtype=dtype, device='cuda')
B = torch.randn(4*dim, dim, dtype=dtype, device='cuda')/math.sqrt(dim)
#B = torch.randn(1, dim+2, dtype=dtype, device='cuda')/math.sqrt(dim)
#B = torch.randn(4, dim, dtype=dtype, device='cuda')/math.sqrt(dim)
B = torch.randn(dim*4, dim, dtype=dtype, device='cuda')/math.sqrt(dim)
#print('')
#print(A)
@ -2432,13 +2432,18 @@ def test_gemv_4bit(dtype, storage_type, double_quant):
#print(dim, (max_err.item(), max_relerr.item()))
print(C1.flatten()[-20:])
print(C2.flatten()[-20:])
print(C3.flatten()[-20:])
#print(C1.flatten())
#print(C2.flatten())
#print(C3.flatten()[-20:])
print(sum(errs)/len(errs)/math.sqrt(dim) , dim)
print(sum(relerrs)/len(relerrs)/math.sqrt(dim) , dim)
if dtype == torch.float16:
assert sum(errs)/len(errs)/math.sqrt(dim) < 5e-5
assert sum(relerrs)/len(relerrs)/math.sqrt(dim) < 0.0005
else:
elif dtype == torch.float32:
assert sum(errs)/len(errs)/math.sqrt(dim) < 5e-8
assert sum(relerrs)/len(relerrs)/math.sqrt(dim) < 1e-8
elif dtype == torch.bfloat16:
assert sum(errs)/len(errs)/math.sqrt(dim) < 3e-4
assert sum(relerrs)/len(relerrs)/math.sqrt(dim) < 0.003