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Fix quantize kernels on rocm 6.4 #4708

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Fix quantize kernels on rocm 6.4 #4708

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6 changes: 5 additions & 1 deletion fbgemm_gpu/experimental/gen_ai/src/quantize/quantize.cu
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Original file line number Diff line number Diff line change
Expand Up @@ -89,7 +89,7 @@ namespace fbgemm_gpu {
// outputs are of size float[D]

#if (defined(USE_ROCM) && ROCM_VERSION >= 60200)
#if HIP_FP8_TYPE_OCP
#if HIP_FP8_TYPE_OCP && !HIP_FP8_TYPE_FNUZ
using __nv_fp8x4_e4m3 = __hip_fp8x4_e4m3;
using __nv_fp8x2_e4m3 = __hip_fp8x2_e4m3;
using __nv_fp8_e4m3 = __hip_fp8_e4m3;
Expand Down Expand Up @@ -1075,7 +1075,11 @@ void invokeComputeScalesAndQuantizeMatrix(
bool stochastic_rounding,
cudaStream_t stream) {
dim3 grid(numel / lda);
#ifdef USE_ROCM
bool use_shmem = true;
#else
bool use_shmem = false;
#endif
auto const shmem_size = lda * sizeof(T_IN);
if (shmem_size >= (48 << 10)) {
cudaError_t ret;
Expand Down
16 changes: 7 additions & 9 deletions fbgemm_gpu/experimental/gen_ai/test/quantize/quantize_test.py
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Expand Up @@ -289,7 +289,9 @@ def test_f8f8bf16(self, kernel: str, use_fast_accum: bool) -> None:
["rowwise", "blockwise"]
+ (["tensorwise_broadcast", "tensorwise"] if torch.version.cuda else [])
),
QType=st.sampled_from([fp8_e4m3, fp8_e5m2]),
QType=(
st.sampled_from([fp8_e4m3, fp8_e5m2] if torch.version.cuda else [fp8_e4m3])
),
Bias=st.sampled_from([True, False]),
CudaGraph=st.sampled_from([True, False]),
UseTriton=st.sampled_from([False] + ([True] if torch.version.cuda else [])),
Expand Down Expand Up @@ -406,14 +408,10 @@ def f(
def f(
x: torch.Tensor, w: torch.Tensor, bias: Optional[torch.Tensor]
) -> torch.Tensor:
if torch.version.cuda:
xq, x_scale = torch.ops.fbgemm.quantize_fp8_per_row(
x, output_dtype=QType
)
wq, w_scale = torch.ops.fbgemm.quantize_fp8_per_row(w)
else:
xq, x_scale = quantize_fp8_row(x)
wq, w_scale = quantize_fp8_row(w)
xq, x_scale = torch.ops.fbgemm.quantize_fp8_per_row(
x, output_dtype=QType
)
wq, w_scale = torch.ops.fbgemm.quantize_fp8_per_row(w)
if UseTriton and torch.version.cuda:
zq = matmul_fp8_row(xq, wq, x_scale, w_scale)
if bias is not None:
Expand Down
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