Fix vector blockwise acc problem

Signed-off-by: wenjh <wenjh@sugon.com>

tests/pytorch/references/blockwise_quantizer_reference.py

@@ -171,7 +171,11 @@ class BlockwiseQuantizerReference:
         qx = x_tiled * scale.reshape(M, K // tile_len, 1)
         qx = torch.clamp(qx, min=-dtype_max, max=dtype_max)
         if quant_dtype == torch.int8:
-            qx = torch.round(qx)
+            positive_mask = qx >= 0
+            negative_mask = ~positive_mask
+            pos_part = torch.where(positive_mask, torch.floor(qx + 0.5), 0)
+            neg_part = torch.where(negative_mask, torch.ceil(qx - 0.5), 0)
+            qx = pos_part + neg_part
         qx = qx.to(dtype=quant_dtype)
         qx = qx.reshape(M, K)
         return qx, scale_inv

tests/pytorch/references/quantize_scale_calc.py

@@ -4,7 +4,7 @@
 
 from typing import Tuple
 import torch
-
+from torch.utils.cpp_extension import IS_HIP_EXTENSION
 
 def scale_from_amax_tensor(
     x_dtype: torch.dtype,
@@ -48,6 +48,10 @@ def scale_from_amax_tensor(
         # No subnormals and zero.
         assert (exp > -127).all()
         unity = torch.tensor([1.0], device=exp.device)
+        if IS_HIP_EXTENSION:
+            host_scale = torch.ldexp(unity.cpu(), exp.cpu())
+            scale = host_scale.to(exp.device)
+        else:
             torch.ldexp(unity, exp, out=scale)
         # Case where amax is inf. The frexp, ldexp logic changes 0.0 scales
         # Return 0.0 for 0.0 scale for consistency with non-pow2 scale

transformer_engine/common/transpose/quantize_transpose_square_blockwise.cu

@@ -187,8 +187,15 @@ __global__ void __launch_bounds__(THREADS_PER_BLOCK)
       // Step 3: Store cast output
       CType scale_data = block_tile_scale;
 
-      OType scaled_elt =
+      OType scaled_elt = 0;
+      if constexpr(std::is_same_v<OType, int8_t>) {
+          scaled_elt =
+            static_cast<OType>(lroundf(fmaxf(-127.0f, fminf(127.0f, static_cast<CType>(thrd_tile_input[i].data.elt[j]) * scale_data))));
+        }
+        else {
+          scaled_elt =
             static_cast<OType>(static_cast<CType>(thrd_tile_input[i].data.elt[j]) * scale_data);
+        }
       tmp_output_c.data.elt[j] = scaled_elt;
       // Step 4: do transpose within thread tile
       if constexpr (kReturnTranspose) {