[Quantizaton] [AMD] Add support for running DeepSeek int8 w8a8 MoE on ROCm

vllm/_custom_ops.py

@@ -559,15 +559,15 @@ def cutlass_scaled_mm(a: torch.Tensor,
         scale_a.shape * [1, 128] == a.shape
         scale_b.shape * [128, 128] == b.shape
     """
-    assert (b.shape[0] % 16 == 0 and b.shape[1] % 16 == 0)
     assert (out_dtype is torch.bfloat16 or out_dtype is torch.float16)
     assert bias is None or bias.shape[0] == b.shape[
         1] and bias.dtype == out_dtype
 
     m = a.shape[0]
     n = b.shape[1]
 
-    if current_platform.is_rocm():
+    cutlass_compatible_b = (b.shape[0] % 16 == 0 and b.shape[1] % 16 == 0)
+    if current_platform.is_rocm() or not cutlass_compatible_b:
         triton_scaled_mm_module = importlib.import_module(
             "vllm.model_executor.layers.quantization.compressed_tensors."
             "triton_scaled_mm")

vllm/model_executor/layers/quantization/utils/int8_utils.py

@@ -85,6 +85,32 @@ def block_dequant(
     return x_dq_block
 
 
+if current_platform.is_rocm():
+    from triton.language import core
+
+    # NOTE: This can be removed when hip.libdevice.round() is available.
+    @core.extern
+    def round_f32(arg0, _builder=None):
+        return core.extern_elementwise("",
+                                       "", [arg0], {
+                                           (core.dtype("fp32"), ):
+                                           ("llvm.round", core.dtype("fp32")),
+                                           (core.dtype("fp64"), ):
+                                           ("llvm.round", core.dtype("fp64")),
+                                       },
+                                       is_pure=True,
+                                       _builder=_builder)
+
+    @triton.jit
+    def round_int8(x):
+        return round_f32(x).to(tl.int8)
+else:
+
+    @triton.jit
+    def round_int8(x):
+        return tl.extra.cuda.libdevice.round(x).to(tl.int8)
+
+
 @triton.jit
 def _per_token_quant_int8(
     x_ptr,
@@ -106,7 +132,7 @@ def _per_token_quant_int8(
     absmax = tl.maximum(tl.max(tl.abs(x)), 1e-10)
     scale_x = absmax / 127
     x_q = x * (127 / absmax)
-    x_q = tl.extra.cuda.libdevice.round(x_q).to(tl.int8)
+    x_q = round_int8(x_q)
 
     tl.store(xq_ptr + row_id * stride_xq + cols, x_q, mask=mask)
     tl.store(scale_ptr + row_id, scale_x)