[mxfp] Reland remove col-major assert for mx weight

python/triton_kernels/tests/test_matmul.py

@@ -194,6 +194,7 @@ class Case:
     x_transpose: bool = False
     w_transpose: bool = False
     y_transpose: bool = False
+    colmajor_mxfp_weight: bool = True
 
 
 @pytest.mark.parametrize(
@@ -267,6 +268,7 @@ class Case:
             Case(1000, 704, 800, "batched", "mxfloat8_e4m3fn", "mxfloat4_e2m1", 2, 1),
             Case(1000, 704, 800, "ragged", "mxfloat8_e4m3fn", "mxfloat4_e2m1", 8, 2, split_k=9),
             Case(1000, 704, 800, "ragged", "mxfloat8_e4m3fn", "mxfloat4_e2m1", 8, 2, split_k=9, hbm_swizzling=True),
+            Case(1000, 704, 800, "ragged", "mxfloat8_e4m3fn", "mxfloat4_e2m1", 8, 2, split_k=9, colmajor_mxfp_weight=False),
             Case(1000, 704, 800, "ragged", "mxfloat8_e4m3fn", "mxfloat4_e2m1", 8, 2),
             Case(1000, 704, 800, "ragged", "mxfloat8_e4m3fn", "mxfloat4_e2m1", 8, 2, hbm_swizzling=True),
             Case(300, 400, 400, "ragged", "mxfloat8_e4m3fn", "mxfloat8_e4m3fn", 8, 4),
@@ -313,7 +315,7 @@ class Case:
 @pytest.mark.parametrize("has_y_gammas", [False, True])
 @pytest.mark.parametrize("is_persistent", [False, True])
 def test_op(m, n, k, split_k, do_gather, do_scatter, fused_scatter, inner_expt_opt, has_y_gammas, is_persistent, n_expts_tot,
-            n_expts_act, mode, act_dtype_str, weight_dtype_str, block_m, hbm_swizzling, epilogue_subtile,
+            n_expts_act, mode, act_dtype_str, weight_dtype_str, block_m, hbm_swizzling, colmajor_mxfp_weight, epilogue_subtile,
             x_transpose, w_transpose, y_transpose,
             device, opt_flags_scope):
     # TODO: remove when Triton FP8 supports proper RTNE
@@ -461,14 +463,72 @@ def test_op(m, n, k, split_k, do_gather, do_scatter, fused_scatter, inner_expt_o
             w_scale_layout, w_scale_layout_opts = layout.make_default_matmul_mxfp4_w_scale_layout(
                 mx_axis=mx_axis, num_warps=8)
         # downcast to mxfp
-        w_tri, w_scale_tri = downcast_to_mxfp(w_tri, weight_dtype, axis=mx_axis)
-        w_ref = upcast_from_mxfp(w_tri, w_scale_tri, torch.bfloat16, axis=mx_axis)
-        w_tri_dtype = FP4 if "float4" in weight_dtype_str else weight_dtype
-        w_tri = wrap_torch_tensor(w_tri, w_tri_dtype)
-        w_scale_tri = wrap_torch_tensor(w_scale_tri)
-        # convert layouts
-        w_tri = convert_layout(w_tri, w_layout, **w_layout_opts)
-        w_scale_tri = convert_layout(w_scale_tri, w_scale_layout, **w_scale_layout_opts)
+        w_tri_orig = w_tri
+        if colmajor_mxfp_weight:
+            w_tri, w_scale_tri = downcast_to_mxfp(w_tri, weight_dtype, axis=mx_axis)
+            w_ref = upcast_from_mxfp(w_tri, w_scale_tri, torch.bfloat16, axis=mx_axis)
+            w_tri_dtype = FP4 if "float4" in weight_dtype_str else weight_dtype
+            w_tri = wrap_torch_tensor(w_tri, w_tri_dtype)
+            w_scale_tri = wrap_torch_tensor(w_scale_tri)
+            # convert layouts
+            w_tri = convert_layout(w_tri, w_layout, **w_layout_opts)
+            w_scale_tri = convert_layout(w_scale_tri, w_scale_layout, **w_scale_layout_opts)
+        else:
+            if is_cuda() and torch.cuda.get_device_capability()[0] < 10:
+                pytest.skip("transposed mxfp weight not supported with cuda capability < 10")
+            if block_m == 16:
+                pytest.skip("PassManager::run failed from Triton compiler")
+            # TODO: swizzling for rowmajor
+
+            # A typical use case is we already quantized col-major weight,
+            # and we want matmul with its transposed row-major weight w/o
+            # requantization.
+
+            # put abs_max of each 32x32 block to diagonal so scales of transposed agree
+            w_ndim = w_tri.ndim
+            if w_ndim == 2:
+                w_tri = w_tri.unsqueeze(0)
+            BLOCK_SIZE = int(MXFP_BLOCK_SIZE)
+            for e, i, j in itertools.product(range(w_tri.shape[0]), range(0, w_tri.shape[1], BLOCK_SIZE), range(0, w_tri.shape[2], BLOCK_SIZE)):
+                i_end = min(i+BLOCK_SIZE, w_tri.shape[1])
+                j_end = min(j+BLOCK_SIZE, w_tri.shape[2])
+                block = w_tri[e, i:i_end, j:j_end]
+                m_abs = block.abs().max()
+                i_len = i_end - i
+                j_len = j_end - j
+                min_len = min(i_len, j_len)
+                signs = torch.randint(0, 2, (max(i_len, j_len),), device=w_tri.device) * 2 - 1
+                block.diagonal(dim1=-2, dim2=-1)[:] = signs[:min_len] * m_abs
+                if j_len > i_len:
+                    block[i_len - 1, i_len:] = signs[min_len:] * m_abs
+                elif i_len > j_len:
+                    block[j_len:, j_len - 1] = signs[min_len:] * m_abs
+            if w_ndim == 2:
+                w_tri = w_tri.squeeze(0)
+
+            # matmul with rowmajor weight expects scale is separately
+            # constructed (not much additional memory needed).
+            _, w_scale_tri = downcast_to_mxfp(w_tri, weight_dtype, axis=mx_axis)
+            # reuse quantized value from colmajor
+            w_tri_rowmajor, w_scale_tri_rowmajor = downcast_to_mxfp(w_tri.mT.contiguous(), weight_dtype, axis=mx_axis)
+            w_ref = upcast_from_mxfp(w_tri_rowmajor, w_scale_tri_rowmajor, torch.bfloat16, axis=mx_axis).mT.contiguous()
+            w_tri = w_tri_rowmajor.data.mT
+
+            def _pad_and_block(x: torch.Tensor) -> torch.Tensor:
+                x = torch.nn.functional.pad(x, (0, x.shape[-1] % BLOCK_SIZE), mode="replicate")
+                return x.view(*x.shape[:-1], x.shape[-1] // BLOCK_SIZE, BLOCK_SIZE)
+
+            # check if generated scale is transpose-invariant as intended construction
+            # [cdiv(K, 32), N] -> dedup to [cdiv(K, 32), cdiv(N, 32)]
+            w_scale_tri_blocked = _pad_and_block(w_scale_tri)
+            w_scale_tri_sampled = w_scale_tri_blocked[..., 0:1]
+            # [cdiv(N, 32), K] -> dedup to [cdiv(N, 32), cdiv(K, 32)]
+            w_scale_tri_rowmajor_blocked = _pad_and_block(w_scale_tri_rowmajor)
+            w_scale_tri_rowmajor_sampled = w_scale_tri_rowmajor_blocked[..., 0:1]
+            assert torch.equal(w_scale_tri_sampled.expand_as(w_scale_tri_blocked), w_scale_tri_blocked)
+            assert torch.equal(w_scale_tri_rowmajor_sampled.expand_as(w_scale_tri_rowmajor_blocked), w_scale_tri_rowmajor_blocked)
+            assert torch.equal(w_scale_tri_sampled.squeeze(-1), w_scale_tri_rowmajor_sampled.squeeze(-1).mT)
+
         precision_opt.weight_scale = w_scale_tri
     epilogue = None
     if act_mxfp8:
@@ -477,7 +537,7 @@ def test_op(m, n, k, split_k, do_gather, do_scatter, fused_scatter, inner_expt_o
         is_input_batched = x_tri.ndim == 3
         y_shape = x_tri.shape if is_input_batched else (1,) + x_tri.shape
         n_rows = y_shape[1] if gindx is None or mode == "batched" else gindx.dst_indx.shape[0]
-        y_shape = (y_shape[0], n_rows, w_tri.shape[-1])
+        y_shape = (y_shape[0], n_rows, w_tri_orig.shape[-1])
         if sindx is None or mode == "batched":
             if not is_input_batched:
                 y_shape = (y_shape[1], y_shape[2])

python/triton_kernels/triton_kernels/matmul_ogs.py

@@ -16,6 +16,7 @@
 from .matmul_ogs_details._p_matmul_ogs import _p_matmul_ogs, get_per_device_per_stream_alloc_fn
 from .matmul_ogs_details._reduce_grouped import _reduce_grouped
 from .numerics_details.mxfp import MXFP_BLOCK_SIZE
+from .tensor_details.layout_details.strided import StridedLayout
 from .matmul_ogs_details.opt_flags import make_opt_flags, update_opt_flags_constraints, InapplicableConstraint
 from .specialize import specialize
 from .tensor import Storage, Tensor, FP4, bitwidth, wrap_torch_tensor, RaggedTensorMetadata
@@ -483,12 +484,13 @@ def matmul_ogs(x, w, bias,
     w_scale = precision_config.weight_scale
     w_has_mx = w_scale is not None
     is_hopper_fp8 = is_cuda() and not target_info.cuda_capability_geq(10, 0) and bitwidth(w.dtype) == 8
-    if w_has_mx: assert w.stride(-2) == 1, "`w` must be column-major when it has data-type mxfp"
     if is_hopper_fp8: assert w.stride(-2) == 1, "`w` must be column-major when it has data-type FP8 on capability < 10"
     if not isinstance(w, Tensor):
         # TODO: remove this code path; using uint8 for mxfp4 weight will bite us when we want to support uint8 for real
         dtype = FP4 if w.dtype == torch.uint8 else w.dtype
         w = wrap_torch_tensor(w, dtype=dtype)
+    if w_has_mx and is_cuda() and (torch.cuda.get_device_capability()[0] < 10 or w.storage.layout is not None and not isinstance(w.storage.layout, StridedLayout)):
+        assert w.stride(-2) == 1, "`w` must be column-major when it has data-type mxfp and (swizzled or not on >=Blackwell)"
     if w_scale is not None and not isinstance(w_scale, Tensor):
         w_scale = Tensor(w_scale)
     if w_scale is not None:
@@ -535,7 +537,7 @@ def matmul_ogs(x, w, bias,
     )
     has_gather_tma = has_gather and target_info.has_tma_gather()
     # hopper w/ mxfp4 doesn't support TMA
-    can_use_tma = can_use_tma and (torch.cuda.get_device_capability()[0] > 9 or bitwidth(w.dtype) != 4)
+    can_use_tma = can_use_tma and  is_cuda() and (torch.cuda.get_device_capability()[0] > 9 or bitwidth(w.dtype) != 4)
     can_use_fused_scatter = has_scatter and (fused_activation.specs.fn is None) and (epilogue.specs.fn is None) and (routing_data.n_expts_act == 1)
     opt_flags = make_opt_flags(out_dtype, x.dtype, w.dtype, precision_config,
         batch_size, M, N, w.shape[-2], routing_data,