[AMD] Supoort T.gemm_v2 for AMD Backend

Author: Paran0idy

examples/plot_layout/fragment_mfma_load_a.py

@@ -0,0 +1,133 @@
+import tilelang.language as T
+from typing import Literal, Callable
+from tvm.tir import IndexMap
+from tilelang.intrinsics.utils import get_mma_micro_size
+
+from tilelang.intrinsics.mfma_layout import (
+    shared_16x4_to_local_64x1_layout_A,
+    shared_16x16_to_local_64x4_layout_A,
+    shared_16x32_to_local_64x8_layout_A,
+    shared_16x64_to_local_64x16_layout_A,
+)
+
+
+def make_mfma_load_base_layout(dtype: str = "float16",
+                               matrix: Literal["A", "B"] = "A",
+                               k_dim: int = 16,
+                               transposed: bool = False) -> T.Fragment:
+    """
+    Create a layout function for storing MFMA results into a fragment buffer.
+    This layout is used in conjunction with `inverse_mfma_store_layout` to
+    map fragment indices to threads and local indices.
+
+    Parameters
+    ----------
+    dtype : str
+        The data type of the matrix.
+    matrix : Literal["A", "B"]
+        The mfma operand to be loaded.
+    k_dim : int
+        The k dimension of the mfma.
+    transposed : bool
+        Whether the matrix is transposed, by default False.
+
+    Returns
+    -------
+    T.Fragment
+        Describes how threads and indices in fragment are laid out.
+
+    """
+
+    assert matrix in ["A", "B"], "matrix should be either A or B"
+    # s represents spatial axis
+    # r represents reduction axis
+    # sr represents the two dims are spatial + reduction
+    # rs represents the two dims are reduction + spatial
+    transform_func_sr_a: Callable = None
+    transform_func_sr_b: Callable = None
+
+    if k_dim == 4:
+        transform_func_sr_a = shared_16x4_to_local_64x1_layout_A
+        transform_func_sr_b = shared_16x4_to_local_64x1_layout_A
+    elif k_dim == 16:
+        transform_func_sr_a = shared_16x16_to_local_64x4_layout_A
+        transform_func_sr_b = shared_16x16_to_local_64x4_layout_A
+    elif k_dim == 32:
+        transform_func_sr_a = shared_16x32_to_local_64x8_layout_A
+        transform_func_sr_b = shared_16x32_to_local_64x8_layout_A
+    elif k_dim == 64:
+        transform_func_sr_a = shared_16x64_to_local_64x16_layout_A
+        transform_func_sr_b = shared_16x64_to_local_64x16_layout_A
+    else:
+        raise ValueError("k_dim must be 4 or 16 or 32 or 64 currently")
+
+    is_sr_conditions = [False]
+    is_sr_conditions.append(matrix == "A" and not transposed)
+    is_sr_conditions.append(matrix == "B" and transposed)
+    is_sr_axis_order = any(is_sr_conditions)
+
+    micro_size_x, micro_size_y, micro_size_k = get_mma_micro_size(dtype)
+
+    # the layout of mma.sync is row.col.
+    # so the b matrix expected a transposed basic layout
+    transform_func: Callable = None
+    if matrix == "A":
+        transform_func = transform_func_sr_a if is_sr_axis_order else lambda i, j: transform_func_sr_a(
+            j, i)
+        micro_size_s, micro_size_r = micro_size_x, micro_size_k
+    elif matrix == "B":
+        transform_func = transform_func_sr_b if is_sr_axis_order else lambda i, j: transform_func_sr_b(
+            j, i)
+        micro_size_s, micro_size_r = micro_size_k, micro_size_y
+    else:
+        raise ValueError(f"Unsupported matrix {matrix}")
+
+    inverse_mma_load_layout = IndexMap.from_func(transform_func, index_dtype="int32")
+
+    def forward_thread(i: int, j: int) -> int:
+        """
+        Given the row index `i` and column index `j` in the fragment,
+        """
+        lane_id, _ = inverse_mma_load_layout.map_indices([i, j])
+        return lane_id
+
+    def forward_index(i: int, j: int) -> int:
+        """
+        Given the row index `i` and column index `j` in the fragment,
+        """
+        _, local_id = inverse_mma_load_layout.map_indices([i, j])
+        return local_id
+
+    base_fragment = T.Fragment(
+        [micro_size_s, micro_size_r] if is_sr_axis_order else [micro_size_r, micro_size_s],
+        forward_thread_fn=forward_thread,
+        forward_index_fn=forward_index,
+    )
+    return base_fragment
+
+
+block_rows = 2
+block_cols = 2
+warp_rows = 2
+warp_cols = 2
+chunk = 2
+
+from tilelang.tools import plot_layout
+
+# ldmatrix layout 16x16
+base_layout = make_mfma_load_base_layout(dtype="float16", matrix="A", transposed=False)
+print(base_layout)
+plot_layout(base_layout, name="base_layout")
+
+# warp layout 32x32
+warp_layout = base_layout.repeat([warp_rows, warp_cols],
+                                 repeat_on_thread=False,
+                                 lower_dim_first=False)
+print(warp_layout)
+plot_layout(warp_layout, name="warp_layout")
+
+# block layout 64x32
+block_layout = warp_layout.repeat([block_rows, 1], repeat_on_thread=True,
+                                  lower_dim_first=True).replicate(block_cols)
+print(block_layout)
+plot_layout(block_layout, name="block_layout")