File size: 3,751 Bytes
a3f2e4d | 1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39 40 41 42 43 44 45 46 47 48 49 50 51 52 53 54 55 56 57 58 59 60 61 62 63 64 65 66 67 68 69 70 71 72 73 74 75 76 77 78 79 80 81 82 83 84 85 86 87 88 89 90 91 92 93 | # Copyright (c) Microsoft Corporation.
# Licensed under the MIT License.
from tvm import DataType
from typing import Literal
from .mma_layout import (
ldmatrix_32x8_to_shared_16x16_layout,
ldmatrix_trans_32x8_to_shared_16x16_layout,
ldmatrix_16x32_to_shared_16x32_layout_a,
ldmatrix_16x32_to_shared_16x32_layout_b,
mma_store_32x8_to_shared_16x16_layout,
)
from .mfma_layout import (thread_id_shared_access_64x4_to_16x16_layout_C_n_m)
from .mma_layout import get_swizzle_layout # noqa: F401
from .mma_layout import make_mma_swizzle_layout # noqa: F401
from .mfma_layout import make_mfma_swizzle_layout # noqa: F401
# the original implementation and insight is from the following code snippet
# 3rdparty/tvm/python/tvm/tir/tensor_intrin/cuda.py#get_ldmatrix_intrin
def get_ldmatrix_offset(
matrix: Literal["A", "B"],
row_idx,
col_idx,
stride,
dtype: Literal["float16", "int8"] = "float16",
transposed: bool = False,
):
assert matrix in ["A", "B"], "matrix should be either A or B"
dtype_bits = DataType(dtype).bits
if dtype_bits == 16:
transform_func = ldmatrix_32x8_to_shared_16x16_layout
transform_func_trans = ldmatrix_trans_32x8_to_shared_16x16_layout
if transposed:
new_row_idx, new_col_idx = transform_func_trans(row_idx, col_idx)
return new_row_idx * stride + new_col_idx
else:
new_row_idx, new_col_idx = transform_func(row_idx, col_idx)
return new_row_idx * stride + new_col_idx
elif dtype_bits == 8:
if matrix == "B" and transposed:
transform_func = ldmatrix_16x32_to_shared_16x32_layout_b
new_row_idx, new_col_idx = transform_func(row_idx, col_idx)
return new_row_idx * stride + new_col_idx
elif matrix == "A" and not transposed:
transform_func = ldmatrix_16x32_to_shared_16x32_layout_a
new_row_idx, new_col_idx = transform_func(row_idx, col_idx)
return new_row_idx * stride + new_col_idx
else:
raise ValueError("ldmatrix only supports B transposed and A non-transposed for int8")
else:
raise ValueError(f"Unsupported dtype {dtype}")
def mma_store_index_map(*args, **kwargs):
return mma_store_32x8_to_shared_16x16_layout(*args, **kwargs)
def mfma_store_index_map(*args, **kwargs):
return thread_id_shared_access_64x4_to_16x16_layout_C_n_m(*args, **kwargs)
def get_mma_micro_size(dtype: Literal["float16", "int8"]):
# TODO(lei): FP8 related precision support.
# Basic Tensor Core Matrix Multiply operation Unit
micro_size_x = micro_size_y = 16
micro_size_k = 16
if dtype == "int8":
micro_size_k = 32
return micro_size_x, micro_size_y, micro_size_k
def index_to_coordinates(index, shape):
'''
General Implementation of:
vjj = index % (micro_size_k // num_elems_per_byte)
coordinates[-1] = index % shape[-1];
vii = index // (micro_size_k // num_elems_per_byte) % micro_size_y
index = index // shape[-1]; coordinates[-2] = index % shape[-2];
vj = index // (micro_size_k // num_elems_per_byte * micro_size_y) % block_K // (micro_size_k // num_elems_per_byte)
index = index // shape[-2]; coordinates[-3] = index % shape[-3];
vi = index // (micro_size_k // num_elems_per_byte * micro_size_y * (block_K // (micro_size_k // num_elems_per_byte))) % block_N // micro_size_y
index = index // shape[-3]; coordinates[-4] = index % shape[-4];
'''
coordinates = []
dims = len(shape)
for i in range(dims):
coordinates.append(index % shape[dims - i - 1])
index = index // shape[dims - i - 1]
coordinates.reverse()
return coordinates
|