mirror of
https://github.com/tile-ai/tilelang.git
synced 2026-10-24 07:04:00 +08:00
Lei Wang
92121fc668
* [Refactor] Update condition for benchmarking in example_gemv.py and simplify cached library path handling in sparse.py * [Enhancement] Extend support for float8 data types in GEMM operations - Updated GEMM operations to recognize additional float8 data types: `float8_e4m3fn` and `float8_e5m2fnuz`. - Refactored condition checks in `checkWgmma` methods to simplify float8 type handling. - Adjusted test cases to ensure compatibility with the new float8 types in tile language examples. * lint fix
230 lines
6.9 KiB
Python
230 lines
6.9 KiB
Python
import torch
|
|
import torch.backends
|
|
from tilelang import tvm as tvm
|
|
import tilelang.testing
|
|
from tvm import DataType
|
|
import tilelang.language as T
|
|
from tilelang.intrinsics import get_swizzle_layout
|
|
from tilelang.intrinsics.mma_macro_generator import (
|
|
TensorCoreIntrinEmitter,)
|
|
from tilelang.transform import simplify_prim_func
|
|
from tilelang.utils.tensor import map_torch_type
|
|
|
|
tilelang.testing.set_random_seed(0)
|
|
|
|
|
|
def make_swizzle_layout(shared_buf):
|
|
dtype = shared_buf.dtype
|
|
shape = shared_buf.shape
|
|
|
|
can_swizzle = shape[-1] * DataType(dtype).bits == 512
|
|
if not can_swizzle:
|
|
return T.Layout(shape, lambda *args: args)
|
|
|
|
def transform_func(i, j):
|
|
new_warp_i, new_warp_j = get_swizzle_layout(i, j, shape[-1], dtype)
|
|
return [new_warp_i, new_warp_j]
|
|
|
|
return T.Layout(shape, transform_func)
|
|
|
|
|
|
@simplify_prim_func
|
|
def tl_matmul(
|
|
M,
|
|
N,
|
|
K,
|
|
in_dtype,
|
|
out_dtype,
|
|
accum_dtype,
|
|
):
|
|
assert in_dtype in [
|
|
"float16",
|
|
"float8_e4m3",
|
|
"float8_e5m2",
|
|
"int8",
|
|
], "Currently only float16 and int8 are supported"
|
|
assert out_dtype in [
|
|
"float16",
|
|
"float32",
|
|
"int32",
|
|
], "Currently only float16, float32 and int32 are supported"
|
|
|
|
micro_size_x = micro_size_y = micro_size_k = 16
|
|
|
|
is_float8 = in_dtype in [
|
|
"float8_e4m3",
|
|
"float8_e5m2",
|
|
"float8_e4m3fn",
|
|
"float8_e5m2fnuz",
|
|
]
|
|
if out_dtype == "int32" or is_float8:
|
|
micro_size_k = 32
|
|
|
|
# This is a debug config
|
|
block_row_warps = 2
|
|
block_col_warps = 2
|
|
warp_row_tiles = 32
|
|
warp_col_tiles = 32
|
|
chunk = 32 if in_dtype == "float16" else 64
|
|
shared_scope = "shared.dyn"
|
|
|
|
# Pipeline Stage
|
|
stage = 2
|
|
|
|
block_M = block_row_warps * warp_row_tiles
|
|
block_N = block_col_warps * warp_col_tiles
|
|
block_K = chunk
|
|
|
|
A_shape = (M, K)
|
|
B_shape = (N, K)
|
|
A_shared_shape = (block_M, block_K)
|
|
B_shared_shape = (block_N, block_K)
|
|
C_shared_shape = (
|
|
block_M // micro_size_x,
|
|
block_N // micro_size_y,
|
|
micro_size_x,
|
|
micro_size_y,
|
|
)
|
|
|
|
warp_size = 32
|
|
threads = warp_size * (block_row_warps * block_col_warps)
|
|
local_size_a = (micro_size_x * micro_size_k) // warp_size
|
|
local_size_b = (micro_size_y * micro_size_k) // warp_size
|
|
local_size_c = (micro_size_x * micro_size_y) // warp_size
|
|
warp_rows = warp_row_tiles // micro_size_x
|
|
warp_cols = warp_col_tiles // micro_size_y
|
|
|
|
# MMA Wrapper to Auto Generate Code for MMA
|
|
mma_emitter = TensorCoreIntrinEmitter(
|
|
a_dtype=in_dtype,
|
|
b_dtype=in_dtype,
|
|
accum_dtype=accum_dtype,
|
|
a_transposed=False,
|
|
b_transposed=True,
|
|
block_row_warps=block_row_warps,
|
|
block_col_warps=block_col_warps,
|
|
warp_row_tiles=warp_row_tiles,
|
|
warp_col_tiles=warp_col_tiles,
|
|
chunk=chunk,
|
|
)
|
|
|
|
@T.prim_func
|
|
def main(
|
|
A: T.Tensor(A_shape, in_dtype),
|
|
B: T.Tensor(B_shape, in_dtype),
|
|
C: T.Tensor((M, N), out_dtype),
|
|
):
|
|
with T.Kernel(T.ceildiv(N, block_N), T.ceildiv(M, block_M), threads=threads) as (bx, by):
|
|
|
|
A_shared = T.alloc_shared(A_shared_shape, in_dtype, scope=shared_scope)
|
|
B_shared = T.alloc_shared(B_shared_shape, in_dtype, scope=shared_scope)
|
|
C_shared = T.alloc_shared(C_shared_shape, out_dtype, scope=shared_scope)
|
|
A_local = T.alloc_local((warp_rows * local_size_a), in_dtype)
|
|
B_local = T.alloc_local((warp_cols * local_size_b), in_dtype)
|
|
C_local = T.alloc_local((warp_rows * warp_cols * local_size_c), accum_dtype)
|
|
|
|
T.annotate_layout({
|
|
A_shared: make_swizzle_layout(A_shared),
|
|
B_shared: make_swizzle_layout(B_shared),
|
|
})
|
|
|
|
# Improve L2 Cache
|
|
T.use_swizzle(panel_size=10)
|
|
|
|
T.clear(C_local)
|
|
|
|
for ko in T.Pipelined((K // block_K), num_stages=stage):
|
|
|
|
# Load A into shared memory
|
|
for i, k in T.Parallel(block_M, block_K):
|
|
A_shared[i, k] = A[by * block_M + i, ko * block_K + k]
|
|
|
|
# Load B into shared memory
|
|
for j, k in T.Parallel(block_N, block_K):
|
|
B_shared[j, k] = B[bx * block_N + j, ko * block_K + k]
|
|
|
|
for ki in T.serial(0, (block_K // micro_size_k)):
|
|
|
|
# Load A into fragment
|
|
mma_emitter.ldmatrix_a(
|
|
A_local,
|
|
A_shared,
|
|
ki,
|
|
)
|
|
|
|
# Load B into fragment
|
|
mma_emitter.ldmatrix_b(
|
|
B_local,
|
|
B_shared,
|
|
ki,
|
|
)
|
|
|
|
# Perform Matrix Multiplication
|
|
mma_emitter.mma(A_local, B_local, C_local)
|
|
|
|
# Perform STMatrix
|
|
mma_emitter.stmatrix(
|
|
C_local,
|
|
C_shared,
|
|
)
|
|
|
|
# Store shared into global
|
|
for i, j in T.Parallel(block_M, block_N):
|
|
C[by * block_M + i, bx * block_N + j] = C_shared[
|
|
i // micro_size_x,
|
|
j // micro_size_y,
|
|
i % micro_size_x,
|
|
j % micro_size_y,
|
|
]
|
|
|
|
return main
|
|
|
|
|
|
def assert_tl_matmul_correctness(M, N, K, in_dtype, out_dtype, accum_dtype):
|
|
matmul = tl_matmul(M, N, K, in_dtype, out_dtype, accum_dtype)
|
|
kernel = tilelang.compile(matmul, out_idx=[2])
|
|
profiler = kernel.get_profiler()
|
|
|
|
src_code = kernel.get_kernel_source()
|
|
print(src_code)
|
|
# src_code is the generated cuda source
|
|
assert src_code is not None
|
|
|
|
in_dtype = map_torch_type(in_dtype)
|
|
out_dtype = map_torch_type(out_dtype)
|
|
accum_dtype = map_torch_type(accum_dtype)
|
|
|
|
if in_dtype in {torch.int8, torch.int32}:
|
|
A = torch.randint(-128, 128, (M, K), dtype=torch.int8).to(in_dtype).cuda()
|
|
B = torch.randint(-128, 128, (N, K), dtype=torch.int8).to(in_dtype).cuda()
|
|
elif in_dtype in {torch.float8_e4m3fn, torch.float8_e5m2}:
|
|
A = torch.randn(M, K).to(in_dtype).cuda()
|
|
B = torch.randn(N, K).to(in_dtype).cuda()
|
|
else:
|
|
A = torch.randn(M, K).to(in_dtype).cuda() - 0.5
|
|
B = torch.randn(N, K).to(in_dtype).cuda() - 0.5
|
|
|
|
C = kernel(A, B)
|
|
|
|
latency = profiler.do_bench()
|
|
|
|
# Ensure that the latency is not None
|
|
assert latency is not None
|
|
|
|
# Get Reference Result
|
|
ref_c = torch.matmul(A.to(accum_dtype), B.T.to(accum_dtype)).to(out_dtype)
|
|
print(C)
|
|
print(ref_c)
|
|
torch.testing.assert_close(C, ref_c, rtol=1e-2, atol=1e-2)
|
|
|
|
|
|
@tilelang.testing.requires_cuda
|
|
@tilelang.testing.requires_cuda_compute_version(8, 9)
|
|
def test_assert_tl_matmul():
|
|
assert_tl_matmul_correctness(128, 128, 128, "float8_e4m3", "float32", "float32")
|
|
assert_tl_matmul_correctness(128, 128, 128, "float8_e5m2", "float32", "float32")
|
|
|
|
|
|
if __name__ == "__main__":
|
|
tilelang.testing.main()
|