mirror of
https://github.com/tile-ai/tilelang.git
synced 2026-10-21 06:40:23 +08:00
Lei Wang
9c95a42dfb
* Remove redundant `map_torch_type` in favor of `T.dtype.as_torch()` `map_torch_type` was a standalone function that converted dtype strings or TVM DataType objects to `torch.dtype`. This was redundant since `T.dtype.as_torch()` (and `T.dtype(x).as_torch()`) already provides the same functionality with better handling (HIP/CUDA awareness, proper dtype lookup tables, etc.). Changes: - Remove `map_torch_type` from `tilelang/utils/tensor.py` and its re-export - Make `T.dtype()` idempotent for existing dtype objects (pass-through) - Add backward-compat for `"e4m3fnuz_float8"` TVM internal name in `as_torch()` - Replace all usages across production code, tests, and examples - Rebuild Cython extension to reflect the import change * Refactor output and accumulation data types in GEMM FP8 example Updated the `out_dtype` and `accum_dtype` variables in the `example_tilelang_gemm_fp8_intrinsic.py` file to use `T.float32` instead of string literals. This change enhances type consistency and aligns with recent updates in type handling.
237 lines
9.4 KiB
Python
237 lines
9.4 KiB
Python
import math
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import torch
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from tilelang import tvm as tvm
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import tilelang.testing
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import tilelang as tl
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import tilelang.language as T
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def matmul_test(M, N, K, block_M, block_N, block_K, dtype=T.float16, accum_dtype=T.float32):
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@T.prim_func
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def main(
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a_ptr: T.ptr,
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b_ptr: T.ptr,
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c_ptr: T.ptr,
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m: T.int32,
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n: T.int32,
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k: T.int32,
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):
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A = T.make_tensor(a_ptr, (m, k), dtype)
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B = T.make_tensor(b_ptr, (k, n), dtype)
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C = T.make_tensor(c_ptr, (m, n), accum_dtype)
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# Initialize Kernel Context
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with T.Kernel(T.ceildiv(N, block_N), T.ceildiv(M, block_M), threads=128) as (bx, by):
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A_shared = T.alloc_shared((block_M, block_K), dtype)
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B_shared = T.alloc_shared((block_N, block_K), dtype)
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C_local = T.alloc_fragment((block_M, block_N), accum_dtype)
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T.clear(C_local)
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for ko in T.Pipelined(T.ceildiv(k, block_K), num_stages=3):
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# Copy tile of A
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T.copy(A[by * block_M, ko * block_K], A_shared)
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T.copy(B[bx * block_N, ko * block_K], B_shared)
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T.gemm(A_shared, B_shared, C_local, transpose_B=True)
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T.copy(C_local, C[by * block_M, bx * block_N])
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return main
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def pointer_table_copy_test(N, dtype=T.float16):
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@T.prim_func
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def main(
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src_ptrs: T.Tensor([1], T.ptr),
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out: T.Tensor([N], dtype),
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):
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with T.Kernel(1, threads=1) as _:
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Src = T.make_tensor(src_ptrs[0], (N,), dtype)
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for i in T.serial(N):
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out[i] = Src[i]
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return main
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def pointer_table_multi_copy_test(G, N, dtype=T.float16):
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@T.prim_func
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def main(
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src_ptrs: T.Tensor([G], T.ptr),
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out: T.Tensor([G, N], dtype),
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):
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with T.Kernel(G, threads=1) as bx:
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Src = T.make_tensor(src_ptrs[bx], (N,), dtype)
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for i in T.serial(N):
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out[bx, i] = Src[i]
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return main
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def pointer_table_grouped_matmul_test(batch_sizes_list, N, K, block_M, block_N, block_K, dtype=T.float16, accum_dtype=T.float32):
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batch_count = len(batch_sizes_list)
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max_M = max(batch_sizes_list)
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total_m_blocks = sum(math.ceil(size / block_M) for size in batch_sizes_list)
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@T.prim_func
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def main(
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a_ptrs: T.Tensor([batch_count], T.ptr),
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b_ptrs: T.Tensor([batch_count], T.ptr),
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c_ptrs: T.Tensor([batch_count], T.ptr),
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batch_tile_offsets: T.Tensor([batch_count], T.int32),
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):
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with T.Kernel(total_m_blocks, T.ceildiv(N, block_N), threads=32) as (bx, by):
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A_shared = T.alloc_shared((block_M, block_K), dtype)
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B_shared = T.alloc_shared((block_K, block_N), dtype)
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C_local = T.alloc_fragment((block_M, block_N), accum_dtype)
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cur_batch_idx = T.alloc_var(dtype=T.int32)
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cur_tile_offset = T.alloc_var(dtype=T.int32)
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cur_batch_idx = 0
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cur_tile_offset = 0
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for i in range(batch_count):
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in_cur_batch_idx = bx >= batch_tile_offsets[i]
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cur_batch_idx = T.if_then_else(in_cur_batch_idx, i, cur_batch_idx)
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cur_tile_offset = T.if_then_else(in_cur_batch_idx, batch_tile_offsets[i], cur_tile_offset)
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m_start = (bx - cur_tile_offset) * block_M
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A = T.make_tensor(a_ptrs[cur_batch_idx], (max_M, K), dtype)
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B = T.make_tensor(b_ptrs[cur_batch_idx], (K, N), dtype)
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C = T.make_tensor(c_ptrs[cur_batch_idx], (max_M, N), dtype)
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T.clear(C_local)
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for ko in T.Pipelined(T.ceildiv(K, block_K), num_stages=1):
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T.copy(A[m_start, ko * block_K], A_shared)
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T.copy(B[ko * block_K, by * block_N], B_shared)
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T.gemm(A_shared, B_shared, C_local)
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T.copy(C_local, C[m_start, by * block_N])
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return main
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def run_matmul(M, N, K, block_M, block_N, block_K, dtype=T.float16, accum_dtype=T.float32):
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program = matmul_test(M, N, K, block_M, block_N, block_K, dtype, accum_dtype)
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cython_jit_kernel = tl.compile(program, execution_backend="cython")
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ffi_jit_kernel = tl.compile(program, execution_backend="tvm_ffi")
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def ref_program(a, b):
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return (a @ b.T).to(torch.float32)
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a = torch.randn(M, K, device="cuda", dtype=dtype.as_torch())
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b = torch.randn(N, K, device="cuda", dtype=dtype.as_torch())
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ffi_c = torch.zeros(M, N, device="cuda", dtype=accum_dtype.as_torch())
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cython_c = torch.zeros(M, N, device="cuda", dtype=accum_dtype.as_torch())
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ffi_jit_kernel(a, b, ffi_c, M, N, K)
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cython_jit_kernel(a.data_ptr(), b.data_ptr(), cython_c.data_ptr(), M, N, K)
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torch.testing.assert_close(ffi_c, ref_program(a, b), atol=1e-2, rtol=1e-2)
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torch.testing.assert_close(cython_c, ffi_c, atol=1e-2, rtol=1e-2)
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def run_pointer_table_copy(N, dtype=T.float16):
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program = pointer_table_copy_test(N, dtype)
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cython_jit_kernel = tl.compile(program, execution_backend="cython")
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ffi_jit_kernel = tl.compile(program, execution_backend="tvm_ffi")
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src = torch.randn(N, device="cuda", dtype=dtype.as_torch())
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src_ptrs = torch.tensor([src.data_ptr()], device="cuda", dtype=torch.int64)
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ffi_out = torch.empty(N, device="cuda", dtype=dtype.as_torch())
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cython_out = torch.empty(N, device="cuda", dtype=dtype.as_torch())
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ffi_jit_kernel(src_ptrs, ffi_out)
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cython_jit_kernel(src_ptrs, cython_out)
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torch.testing.assert_close(ffi_out, src)
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torch.testing.assert_close(cython_out, src)
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torch.testing.assert_close(cython_out, ffi_out)
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def run_pointer_table_multi_copy(G, N, dtype=T.float16):
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program = pointer_table_multi_copy_test(G, N, dtype)
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cython_jit_kernel = tl.compile(program, execution_backend="cython")
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ffi_jit_kernel = tl.compile(program, execution_backend="tvm_ffi")
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srcs = [torch.randn(N, device="cuda", dtype=dtype.as_torch()) for _ in range(G)]
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src_ptrs = torch.tensor([src.data_ptr() for src in srcs], device="cuda", dtype=torch.int64)
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ref = torch.stack(srcs, dim=0)
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ffi_out = torch.empty((G, N), device="cuda", dtype=dtype.as_torch())
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cython_out = torch.empty((G, N), device="cuda", dtype=dtype.as_torch())
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ffi_jit_kernel(src_ptrs, ffi_out)
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cython_jit_kernel(src_ptrs, cython_out)
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torch.testing.assert_close(ffi_out, ref)
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torch.testing.assert_close(cython_out, ref)
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torch.testing.assert_close(cython_out, ffi_out)
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def run_pointer_table_grouped_matmul(batch_sizes_list, N, K, block_M, block_N, block_K, dtype=T.float16, accum_dtype=T.float32):
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program = pointer_table_grouped_matmul_test(batch_sizes_list, N, K, block_M, block_N, block_K, dtype, accum_dtype)
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compile_kwargs = {"pass_configs": {"tl.disable_warp_specialized": True}}
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cython_jit_kernel = tl.compile(program, execution_backend="cython", **compile_kwargs)
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ffi_jit_kernel = tl.compile(program, execution_backend="tvm_ffi", **compile_kwargs)
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device = "cuda"
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torch_dtype = dtype.as_torch()
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torch_accum_dtype = accum_dtype.as_torch()
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max_M = max(batch_sizes_list)
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batch_tile_offsets = [0]
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for size in batch_sizes_list[:-1]:
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batch_tile_offsets.append(batch_tile_offsets[-1] + math.ceil(size / block_M))
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batch_tile_offsets_t = torch.tensor(batch_tile_offsets, device=device, dtype=torch.int32)
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a_exact = [torch.randn(size, K, device=device, dtype=torch_dtype) for size in batch_sizes_list]
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b_list = [torch.randn(K, N, device=device, dtype=torch_dtype) for _ in batch_sizes_list]
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ref = [(a @ b).to(torch_accum_dtype) for a, b in zip(a_exact, b_list)]
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def build_backend_buffers():
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a_list = [torch.zeros(max_M, K, device=device, dtype=torch_dtype) for _ in batch_sizes_list]
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c_list = [torch.empty(max_M, N, device=device, dtype=torch_dtype) for _ in batch_sizes_list]
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for buf, src in zip(a_list, a_exact):
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buf[: src.shape[0]].copy_(src)
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return (
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a_list,
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c_list,
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torch.tensor([buf.data_ptr() for buf in a_list], device=device, dtype=torch.int64),
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torch.tensor([buf.data_ptr() for buf in b_list], device=device, dtype=torch.int64),
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torch.tensor([buf.data_ptr() for buf in c_list], device=device, dtype=torch.int64),
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)
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ffi_a_list, ffi_c_list, ffi_a_ptrs, ffi_b_ptrs, ffi_c_ptrs = build_backend_buffers()
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cython_a_list, cython_c_list, cython_a_ptrs, cython_b_ptrs, cython_c_ptrs = build_backend_buffers()
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ffi_jit_kernel(ffi_a_ptrs, ffi_b_ptrs, ffi_c_ptrs, batch_tile_offsets_t)
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cython_jit_kernel(cython_a_ptrs, cython_b_ptrs, cython_c_ptrs, batch_tile_offsets_t)
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for out, expected, size in zip(ffi_c_list, ref, batch_sizes_list):
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torch.testing.assert_close(out[:size].to(torch_accum_dtype), expected, atol=1e-2, rtol=1e-2)
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for out, expected, size in zip(cython_c_list, ref, batch_sizes_list):
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torch.testing.assert_close(out[:size].to(torch_accum_dtype), expected, atol=1e-2, rtol=1e-2)
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def test_matmul():
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run_matmul(256, 256, 256, 64, 64, 32)
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def test_pointer_table_annotation_lowers_to_int64_buffer():
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program = pointer_table_multi_copy_test(4, 8)
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src_ptrs = program.buffer_map[program.params[0]]
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assert src_ptrs.dtype == "int64"
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assert [int(dim) for dim in src_ptrs.shape] == [4]
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def test_pointer_table_copy():
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run_pointer_table_copy(64)
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def test_pointer_table_multi_copy():
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run_pointer_table_multi_copy(2, 64)
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@tilelang.testing.requires_cuda
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def test_pointer_table_grouped_matmul():
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run_pointer_table_grouped_matmul([8, 12, 17], 32, 32, 16, 16, 16)
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if __name__ == "__main__":
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tilelang.testing.main()
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