mirror of
https://github.com/tile-ai/tilelang.git
synced 2026-10-26 07:22:54 +08:00
e9a608e222
* fix CI bugs in hopper * lint fix * Update bulk_copy.cc * Refactor bulk copy logic in LowerBulkCopy function - Removed unnecessary blank lines for improved code readability. - Enhanced stride validation by checking for null pointers in global stride calculations, ensuring robustness against symbolic strides. - Updated pass configuration handling in dynamic tile language tests to streamline dynamic alignment and TMA lower pass settings. * test fix * ci fix * Update flash-attention dependencies and clean up example code - Downgraded `flash-attn` dependency version in `requirements-test.txt` to `<=2.2.0`. - Removed unused imports and commented-out code in various example files to enhance readability and maintainability. - Updated the `flashattn` function signature to include default parameters for `block_M`, `block_N`, `num_stages`, and `threads`. - Cleaned up the `example_mha_fwd_varlen.py` and `example_mha_bwd_wgmma_pipelined.py` files by removing unnecessary comments and improving code clarity. - Deleted the `example_mha_inference.py` file as it is no longer needed. * Update CI workflow to remove `--user` flag from pip install commands - Removed the `--user` flag from the pip install commands in both the development and testing sections of the CI workflow to ensure proper installation of dependencies in the virtual environment. * Update CI workflow to include `--no-user` flag in pip install commands - Added the `--no-user` flag to the pip install commands in both the development and testing sections of the CI workflow to ensure dependencies are installed correctly within the virtual environment. * Update CI workflow to include `--no-user` flag in pip install command for wheel mode - Added the `--no-user` flag to the pip install command in the wheel mode section of the CI workflow to ensure dependencies are installed correctly within the virtual environment. * test fix * avoid conflict with system environments * test fix * add commnets --------- Co-authored-by: Lei Wang <34334180+LeiWang1999@users.noreply.github.com> Co-authored-by: LeiWang1999 <leiwang1999@outlook.com>
518 lines
15 KiB
Python
518 lines
15 KiB
Python
import torch
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import torch.backends
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from tilelang import tvm as tvm
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import tilelang.testing
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from tvm import DataType
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import tilelang.language as T
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from tilelang.intrinsics.utils import get_swizzle_layout
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from tilelang.intrinsics.mma_macro_generator import (TensorCoreIntrinEmitter)
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tilelang.testing.set_random_seed(0)
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def make_swizzle_layout(shared_buf):
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dtype = shared_buf.dtype
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shape = shared_buf.shape
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can_swizzle = shape[-1] * DataType(dtype).bits == 512
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if not can_swizzle:
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return T.Layout(shape, lambda *args: args)
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def transform_func(i, j):
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new_warp_i, new_warp_j = get_swizzle_layout(i, j, shape[-1], dtype)
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return [new_warp_i, new_warp_j]
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return T.Layout(shape, transform_func)
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def tl_matmul_macro(
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N,
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K,
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in_dtype,
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out_dtype,
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accum_dtype,
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):
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assert in_dtype in [
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"float16",
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"int8",
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], "Currently only float16 and int8 are supported"
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assert out_dtype in [
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"float16",
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"float32",
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"int32",
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], "Currently only float16, float32 and int32 are supported"
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micro_size_x = micro_size_y = micro_size_k = 16
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if out_dtype == "int32":
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micro_size_k = 32
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# This is a debug config
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block_row_warps = 1
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block_col_warps = 1
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warp_row_tiles = 16
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warp_col_tiles = 16
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chunk = 32 if in_dtype == "float16" else 64
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shared_scope = "shared.dyn"
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# Pipeline Stage
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stage = 2
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block_M = block_row_warps * warp_row_tiles
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block_N = block_col_warps * warp_col_tiles
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block_K = chunk
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M = tvm.te.var("m")
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A_shape = (M, K)
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B_shape = (N, K)
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A_shared_shape = (block_M, block_K)
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B_shared_shape = (block_N, block_K)
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C_shared_shape = (
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block_M // micro_size_x,
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block_N // micro_size_y,
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micro_size_x,
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micro_size_y,
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)
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warp_size = 32
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threads = warp_size * (block_row_warps * block_col_warps)
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local_size = (micro_size_x * micro_size_y) // warp_size
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warp_rows = warp_row_tiles // micro_size_x
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warp_cols = warp_col_tiles // micro_size_y
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# MMA Wrapper to Auto Generate Code for MMA
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mma_emitter = TensorCoreIntrinEmitter(
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a_dtype=in_dtype,
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b_dtype=in_dtype,
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accum_dtype=accum_dtype,
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a_transposed=False,
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b_transposed=True,
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block_row_warps=block_row_warps,
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block_col_warps=block_col_warps,
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warp_row_tiles=warp_row_tiles,
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warp_col_tiles=warp_col_tiles,
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chunk=chunk,
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)
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@T.prim_func
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def main(
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A: T.Tensor(A_shape, in_dtype),
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B: T.Tensor(B_shape, in_dtype),
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C: T.Tensor((M, N), out_dtype),
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):
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with T.Kernel(T.ceildiv(N, block_N), T.ceildiv(M, block_M), threads=threads) as (bx, by):
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A_shared = T.alloc_shared(A_shared_shape, in_dtype, scope=shared_scope)
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B_shared = T.alloc_shared(B_shared_shape, in_dtype, scope=shared_scope)
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C_shared = T.alloc_shared(C_shared_shape, out_dtype, scope=shared_scope)
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A_local = T.alloc_local((warp_rows * local_size), in_dtype)
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B_local = T.alloc_local((warp_cols * local_size), in_dtype)
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C_local = T.alloc_local((warp_rows * warp_cols * local_size), accum_dtype)
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T.annotate_layout({
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A_shared: make_swizzle_layout(A_shared),
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B_shared: make_swizzle_layout(B_shared),
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})
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# Improve L2 Cache
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T.use_swizzle(panel_size=10)
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T.clear(C_local)
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for ko in T.Pipelined((K // block_K), num_stages=stage):
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# Load A into shared memory
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for i, k in T.Parallel(block_M, block_K):
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A_shared[i, k] = A[by * block_M + i, ko * block_K + k]
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# Load B into shared memory
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for j, k in T.Parallel(block_N, block_K):
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B_shared[j, k] = B[bx * block_N + j, ko * block_K + k]
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for ki in T.serial(0, (block_K // micro_size_k)):
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# Load A into fragment
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mma_emitter.ldmatrix_a(
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A_local,
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A_shared,
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ki,
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)
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# Load B into fragment
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mma_emitter.ldmatrix_b(
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B_local,
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B_shared,
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ki,
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)
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# Perform Matrix Multiplication
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mma_emitter.mma(A_local, B_local, C_local)
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# Perform STMatrix
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mma_emitter.stmatrix(
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C_local,
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C_shared,
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)
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# Store shared into global
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for i, j in T.Parallel(block_M, block_N):
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C[by * block_M + i, bx * block_N + j] = C_shared[
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i // micro_size_x,
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j // micro_size_y,
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i % micro_size_x,
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j % micro_size_y,
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]
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return main
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def assert_tl_matmul_macro_correctness(M, N, K, in_dtype, out_dtype, accum_dtype):
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matmul = tl_matmul_macro(N, K, in_dtype, out_dtype, accum_dtype)
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kernel = tilelang.compile(matmul, out_idx=[2])
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src_code = kernel.get_kernel_source()
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# src_code is the generated cuda source
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assert src_code is not None
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A = torch.rand(M, K, device="cuda", dtype=getattr(torch, in_dtype))
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B = torch.rand(N, K, device="cuda", dtype=getattr(torch, in_dtype))
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C = torch.zeros(M, N, device="cuda", dtype=getattr(torch, accum_dtype))
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C = kernel(A, B)
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# Get Reference Result
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ref_c = torch.matmul(A, B.T).to(getattr(torch, accum_dtype))
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torch.testing.assert_close(C, ref_c, rtol=1e-2, atol=1e-2)
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def tl_matmul_block(
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N,
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K,
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block_M,
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block_N,
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block_K,
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trans_A,
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trans_B,
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in_dtype,
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out_dtype,
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accum_dtype,
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num_stages,
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threads,
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):
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M = tvm.te.var("m")
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A_shape = (K, M) if trans_A else (M, K)
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B_shape = (N, K) if trans_B else (K, N)
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A_shared_shape = (block_K, block_M) if trans_A else (block_M, block_K)
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B_shared_shape = (block_N, block_K) if trans_B else (block_K, block_N)
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@T.prim_func
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def main(A: T.Tensor(A_shape, in_dtype), B: T.Tensor(B_shape, in_dtype), C: T.Tensor(
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(M, N), out_dtype)):
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with T.Kernel(T.ceildiv(N, block_N), T.ceildiv(M, block_M), threads=threads) as (bx, by):
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A_shared = T.alloc_shared(A_shared_shape, in_dtype)
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B_shared = T.alloc_shared(B_shared_shape, in_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 k in T.Pipelined(T.ceildiv(K, block_K), num_stages=num_stages):
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if trans_A:
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T.copy(A[k * block_K, by * block_M], A_shared)
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else:
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T.copy(A[by * block_M, k * block_K], A_shared)
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if trans_B:
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T.copy(B[bx * block_N, k * block_K], B_shared)
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else:
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T.copy(B[k * block_K, bx * block_N], B_shared)
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T.gemm(A_shared, B_shared, C_local, trans_A, trans_B)
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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 assert_tl_matmul_block_correctness(
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M,
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N,
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K,
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trans_A,
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trans_B,
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in_dtype,
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out_dtype,
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dtypeAccum,
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block_M,
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block_N,
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block_K,
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num_stages=3,
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num_threads=128,
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):
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program = tl_matmul_block(
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N,
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K,
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block_M,
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block_N,
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block_K,
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trans_A,
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trans_B,
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in_dtype,
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out_dtype,
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dtypeAccum,
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num_stages,
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num_threads,
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)
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kernel = tilelang.compile(program, out_idx=[2])
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A = torch.rand(M, K, device="cuda", dtype=getattr(torch, in_dtype))
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B = torch.rand(N, K, device="cuda", dtype=getattr(torch, in_dtype))
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C = torch.zeros(M, N, device="cuda", dtype=getattr(torch, out_dtype))
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C = kernel(A, B)
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def ref_program(A, B):
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import torch
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if trans_A:
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A = A.T
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if trans_B:
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B = B.T
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C = torch.matmul(A.to(torch.float), B.to(torch.float))
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C = C.to(torch.__getattribute__(out_dtype))
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return C
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# Get Reference Result
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ref_c = ref_program(A, B)
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torch.testing.assert_close(C, ref_c, rtol=1e-2, atol=1e-2)
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def tl_matmul_block_all_dynamic(
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block_M,
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block_N,
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block_K,
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trans_A,
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trans_B,
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in_dtype,
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out_dtype,
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accum_dtype,
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num_stages,
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threads,
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):
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M = tvm.te.var("m")
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N = tvm.te.var("n")
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K = tvm.te.var("k")
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A_shape = (K, M) if trans_A else (M, K)
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B_shape = (N, K) if trans_B else (K, N)
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A_shared_shape = (block_K, block_M) if trans_A else (block_M, block_K)
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B_shared_shape = (block_N, block_K) if trans_B else (block_K, block_N)
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@T.prim_func
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def main(A: T.Tensor(A_shape, in_dtype), B: T.Tensor(B_shape, in_dtype), C: T.Tensor(
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(M, N), out_dtype)):
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with T.Kernel(T.ceildiv(N, block_N), T.ceildiv(M, block_M), threads=threads) as (bx, by):
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A_shared = T.alloc_shared(A_shared_shape, in_dtype)
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B_shared = T.alloc_shared(B_shared_shape, in_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 k in T.Pipelined(T.ceildiv(K, block_K), num_stages=num_stages):
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if trans_A:
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T.copy(A[k * block_K, by * block_M], A_shared)
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else:
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T.copy(A[by * block_M, k * block_K], A_shared)
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if trans_B:
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T.copy(B[bx * block_N, k * block_K], B_shared)
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else:
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T.copy(B[k * block_K, bx * block_N], B_shared)
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T.gemm(A_shared, B_shared, C_local, trans_A, trans_B)
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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 assert_tl_matmul_block_all_dynamic_correctness(
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M,
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N,
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K,
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trans_A,
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trans_B,
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in_dtype,
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out_dtype,
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dtypeAccum,
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block_M,
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block_N,
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block_K,
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num_stages=3,
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num_threads=128,
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):
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program = tl_matmul_block_all_dynamic(
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block_M,
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block_N,
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block_K,
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trans_A,
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trans_B,
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in_dtype,
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out_dtype,
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dtypeAccum,
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num_stages,
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num_threads,
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)
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kernel = tilelang.compile(program)
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if trans_A:
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A = torch.rand(K, M, device="cuda", dtype=getattr(torch, in_dtype))
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else:
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A = torch.rand(M, K, device="cuda", dtype=getattr(torch, in_dtype))
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if trans_B:
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B = torch.rand(N, K, device="cuda", dtype=getattr(torch, in_dtype))
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else:
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B = torch.rand(K, N, device="cuda", dtype=getattr(torch, in_dtype))
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C = torch.zeros(M, N, device="cuda", dtype=getattr(torch, out_dtype))
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kernel(A, B, C)
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def ref_program(A, B):
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import torch
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if trans_A:
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A = A.T
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if trans_B:
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B = B.T
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C = torch.matmul(A.to(torch.float), B.to(torch.float))
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C = C.to(torch.__getattribute__(out_dtype))
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return C
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# Get Reference Result
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ref_c = ref_program(A, B)
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torch.testing.assert_close(C, ref_c, rtol=1e-2, atol=1e-2)
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def assert_tl_matmul_block_all_dynamic_correctness_with_pass_config(
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M,
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N,
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K,
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trans_A,
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trans_B,
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in_dtype,
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out_dtype,
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dtypeAccum,
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block_M,
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block_N,
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block_K,
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num_stages=3,
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num_threads=128,
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dynamic_alignment=8,
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):
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program = tl_matmul_block_all_dynamic(
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block_M,
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block_N,
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block_K,
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trans_A,
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trans_B,
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in_dtype,
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out_dtype,
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dtypeAccum,
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num_stages,
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num_threads,
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)
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pass_configs = {
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tilelang.PassConfigKey.TL_DISABLE_DYNAMIC_TAIL_SPLIT: dynamic_alignment != 0,
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tilelang.PassConfigKey.TL_DYNAMIC_ALIGNMENT: dynamic_alignment
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}
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if M % 64 == 0 or N % 64 == 0 or K % 64 != 0:
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# workaround for hopper tma lower pass
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pass_configs[tilelang.PassConfigKey.TL_DISABLE_TMA_LOWER] = True
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pass_configs[tilelang.PassConfigKey.TL_DISABLE_WARP_SPECIALIZED] = True
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kernel = tilelang.compile(program, pass_configs=pass_configs)
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if trans_A:
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A = torch.rand(K, M, device="cuda", dtype=getattr(torch, in_dtype))
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else:
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A = torch.rand(M, K, device="cuda", dtype=getattr(torch, in_dtype))
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if trans_B:
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B = torch.rand(N, K, device="cuda", dtype=getattr(torch, in_dtype))
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else:
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B = torch.rand(K, N, device="cuda", dtype=getattr(torch, in_dtype))
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C = torch.zeros(M, N, device="cuda", dtype=getattr(torch, out_dtype))
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kernel(A, B, C)
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def ref_program(A, B):
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import torch
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if trans_A:
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A = A.T
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if trans_B:
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B = B.T
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C = torch.matmul(A.to(torch.float), B.to(torch.float))
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C = C.to(torch.__getattribute__(out_dtype))
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return C
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# Get Reference Result
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ref_c = ref_program(A, B)
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torch.testing.assert_close(C, ref_c, rtol=1e-2, atol=1e-2)
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def test_assert_tl_matmul_macro():
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assert_tl_matmul_macro_correctness(128, 128, 128, "float16", "float16", "float16")
|
|
assert_tl_matmul_macro_correctness(66, 128, 128, "float16", "float16", "float16")
|
|
assert_tl_matmul_macro_correctness(32, 128, 128, "float16", "float16", "float16")
|
|
|
|
|
|
def test_assert_tl_matmul_block():
|
|
assert_tl_matmul_block_correctness(128, 128, 128, False, False, "float16", "float16", "float16",
|
|
64, 64, 32)
|
|
assert_tl_matmul_block_correctness(67, 128, 128, False, False, "float16", "float16", "float16",
|
|
64, 64, 32)
|
|
assert_tl_matmul_block_correctness(36, 128, 128, False, False, "float16", "float16", "float16",
|
|
64, 64, 32)
|
|
|
|
|
|
def test_assert_tl_matmul_block_all_dynamic():
|
|
assert_tl_matmul_block_all_dynamic_correctness(128, 128, 128, False, False, "float16",
|
|
"float16", "float16", 64, 64, 32)
|
|
assert_tl_matmul_block_all_dynamic_correctness(67, 128, 128, False, False, "float16", "float16",
|
|
"float16", 64, 64, 32)
|
|
assert_tl_matmul_block_all_dynamic_correctness(36, 128, 128, False, False, "float16", "float16",
|
|
"float16", 64, 64, 32)
|
|
|
|
|
|
def test_assert_tl_matmul_block_all_dynamic_with_pass_config():
|
|
assert_tl_matmul_block_all_dynamic_correctness_with_pass_config(
|
|
128,
|
|
128,
|
|
128,
|
|
False,
|
|
False,
|
|
"float16",
|
|
"float16",
|
|
"float16",
|
|
64,
|
|
64,
|
|
32,
|
|
dynamic_alignment=8)
|
|
assert_tl_matmul_block_all_dynamic_correctness_with_pass_config(
|
|
64,
|
|
128,
|
|
128,
|
|
False,
|
|
False,
|
|
"float16",
|
|
"float16",
|
|
"float16",
|
|
64,
|
|
64,
|
|
32,
|
|
dynamic_alignment=8)
|
|
assert_tl_matmul_block_all_dynamic_correctness_with_pass_config(
|
|
64, 128, 60, False, False, "float16", "float16", "float16", 64, 64, 32, dynamic_alignment=4)
|
|
# Tail split is enabled with dynamic alignment 0
|
|
assert_tl_matmul_block_all_dynamic_correctness_with_pass_config(
|
|
64, 128, 64, False, False, "float16", "float16", "float16", 64, 64, 32, dynamic_alignment=0)
|
|
|
|
|
|
if __name__ == "__main__":
|
|
tilelang.testing.main()
|