diff --git a/.github/workflows/amd_perf_kernel_Integration_tests.yml b/.github/workflows/amd_perf_kernel_Integration_tests.yml new file mode 100644 index 000000000000..4262a3eb08f7 --- /dev/null +++ b/.github/workflows/amd_perf_kernel_Integration_tests.yml @@ -0,0 +1,137 @@ +name: AMD Perf Kernel Integration Tests + +on: + workflow_dispatch: + pull_request: + branches: [main_perf] + merge_group: + branches: [main_perf] + types: [checks_requested] + +concurrency: + group: ${{ github.ref }} + cancel-in-progress: ${{ github.ref != 'refs/heads/main_perf' }} + +permissions: read-all + +env: + TRITON_BUILD_WITH_CLANG_LLD: "TRUE" + TRITON_USE_ASSERT_ENABLED_LLVM: "TRUE" + TRITON_DISABLE_LINE_INFO: 1 + +jobs: + Check-File-Changes: + if: github.event_name == 'pull_request' + runs-on: ubuntu-latest + steps: + - name: Checkout code + uses: actions/checkout@v4 + with: + fetch-depth: 0 + - name: Check file changes + run: | + git fetch origin ${{ github.base_ref }} + changed_files=$(git diff --name-only origin/${{ github.base_ref }} ${{ github.sha }}) + echo "Changed files:" + echo "$changed_files" + if echo "$changed_files" | grep -vE "^python/perf-kernels/|^\.github/workflows/amd_"; then + echo "Changes detected outside of the python/perf-kernels directory or .github/workflows/amd_ files. Failing the workflow." + exit 1 + fi + + Runner-Preparation-AMD: + runs-on: ubuntu-latest + timeout-minutes: 30 + outputs: + matrix-HIP: ${{ steps.set-matrix.outputs.matrix-HIP }} + steps: + - name: Prepare runner matrix + id: set-matrix + run: | + if [ x"${{ github.repository }}" == x"ROCm/triton" ]; then + echo '::set-output name=matrix-HIP::[["self-hosted", "rocm.gfx90a"]]' + else + echo '::set-output name=matrix-HIP::[["ubuntu-latest"]]' + fi + + pre-commit: + name: pre-commit (code formatting) + needs: Runner-Preparation-AMD + runs-on: ubuntu-latest + steps: + - name: Checkout + uses: actions/checkout@v4 + - uses: actions/setup-python@v5 + with: + python-version: '3.12' + cache: 'pip' + - name: Compute hash of pre-commit config + id: cache-key + run: | + echo "pre_commit_hash=$(sha256sum .pre-commit-config.yaml)" >> $GITHUB_OUTPUT + shell: bash + - name: Cache pre-commit's cache dir + uses: actions/cache@v4 + with: + # Note that we cannot use environment variables here given there is + # no shell to interpret them in the paths. + path: | + ~/.cache/pre-commit + key: ${{ runner.os }}-${{ steps.cache-key.outputs.pre_commit_hash }} + - name: Check pre-commit + run: | + python3 -m pip install --upgrade pre-commit + # TODO: ignore the first yapf failure until https://github.com/google/yapf/issues/1164 is fixed + python3 -m pre_commit run --all-files --verbose yapf &> /dev/null || true + # If first run of yapf worked and made changes reset the tree to the original state + git reset --hard + python3 -m pre_commit run --all-files --verbose + - name: Print diff of changes if pre-commit failed + if: failure() + run: | + git diff + + Integration-Tests-AMD: + needs: Runner-Preparation-AMD + if: needs.Runner-Preparation-AMD.outputs.matrix-HIP != '' + runs-on: ${{ matrix.runner }} + timeout-minutes: 90 + strategy: + matrix: + runner: ${{fromJson(needs.Runner-Preparation-AMD.outputs.matrix-HIP)}} + container: + image: rocm/pytorch:rocm6.1_ubuntu22.04_py3.10_pytorch_2.4 + options: --device=/dev/kfd --device=/dev/dri --security-opt seccomp=unconfined --group-add video --user root + steps: + - name: Checkout + uses: actions/checkout@v4 + - name: Clear cache + run: | + rm -rf ~/.triton + mkdir -p ~/.triton + ls -alh ~/.triton + - name: Update PATH + run: | + echo "/opt/rocm/llvm/bin" >> $GITHUB_PATH + - name: Install pip dependencies + run: | + python3 -m pip install --upgrade pip + python3 -m pip install lit matplotlib pandas + - name: Install Triton + run: | + echo "PATH is '$PATH'" + pip uninstall -y triton + cd python + pip install -v -e . + - name: Run Perf Kernels Unit Tests + run: | + pytest -vvv ./python/perf-kernels/flash-attention.py + pytest -vvvv ./python/perf-kernels/softmax.py + pytest -vvv ./python/perf-kernels/rmsnorm.py + pytest -vvv ./python/perf-kernels/layernorm.py + - name: Run Perf Kernels Benchmark + run: | + python ./python/perf-kernels/flash-attention.py + python ./python/perf-kernels/softmax.py + python ./python/perf-kernels/rmsnorm.py + python ./python/perf-kernels/layernorm.py diff --git a/.github/workflows/amd_perf_kernel_postmerge_tests.yml b/.github/workflows/amd_perf_kernel_postmerge_tests.yml new file mode 100644 index 000000000000..21470c094e46 --- /dev/null +++ b/.github/workflows/amd_perf_kernel_postmerge_tests.yml @@ -0,0 +1,92 @@ +name: AMD Perf Kernel Post-Merge Tests + +on: + workflow_dispatch: + push: + branches: [main_perf, micmelesse/post_merge_ci] + +concurrency: + group: ${{ github.ref }} + cancel-in-progress: ${{ github.ref != 'refs/heads/main_perf' }} + +permissions: read-all + +env: + TRITON_BUILD_WITH_CLANG_LLD: "TRUE" + TRITON_USE_ASSERT_ENABLED_LLVM: "TRUE" + TRITON_DISABLE_LINE_INFO: 1 + +jobs: + Runner-Preparation-AMD: + runs-on: ubuntu-latest + timeout-minutes: 30 + outputs: + matrix-HIP: ${{ steps.set-matrix.outputs.matrix-HIP }} + steps: + - name: Prepare runner matrix + id: set-matrix + run: | + if [ x"${{ github.repository }}" == x"ROCm/triton" ]; then + echo '::set-output name=matrix-HIP::[["self-hosted", "rocm.gfx90a"]]' + else + echo '::set-output name=matrix-HIP::[["ubuntu-latest"]]' + fi + + PostMerge-Tests-AMD: + needs: Runner-Preparation-AMD + if: needs.Runner-Preparation-AMD.outputs.matrix-HIP != '' + runs-on: ${{ matrix.runner }} + timeout-minutes: 90 + strategy: + matrix: + runner: ${{fromJson(needs.Runner-Preparation-AMD.outputs.matrix-HIP)}} + container: + image: rocm/pytorch:rocm6.0.2_ubuntu22.04_py3.10_pytorch_2.1.2 + options: --device=/dev/kfd --device=/dev/dri --security-opt seccomp=unconfined --group-add video --user root + steps: + - name: Checkout + uses: actions/checkout@v4 + with: + fetch-depth: 0 # Ensure the entire history is fetched for rebase + - name: Add upstream remote + run: | + git config --global --add safe.directory /__w/triton/triton + if [ $(git remote | grep -c upstream) -eq 0 ]; then + git remote add upstream https://github.com/triton-lang/triton.git + fi + git fetch upstream + - name: Rebase onto upstream/main + run: | + git config --global user.email "ci@amd.com" + git config --global user.name "Github Actions Post-Merge CI Script" + git rebase upstream/main || { echo "Rebase failed"; exit 1; } + - name: Show Git Log + run: | + echo "Git log after rebase from upstream/main to HEAD:" + git log $(git rev-parse upstream/main~2)..HEAD --oneline --graph --decorate + env: + GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} + - name: Clear cache + run: | + rm -rf ~/.triton + mkdir -p ~/.triton + ls -alh ~/.triton + - name: Update PATH + run: | + echo "/opt/rocm/llvm/bin" >> $GITHUB_PATH + - name: Install pip dependencies + run: | + python3 -m pip install --upgrade pip + python3 -m pip install lit matplotlib pandas + - name: Install Triton + run: | + echo "PATH is '$PATH'" + pip uninstall -y triton + cd python + pip install -v -e . + - name: Run Perf Kernels Unit Tests + run: | + pytest -vvv ./python/perf-kernels/flash-attention.py + - name: Run Perf Kernels Benchmark + run: | + python ./python/perf-kernels/flash-attention.py diff --git a/python/perf-kernels/03-matrix-multiplication-all-types.py b/python/perf-kernels/03-matrix-multiplication-all-types.py new file mode 100644 index 000000000000..1b0676079ede --- /dev/null +++ b/python/perf-kernels/03-matrix-multiplication-all-types.py @@ -0,0 +1,377 @@ +import torch + +import triton +import triton.language as tl +import sys +import argparse +import pytest +import re + + +@triton.autotune( + configs=[ + triton.Config( + {'BLOCK_SIZE_M': 256, 'BLOCK_SIZE_N': 256, 'BLOCK_SIZE_K': 128, 'GROUP_SIZE_M': 4, 'waves_per_eu': 0}, + num_warps=8, num_stages=0), + triton.Config( + {'BLOCK_SIZE_M': 128, 'BLOCK_SIZE_N': 256, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 4, 'waves_per_eu': 0}, + num_warps=8, num_stages=0), + triton.Config( + {'BLOCK_SIZE_M': 128, 'BLOCK_SIZE_N': 256, 'BLOCK_SIZE_K': 16, 'GROUP_SIZE_M': 4, 'waves_per_eu': 2}, + num_warps=4, num_stages=0), + triton.Config( + {'BLOCK_SIZE_M': 128, 'BLOCK_SIZE_N': 128, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 1, 'waves_per_eu': 2}, + num_warps=8, num_stages=0), + triton.Config( + {'BLOCK_SIZE_M': 128, 'BLOCK_SIZE_N': 64, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 32, 'waves_per_eu': 2}, + num_warps=4, num_stages=0), + ], + key=['M', 'N', 'K'], + use_cuda_graph=True, +) +@triton.heuristics({ + 'EVEN_K': lambda args: args['K'] % args['BLOCK_SIZE_K'] == 0, +}) +@triton.jit +def matmul_kernel( + # Pointers to matrices + a_ptr, + b_ptr, + c_ptr, + # Matrix dimensions + M, + N, + K, + # The stride variables represent how much to increase the ptr by when moving by 1 + # element in a particular dimension. E.g. `stride_am` is how much to increase `a_ptr` + # by to get the element one row down (A has M rows). + stride_am, + stride_ak, + stride_bk, + stride_bn, + stride_cm, + stride_cn, + # Meta-parameters + BLOCK_SIZE_M: tl.constexpr, + BLOCK_SIZE_N: tl.constexpr, + BLOCK_SIZE_K: tl.constexpr, + EVEN_K: tl.constexpr, + GROUP_SIZE_M: tl.constexpr, + ACTIVATION: tl.constexpr, +): + """Kernel for computing the matmul C = A x B. + A has shape (M, K), B has shape (K, N) and C has shape (M, N) + """ + # ----------------------------------------------------------- + # Map program ids `pid` to the block of C it should compute. + # This is done in a grouped ordering to promote L2 data reuse. + # See above `L2 Cache Optimizations` section for details. + pid = tl.program_id(axis=0) + num_pid_m = tl.cdiv(M, BLOCK_SIZE_M) + num_pid_n = tl.cdiv(N, BLOCK_SIZE_N) + if GROUP_SIZE_M == 1: + pid_m = pid // num_pid_n + pid_n = pid % num_pid_n + else: + num_pid_in_group = GROUP_SIZE_M * num_pid_n + group_id = pid // num_pid_in_group + first_pid_m = group_id * GROUP_SIZE_M + group_size_m = min(num_pid_m - first_pid_m, GROUP_SIZE_M) + pid_m = first_pid_m + (pid % group_size_m) + pid_n = (pid % num_pid_in_group) // group_size_m + + # ---------------------------------------------------------- + # Create pointers for the first blocks of A and B. + # We will advance this pointer as we move in the K direction + # and accumulate + # `a_ptrs` is a block of [BLOCK_SIZE_M, BLOCK_SIZE_K] pointers + # `b_ptrs` is a block of [BLOCK_SIZE_K, BLOCK_SIZE_N] pointers + # See above `Pointer Arithmetics` section for details + offs_k = tl.arange(0, BLOCK_SIZE_K) + offs_am = (pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M)) % M + offs_bn = (pid_n * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N)) % N + a_ptrs = a_ptr + (offs_am[:, None] * stride_am + offs_k[None, :] * stride_ak) + b_ptrs = b_ptr + (offs_k[:, None] * stride_bk + offs_bn[None, :] * stride_bn) + + # ----------------------------------------------------------- + # Iterate to compute a block of the C matrix. + # We accumulate into a `[BLOCK_SIZE_M, BLOCK_SIZE_N]` block + # of fp32 values for higher accuracy. + # `accumulator` will be converted back to fp16 after the loop. + acc_dtype = tl.float32 if c_ptr.type.element_ty != tl.int8 else tl.int32 + accumulator = tl.zeros((BLOCK_SIZE_M, BLOCK_SIZE_N), dtype=acc_dtype) + + for k in range(0, tl.cdiv(K, BLOCK_SIZE_K)): + # Load the next block of A and B, generate a mask by checking the K dimension. + # If it is out of bounds, set it to 0. + if EVEN_K: + a = tl.load(a_ptrs) + b = tl.load(b_ptrs) + else: + a = tl.load(a_ptrs, mask=offs_k[None, :] < K - k * BLOCK_SIZE_K, other=0.0) + b = tl.load(b_ptrs, mask=offs_k[:, None] < K - k * BLOCK_SIZE_K, other=0.0) + # We accumulate along the K dimension. + accumulator += tl.dot(a, b) + + # Advance the ptrs to the next K block. + a_ptrs += BLOCK_SIZE_K * stride_ak + b_ptrs += BLOCK_SIZE_K * stride_bk + # You can fuse arbitrary activation functions here + # while the accumulator is still in FP32! + if ACTIVATION == "leaky_relu": + accumulator = leaky_relu(accumulator) + c = accumulator.to(c_ptr.type.element_ty) + + # ----------------------------------------------------------- + # Write back the block of the output matrix C with masks. + offs_cm = pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M) + offs_cn = pid_n * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N) + c_ptrs = c_ptr + stride_cm * offs_cm[:, None] + stride_cn * offs_cn[None, :] + c_mask = (offs_cm[:, None] < M) & (offs_cn[None, :] < N) + tl.store(c_ptrs, c, mask=c_mask) + + +# We can fuse `leaky_relu` by providing it as an `ACTIVATION` meta-parameter in `_matmul`. +@triton.jit +def leaky_relu(x): + x = x + 1 + return tl.where(x >= 0, x, 0.01 * x) + + +# %% +# We can now create a convenience wrapper function that only takes two input tensors, +# and (1) checks any shape constraint; (2) allocates the output; (3) launches the above kernel. + + +def matmul(a, b, c, activation=""): + # Check constraints. + assert a.shape[1] == b.shape[0], "Incompatible dimensions" + # assert a.is_contiguous(), "Matrix A must be contiguous" + # assert b.is_contiguous(), "Matrix B must be contiguous" + M, K = a.shape + K, N = b.shape + # 1D launch kernel where each block gets its own program. + grid = lambda META: (triton.cdiv(M, META['BLOCK_SIZE_M']) * triton.cdiv(N, META['BLOCK_SIZE_N']), ) + matmul_kernel[grid]( + a, + b, + c, + M, + N, + K, + a.stride(0), + a.stride(1), + b.stride(0), + b.stride(1), + c.stride(0), + c.stride(1), + ACTIVATION=activation, + ) + + +TORCH_HAS_FP8E5B16 = hasattr(torch, 'float8_e5m2fnuz') +TORCH_HAS_FP8E4B8 = hasattr(torch, 'float8_e4m3fnuz') +tl_to_torch_types = { + tl.float16: torch.float16, + tl.bfloat16: torch.bfloat16, + tl.float32: torch.float32, + tl.int8: torch.int8, + tl.int32: torch.int32, +} +if TORCH_HAS_FP8E5B16: + tl_to_torch_types[tl.float8e5b16] = torch.float8_e5m2fnuz +if TORCH_HAS_FP8E4B8: + tl_to_torch_types[tl.float8e4b8] = torch.float8_e4m3fnuz + +name_to_tl_types = { + 'int8': tl.int8, + 'int32': tl.int32, + 'fp16': tl.float16, + 'fp32': tl.float32, + 'bf16': tl.bfloat16, + 'fp8e4': tl.float8e4b8, + 'fp8e5': tl.float8e5b16, +} + + +def gen_input(M, N, ty_name, needTrans, seed, device='cuda'): + d_type = name_to_tl_types[ty_name] + torch.manual_seed(seed) + torch.cuda.manual_seed(seed) + + @triton.jit + def copy_kernel(input_ptr, output_ptr, n_elements, BLOCK_SIZE: tl.constexpr): + offsets = tl.program_id(axis=0) * BLOCK_SIZE + tl.arange(0, BLOCK_SIZE) + mask = offsets < n_elements + input = tl.load(input_ptr + offsets, mask=mask) + output = input + tl.store(output_ptr + offsets, output, mask=mask) + + if needTrans: + raw_data = torch.randn((N, M), dtype=torch.float32, device='cuda').T + else: + raw_data = torch.randn((M, N), dtype=torch.float32, device='cuda') + # avoid type conversion rounding errors of subnormal values + raw_data += 0.1 + if d_type == tl.float8e4b8: + raw_data += torch.sign(raw_data) + + if (d_type == tl.float8e4b8 and TORCH_HAS_FP8E4B8) or \ + (d_type == tl.float8e5b16 and TORCH_HAS_FP8E5B16) or not d_type.is_fp8(): + input = raw_data.to(tl_to_torch_types[d_type]) + input_f16 = input.to(torch.float16) + else: + f8_tensor = raw_data.to(torch.int8) + # keep only two bits of exponent to avoid overflow + f8_tensor = f8_tensor & 0b00111111 + input = triton.reinterpret(f8_tensor, d_type) + input_f16 = torch.empty_like(f8_tensor, dtype=torch.float16) + grid = lambda meta: (triton.cdiv(n_elements, meta['BLOCK_SIZE']), ) + n_elements = raw_data.numel() + copy_kernel[grid](input, input_f16, n_elements, BLOCK_SIZE=1024) + + return input, input_f16 + + +# %% +# Unit Test +# --------- +# +# We can test our custom matrix multiplication operation against a native torch implementation (i.e., rocBLAS). +def get_x_vals(): + x_vals = [(1024 * v, 1024 * v, 1024 * v) for v in range(1, 9)] + + x_vals += [(4864, 4096, 8192), (9728, 8192, 65536)] + + return x_vals + + +@pytest.mark.parametrize("M, N, K, in_dtype, out_dtype, col_a, col_b", [ + (*shape, in_dtype, out_dtype, col_a, col_b) + for shape in get_x_vals() + for in_dtype, out_dtype in [('fp16', 'fp16'), ('bf16', 'bf16'), ('fp16', + 'fp32'), ('fp32', + 'fp32'), ('fp8e4', + 'fp16'), ('fp8e5', 'fp16'), + #('int8', 'int8'), + ('int8', 'int32')] + # Only test k-major tensors because + # 1. This is the most preformant config and the current focus + # 2. Other case does not work with num_stages=0 (TODO (zhanglx)) + for col_a in [True, False] + for col_b in [True, False] +]) +def test_correctness(M, N, K, col_a, col_b, in_dtype, out_dtype): + a, a_fp16 = gen_input(M, K, in_dtype, col_a, 1, device='cuda') + b, b_fp16 = gen_input(K, N, in_dtype, col_b, 2, device='cuda') + # Allocates output. + tl_out_dtype = name_to_tl_types[out_dtype] + torch_out_dtype = tl_to_torch_types[tl_out_dtype] + c = torch.empty((M, N), device=a.device, dtype=torch_out_dtype) + matmul(a, b, c, activation="") + if in_dtype == 'fp8e4' or in_dtype == 'fp8e5' or in_dtype == 'int8': + # For f8 and int8 inputs, use fp16 for torch.matmul + torch_output = torch.matmul(a_fp16, b_fp16) + else: + torch_output = torch.matmul(a, b) + #print(f"triton_output={c}") + #print(f"torch_output={torch_output}") + rtol = 0 if torch.version.hip is None else 1e-2 + if in_dtype == 'int8': + torch.testing.assert_close(c.to(torch.float16), torch_output, atol=1e-3, rtol=rtol) + else: + torch.testing.assert_close(c, torch_output.to(torch_out_dtype), atol=5e-3, rtol=rtol) + + +# %% +# Benchmark +# --------- +# +# Square Matrix Performance +# ~~~~~~~~~~~~~~~~~~~~~~~~~~ +# +# We can now compare the performance of our kernel against that of rocBLAS. Here we focus on square matrices, +# but feel free to arrange this script as you wish to benchmark any other matrix shape. + + +def get_type(provider): + res = re.findall(r'\(.*?\)', provider) + return res[0][1:-1] + + +inout_dtype = { + 'int8': torch.int8, + 'fp16': torch.float16, + 'fp32': torch.float32, + 'bf16': torch.bfloat16, + 'fp8e4': torch.float16, + 'fp8e5': torch.float16, +} + + +@triton.testing.perf_report( + triton.testing.Benchmark( + x_names=['M', 'N', 'K'], # Argument names to use as an x-axis for the plot + x_vals=get_x_vals(), + line_arg='provider', # Argument name whose value corresponds to a different line in the plot + # Possible values for `line_arg` + line_vals=[ + 'rocblas(fp16)', 'rocblas(bf16)', 'triton(fp16)', 'triton(bf16)', 'triton(int8)', 'triton(fp8e4)', + 'triton(fp8e5)' + ], + # Label name for the lines + line_names=[ + "rocBLAS.Fp16", "rocBLAS.Bf16", "Triton.Fp16", "Triton.Bf16", "Triton.Int8", "Triton.Fp8E4", "Triton.Fp8E5" + ], + ylabel="TFLOPS", # Label name for the y-axis + plot_name="matmul-performance", # Name for the plot, used also as a file name for saving the plot. + args={}, + )) +def benchmark(M, N, K, provider): + in_dtype = get_type(provider) + out_dtype = inout_dtype[in_dtype] + + quantiles = [0.5, 0.2, 0.8] + if 'rocblas' in provider: + a = torch.randn((M, K), dtype=tl_to_torch_types[name_to_tl_types[in_dtype]], device='cuda') + b = torch.randn((K, N), dtype=tl_to_torch_types[name_to_tl_types[in_dtype]], device='cuda') + + ms, min_ms, max_ms = triton.testing.do_bench(lambda: torch.matmul(a, b), quantiles=quantiles) + else: # triton, different data types + assert "triton" in provider + a, _ = gen_input(M, K, in_dtype, False, 1, device='cuda') + b, _ = gen_input(K, N, in_dtype, True, 2, device='cuda') + # Allocates output. + c = torch.empty((M, N), device=a.device, dtype=out_dtype) + + ms, min_ms, max_ms = triton.testing.do_bench(lambda: matmul(a, b, c, activation=""), quantiles=quantiles) + global verbose + if verbose: + print(f'SIZE: {M},{N},{K} Best tuning config: ({matmul_kernel.get_best_config()})') + perf = lambda ms: 2 * M * N * K * 1e-12 / (ms * 1e-3) + return perf(ms), perf(max_ms), perf(min_ms) + + +def parse_args(): + parser = argparse.ArgumentParser( + prog="GEMM tutorial example", + allow_abbrev=False, + ) + + parser.add_argument("-v", action='store_true', default=False, help="Print out the best tuning config") + args = parser.parse_args() + + return args + + +def main(): + # assign to a global verbose var to indicate whether print + # best tuning config + global verbose + args = parse_args() + verbose = args.v + benchmark.run(show_plots=True, print_data=True) + + +if __name__ == '__main__': + sys.exit(main()) diff --git a/python/perf-kernels/06-attention-decode.py b/python/perf-kernels/06-attention-decode.py new file mode 100644 index 000000000000..3f38e5031eca --- /dev/null +++ b/python/perf-kernels/06-attention-decode.py @@ -0,0 +1,730 @@ +from typing import Optional +import pytest +import torch +import sys + +import triton +import triton.language as tl + + +def _strides(x: torch.Tensor, *stride_names: str): + assert x.ndim == len(stride_names) + return {f"stride_{s}": x.stride(i) for i, s in enumerate(stride_names)} + + +@triton.jit +def _fwd_kernel_splitK( + Q, + K, + V, + sm_scale, + Out_splitK, # [B, H, split_k, Mq, K] + Metadata, # [B, H, 2, split_k, M_ceil] contains [mi, li] + Seq_len, + stride_qz, + stride_qm, + stride_qg, + stride_qh, + stride_qk, + stride_kz, + stride_kn, + stride_kg, + stride_kh, + stride_kk, + stride_vz, + stride_vn, + stride_vg, + stride_vh, + stride_vk, + stride_osk_zhg, + stride_osk_s, + stride_osk_m, + stride_osk_k, + stride_mzhg, + stride_m2, + stride_ms, + stride_mm, + Z, + N_CTX_Q, + N_CTX_K, + BLOCK_N_PER_SPLIT, + H: tl.constexpr, + G: tl.constexpr, + BLOCK_M: tl.constexpr, + BLOCK_DMODEL: tl.constexpr, + BLOCK_N: tl.constexpr, + BOUNDS_CHECKS_N: tl.constexpr, + USE_SEQ_LEN: tl.constexpr, + PACKED_PER_VAL: tl.constexpr = 1, + N_GROUPS: tl.constexpr = 1, +): + """This kernel can accept non-quantized or int4-quantized keys/values. + PACKED_PER_VAL determines the quantization type: + - PACKED_PER_VAL == 1 means no quantization + - PACKED_PER_VAL == 8 means 4-bit quantization (8 packed quantized values inside one int32) + For the quantized case K/V should be int32 tensors. + Quantization can be row-wise (when N_GROUPS = 1) or group-wise with N_GROUPS = 2, 4, or 8. + Quantization coefficients are stored at the beginning of the row along the last dimension of K/V + So K[B, H, M, :] has a form + [ quant_coef0, quant_coef1, ...| + group0_quant_value0, group0_quant_value1,... | + group1_quant_value0, group1_quant_value1,...] + where each quant_coef is an int32 which should be interpreted as 2 packed float16: scale and offset. + + """ + tl.static_assert( + (PACKED_PER_VAL == 1 and tl.constexpr(K.dtype.element_ty != tl.int32)) + or (PACKED_PER_VAL == 8 and tl.constexpr(K.dtype.element_ty == tl.int32)), + f"Only 4-bit quantization is supported, K/V should have dtype int32 in " + f"the quantized case: {PACKED_PER_VAL=} {tl.constexpr(K.dtype)=} {tl.constexpr(K.dtype.element_ty)=}", + ) + tl.static_assert( + (((N_GROUPS == 1 or N_GROUPS == 2) or N_GROUPS == 4) or N_GROUPS == 8), + "Number of quantization groups can be 1 (row-wise quantization), 2, 4, or 8.", + ) + + QUANTIZED: tl.constexpr = PACKED_PER_VAL > 1 + PACKED_D_PER_GROUP: tl.constexpr = BLOCK_DMODEL // PACKED_PER_VAL // N_GROUPS + D_PER_GROUP: tl.constexpr = BLOCK_DMODEL // N_GROUPS + + start_m = tl.program_id(0) + off_zhg = tl.program_id(1) + off_z = off_zhg // (H * G) + off_h = (off_zhg // G) % H + off_g = off_zhg % G + splitk_idx = tl.program_id(2) + + lo = splitk_idx * BLOCK_N_PER_SPLIT + if USE_SEQ_LEN: + kv_len = tl.load(Seq_len + off_z) + else: + kv_len = N_CTX_K + hi = tl.minimum((splitk_idx + 1) * BLOCK_N_PER_SPLIT, kv_len) + + Q_block_ptr = tl.make_block_ptr( + base=Q + off_h * stride_qh + off_z * stride_qz + off_g * stride_qg, + shape=(N_CTX_Q, D_PER_GROUP), + strides=(stride_qm, stride_qk), + offsets=(start_m * BLOCK_M, 0), + block_shape=(BLOCK_M, D_PER_GROUP), + order=(1, 0), + ) + + k_base = K + off_h * stride_kh + off_z * stride_kz + off_g * stride_kg + # Additional shift by 1 along the last dimension in the quantized case, since + # the first element along that dim contains packed quantization coefficients. + K_block_ptr = tl.make_block_ptr( + base=k_base + stride_kk * QUANTIZED * N_GROUPS, + shape=(PACKED_D_PER_GROUP, hi), + strides=(stride_kk, stride_kn), + offsets=(0, lo), + block_shape=(PACKED_D_PER_GROUP, BLOCK_N), + order=(0, 1), + ) + v_base = V + off_h * stride_vh + off_z * stride_vz + off_g * stride_vg + V_block_ptr = tl.make_block_ptr( + base=v_base + stride_vk * QUANTIZED * N_GROUPS, + shape=(hi, PACKED_D_PER_GROUP), + strides=(stride_vn, stride_vk), + offsets=(lo, 0), + block_shape=(BLOCK_N, PACKED_D_PER_GROUP), + order=(1, 0), + ) + + if QUANTIZED: + # Pointers to quantization coefficients + K_scale_shift_block_ptr = tl.make_block_ptr( + base=k_base, + shape=(1, hi), + strides=(stride_kk, stride_kn), + offsets=(0, lo), + block_shape=(1, BLOCK_N), + order=(0, 1), + ) + V_scale_shift_block_ptr = tl.make_block_ptr( + base=v_base, + shape=(hi, 1), + strides=(stride_vn, stride_vk), + offsets=(lo, 0), + block_shape=(BLOCK_N, 1), + order=(1, 0), + ) + else: + K_scale_shift_block_ptr = None + V_scale_shift_block_ptr = None + + # initialize pointer to m and l + m_i = tl.zeros([BLOCK_M], dtype=tl.float32) - float("inf") + l_i = tl.zeros([BLOCK_M], dtype=tl.float32) + + acc = tl.zeros([BLOCK_M, D_PER_GROUP], dtype=tl.float32) # noqa: F821 + + # scale sm_scale by log_2(e) and use + # 2^x instead of exp in the loop because CSE and LICM + # don't work as expected with `exp` in the loop + qk_scale = sm_scale * 1.44269504 + # load q: it will stay in SRAM throughout + q = tl.load( # noqa: F821 + tl.advance(Q_block_ptr, (0, 0)), boundary_check=(0, )) + q = (q * qk_scale).to(q.dtype) + + # loop over k, v and update accumulator + for start_n in range(lo, hi, BLOCK_N): + k, v = load_dequantize_k_v_group( + K_block_ptr, + V_block_ptr, + K_scale_shift_block_ptr, + V_scale_shift_block_ptr, + BOUNDS_CHECKS_N, + PACKED_PER_VAL, + PACKED_D_PER_GROUP, + Q.dtype.element_ty, + 0, + ) + + # -- compute qk --- + qk = tl.zeros([BLOCK_M, BLOCK_N], dtype=tl.float32) + qk += tl.dot(q, k) # noqa: F821 + + # TODO: This is slow, and only needed at the last iteration. + # Maybe we can unroll the last iteration instead? + if BOUNDS_CHECKS_N: + qk = tl.where(tl.arange(0, BLOCK_N) < hi - start_n, qk, float("-inf")) + # -- compute scaling constant --- + m_i_new = tl.maximum(m_i, tl.max(qk, 1)) + alpha = tl.math.exp2(m_i - m_i_new) + p = tl.math.exp2(qk - m_i_new[:, None]) + + # -- update m_i and l_i -- + l_i = l_i * alpha + tl.sum(p, 1) + m_i = m_i_new + p = p.to(Q.dtype.element_ty) + + # -- scale and update acc -- + acc *= alpha[:, None] + acc += tl.dot(p, v) + # update pointers + K_block_ptr = tl.advance(K_block_ptr, (0, BLOCK_N)) + V_block_ptr = tl.advance(V_block_ptr, (BLOCK_N, 0)) + if PACKED_PER_VAL > 1: + K_scale_shift_block_ptr = tl.advance(K_scale_shift_block_ptr, (0, BLOCK_N)) + V_scale_shift_block_ptr = tl.advance(V_scale_shift_block_ptr, (BLOCK_N, 0)) + + # write back O + O_block_ptr = tl.make_block_ptr( + base=Out_splitK + off_zhg * stride_osk_zhg + splitk_idx * stride_osk_s, + shape=(N_CTX_Q, D_PER_GROUP), + strides=(stride_osk_m, 1), + offsets=(start_m * BLOCK_M, 0), + block_shape=(BLOCK_M, D_PER_GROUP), + order=(1, 0), + ) + tl.store( + tl.advance(O_block_ptr, (0, 0)), + acc, + boundary_check=(0, ), + ) + # Write metadata for split-K reduction + Metadata_ptr = (Metadata + off_zhg * stride_mzhg + splitk_idx * stride_ms + start_m * BLOCK_M + + tl.arange(0, BLOCK_M)) + tl.store(Metadata_ptr, m_i) + tl.store(Metadata_ptr + stride_m2, l_i) + + +@triton.jit +def load_dequantize_k_v_group( + K_block_ptr, + V_block_ptr, + K_scale_shift_block_ptr, + V_scale_shift_block_ptr, + BOUNDS_CHECKS_N: tl.constexpr, + PACKED_PER_VAL: tl.constexpr, + PACKED_D_PER_GROUP: tl.constexpr, + dtype: tl.constexpr, + group_id: tl.constexpr, +): + #Load K/V for a given block. In case of int4-quantized K/V, + # dequantize them after loading. If quantization is group-wise, + # use group_id to advance the pointers to the current group. + + # Advance to the current quantization group + K_block_ptr = tl.advance(K_block_ptr, (PACKED_D_PER_GROUP * group_id, 0)) + V_block_ptr = tl.advance(V_block_ptr, (0, PACKED_D_PER_GROUP * group_id)) + + # -- load k, v -- + k = tl.load(K_block_ptr, boundary_check=(1, ) if BOUNDS_CHECKS_N else ()) + v = tl.load(V_block_ptr, boundary_check=(0, ) if BOUNDS_CHECKS_N else ()) + + if PACKED_PER_VAL > 1: + # K/V are quantized, load quantization coefficients and dequantize + K_scale_shift_block_ptr = tl.advance(K_scale_shift_block_ptr, (group_id, 0)) + V_scale_shift_block_ptr = tl.advance(V_scale_shift_block_ptr, (0, group_id)) + + k_scale_shift = tl.load(K_scale_shift_block_ptr, boundary_check=(1, ) if BOUNDS_CHECKS_N else ()) + v_scale_shift = tl.load(V_scale_shift_block_ptr, boundary_check=(0, ) if BOUNDS_CHECKS_N else ()) + + k_scale, k_shift = cast_uint32_to_half2(k_scale_shift) + v_scale, v_shift = cast_uint32_to_half2(v_scale_shift) + v = dequantize(v, v_scale, v_shift, PACKED_PER_VAL).to(dtype) + k_t = dequantize( + tl.trans(k), + tl.trans(k_scale), + tl.trans(k_shift), + PACKED_PER_VAL, + ).to(dtype) + k = tl.trans(k_t) + return k, v + + +@triton.jit +def cast_uint32_to_half2(scale_shift): + # Extract two float16 packed into one int32 + scale = scale_shift & 0xFFFF + shift = scale_shift >> 16 + scale = scale.to(tl.uint16).to(tl.float16, bitcast=True) + shift = shift.to(tl.uint16).to(tl.float16, bitcast=True) + return scale, shift + + +@triton.jit +def dequantize( + x_, + scale, + shift, + PACKED_PER_VAL: tl.constexpr = 8, +): + # PACKED_PER_VAL is the number of values packed into + # each element x_. For example, for int4 quantization + #and x_ of type int32, PACKED_PER_VAL is 8. + + BLOCK_N: tl.constexpr = x_.shape[0] + BLOCK_DMODEL_PACKED: tl.constexpr = x_.shape[1] + offsets = tl.arange(0, PACKED_PER_VAL) * 4 + quant_offset = (x_[:, None, :] >> offsets[None, :, None]) # (BLOCK_N, PACKED_PER_VAL, D // PACKED_PER_VAL) + + quant_offset = tl.view(quant_offset, (BLOCK_N, BLOCK_DMODEL_PACKED * PACKED_PER_VAL)) + # Trick - instead of converting int4 to float16 we view it as float16 + # and then multiply by 32768 * 512 == 2**24 + quant_offset = (quant_offset & 0xF).to(tl.uint16).to(tl.float16, bitcast=True) + quant_offset = (quant_offset * 32768.0).to(tl.float16) + scale_512 = scale * 512 + + dequant = quant_offset * scale_512 + shift + return dequant + + +@triton.jit +def _splitK_reduce( + Out_splitK, # [B, H, split_k, Mq, K] + Metadata, # [B, H, 2, split_k, M_ceil] contains [mi, li] + Out, # [B, H, M, K] + LSE, # [B, H, M] + stride_osk_zhg, + stride_osk_s, + stride_osk_m, + stride_osk_k, + stride_mzhg, + stride_m2, + stride_ms, + stride_mm, + stride_oz, + stride_oh, + stride_og, + stride_om, + stride_ok, + stride_lse_zhg, + stride_lse_m, + M_ceil: tl.constexpr, + BLOCK_SIZE: tl.constexpr, + H: tl.constexpr, + G: tl.constexpr, + split_k: tl.constexpr, + splitK_pow2: tl.constexpr, + use_mask: tl.constexpr, +): + off_zhg = tl.program_id(0) + off_z = off_zhg // (H * G) + off_h = (off_zhg // G) % H + off_g = off_zhg % G + off_m = tl.program_id(1) + off_k = tl.program_id(2) + + # read chunk + spk_idx = tl.arange(0, splitK_pow2) + kidx = tl.arange(0, BLOCK_SIZE) + + Metadata_ptr = (Metadata + stride_mzhg * off_zhg + spk_idx * stride_ms + off_m * stride_mm) + + o_ptr = (Out_splitK + off_zhg * stride_osk_zhg + stride_osk_m * off_m + off_k * BLOCK_SIZE + + stride_osk_s * spk_idx[:, None] + kidx[None, :] * stride_osk_k) + + # read max values of each splitK + if use_mask: + spk_mask = spk_idx < split_k + l_m = tl.load(Metadata_ptr, mask=spk_mask, other=float("-inf")) + l_sum = tl.load(Metadata_ptr + stride_m2, mask=spk_mask, other=0.0) + acc = tl.load(o_ptr, mask=spk_mask[:, None], other=0.0) + else: + l_m = tl.load(Metadata_ptr) + l_sum = tl.load(Metadata_ptr + stride_m2) + acc = tl.load(o_ptr) + + g_m = tl.max(l_m, axis=0) + alpha = tl.math.exp2(l_m - g_m) + + # read sum + l_sum *= alpha + g_sum = tl.sum(l_sum, axis=0) + acc = acc * alpha[:, None] + acc_out = tl.sum(acc, axis=0) / g_sum + Out_ptr = (Out + stride_oz * off_z + stride_oh * off_h + stride_og * off_g + stride_om * off_m + + off_k * BLOCK_SIZE + tl.arange(0, BLOCK_SIZE)) + tl.store(Out_ptr, acc_out) + l_ptrs = LSE + off_zhg * stride_lse_zhg + off_m + tl.store(l_ptrs, (g_m + tl.math.log2(g_sum)) / 1.44269504) + + +def quantize_kv_int4(k: torch.Tensor, num_groups: int = 1) -> torch.Tensor: + # Scale and shift are such that quantization linearly maps + # int4 values range [0..15] to input values range min(k)..max(k) + # individually for every row + k = k.reshape(*k.shape[:-1], num_groups, k.shape[-1] // num_groups) + max_vals = torch.max(k, dim=-1, keepdim=True).values + min_vals = torch.min(k, dim=-1, keepdim=True).values + scale_k: torch.Tensor = (max_vals - min_vals) / 15 + + shift_k = torch.min(k, dim=-1, keepdim=True).values + scale_k = scale_k.to(torch.float16) + shift_k = shift_k.to(torch.float16) + + in_bytes = ((k - shift_k.expand(k.shape)) / scale_k.expand(k.shape)) + 0.5 + in_bytes = in_bytes.to(torch.uint8) + in_int4 = in_bytes & 0xF + in_int4_packed = in_int4[..., ::2] + (in_int4[..., 1::2] << 4) + scale_shift = torch.concat([scale_k.view(torch.uint8), shift_k.view(torch.uint8)], dim=-1) + k_quant = torch.concat( + [ + scale_shift.flatten(start_dim=-2), + in_int4_packed.flatten(start_dim=-2), + ], + dim=-1, + ).view(torch.int16) + return k_quant + + +def dequantize_kv_fp16(quant_k: torch.Tensor, num_groups: int = 1) -> torch.Tensor: + k_i16 = quant_k.view(torch.int16) + k_ui8 = k_i16.view(torch.uint8) + + ss_size = num_groups * 4 + scale_shift_ui8 = k_ui8[..., 0:ss_size] + scale_shift_ui8 = scale_shift_ui8.reshape(*scale_shift_ui8.shape[:-1], num_groups, 4) + scale = scale_shift_ui8[..., 0:2].view(torch.float16) + shift = scale_shift_ui8[..., 2:4].view(torch.float16) + + kv_ui8 = k_ui8[..., ss_size:] + k_ui8 = kv_ui8.reshape(*kv_ui8.shape[:-1], num_groups, -1) + k1_i4 = k_ui8 & 0xF + k2_i4 = (k_ui8 & 0xF0) >> 4 + k_shape = k1_i4.shape + k1_f16 = k1_i4.to(torch.float16) * scale.expand(k_shape) + shift.expand(k_shape) + k2_f16 = k2_i4.to(torch.float16) * scale.expand(k_shape) + shift.expand(k_shape) + + out = torch.empty((*k1_f16.shape[:-1], k1_f16.shape[-1] * 2), dtype=torch.float16, device=quant_k.device) + out[..., ::2] = k1_f16 + out[..., 1::2] = k2_f16 + out = out.reshape(*k_shape[:-2], -1) + + return out + + +def get_split_k(B: int, G: int, H: int, Mk: int) -> int: + """Heuristic for the number of splits""" + bh = max(B * H, 1) # NOTE: Handle B*h=0 case + split_k = max(Mk, 1024) // bh + max_chunk_size = 64 + while split_k > 0 and Mk / split_k < max_chunk_size: + split_k = split_k // 2 + while B * H * G * split_k >= 1024: + split_k = split_k // 2 + split_k = min(split_k, 512) + split_k = max(split_k, 1) + return split_k + + +class _attention(torch.autograd.Function): + + OPERATOR = _fwd_kernel_splitK + SUPPORTED_DEVICES = {"cuda"} + CUDA_MINIMUM_COMPUTE_CAPABILITY = (8, 0) + SUPPORTED_DTYPES = { + torch.half, + torch.bfloat16, + } + SUPPORTED_MAX_K = 128 + SUPPORTS_DROPOUT = False + SUPPORTS_CUSTOM_SCALE = True + SUPPORTS_BMGHK = True + NAME = "triton_splitKF" + + @staticmethod + def forward(cls, q, k, v, scale_float): + + cls.SPLIT_K: Optional[int] = None + cls.BLOCK_M = 16 + cls.BLOCK_N = 64 + + cls.NUM_GROUPS = 1 # Default quantization is row-wise + + # attn_bias = inp.attn_bias + seq_len = None + + # Transpose in the case of MQA/GQA + mqa_swap_seqlen_head = False + if k.shape[3] > 1 and k.stride(3) == 0 and v.stride(3) == 0: + mqa_swap_seqlen_head = True + assert q.shape[1] == 1 + q = q.transpose(1, 3) + k = k[:, :, :, :1] + v = v[:, :, :, :1] + + if k.dtype == torch.int32: + # Quantized K/V + PACKED_PER_VAL = 8 + Lk = (k.shape[-1] - cls.NUM_GROUPS) * 8 + else: + Lk = k.shape[-1] + PACKED_PER_VAL = 1 + + B, Mk, G, H, Kkv = k.shape + B, M, G, H, Kq = q.shape + assert Lk == Kq, f"Keys have head dim {Lk} but queries have head dim {Kq}" + # print(f"B = {B}, M = {M}, G = {G}, H = {H}, Kkv = {Kkv}, Kq = {Kq}") + + BLOCK_M = cls.BLOCK_M + BLOCK_N = cls.BLOCK_N + if cls.SPLIT_K is not None: + split_k = cls.SPLIT_K + else: + # Use heuristics + split_k = get_split_k(B, G, H, Mk) + + M_ceil = (M + BLOCK_M - 1) // BLOCK_M * BLOCK_M + o_splitk = torch.empty([B * G * H, split_k, M_ceil, Kq], dtype=torch.float32, device=q.device) + metadata = torch.empty([B * G * H, 2, split_k, M_ceil], dtype=torch.float32, device=q.device) + lse = torch.empty((B * G * H, M), device=q.device, dtype=torch.float32) + grid = (triton.cdiv(M, BLOCK_M), B * G * H, split_k) + + num_warps = 1 + split_size = (Mk + split_k - 1) // split_k + use_seq_len = seq_len is not None + + # print(f"B = {B}, G = {G}, H = {H}, split_k = {split_k}, M_ceil = {M_ceil}, Kq = {Kq}, num_of_wgs = {G * G * H * split_k}") + + _fwd_kernel_splitK[grid]( + Q=q, + K=k, + V=v, + sm_scale=scale_float, + Out_splitK=o_splitk, + Metadata=metadata, + Seq_len=seq_len, + **_strides(q, "qz", "qm", "qg", "qh", "qk"), + **_strides(k, "kz", "kn", "kg", "kh", "kk"), + **_strides(v, "vz", "vn", "vg", "vh", "vk"), + **_strides(o_splitk, "osk_zhg", "osk_s", "osk_m", "osk_k"), + **_strides(metadata, "mzhg", "m2", "ms", "mm"), + Z=B, + H=H, + G=G, + N_CTX_Q=M, + N_CTX_K=Mk, + BLOCK_N_PER_SPLIT=split_size, + BLOCK_M=BLOCK_M, + BLOCK_N=BLOCK_N, + BLOCK_DMODEL=Lk, + BOUNDS_CHECKS_N=(split_size % BLOCK_N) > 0 or use_seq_len, + USE_SEQ_LEN=use_seq_len, + num_warps=num_warps, + num_stages=1, + PACKED_PER_VAL=PACKED_PER_VAL, + N_GROUPS=cls.NUM_GROUPS if PACKED_PER_VAL > 1 else 1, + ) + + if mqa_swap_seqlen_head: + out = torch.empty((B, H, G, M, Kq), device=q.device, dtype=q.dtype).transpose(1, 3) + else: + out = torch.empty((B, M, G, H, Kq), device=q.device, dtype=q.dtype) + + # Merge together + splitK_pow2 = triton.next_power_of_2(split_k) + use_mask = splitK_pow2 > split_k + if B * G * H * M >= 512: + k_block_num = 1 + else: + k_block_num = 2 + assert out.shape[-1] % k_block_num == 0 + k_block_size = out.shape[-1] // k_block_num + grid = (B * G * H, M, k_block_num) + _splitK_reduce[grid]( + o_splitk, metadata, out, lse, **_strides(o_splitk, "osk_zhg", "osk_s", "osk_m", "osk_k"), + **_strides(metadata, "mzhg", "m2", "ms", "mm"), **_strides(out, "oz", "om", "og", "oh", "ok"), + **_strides(lse, "lse_zhg", "lse_m"), M_ceil=M_ceil, BLOCK_SIZE=k_block_size, G=G, H=H, + # TODO: Tune num_warps + split_k=split_k, splitK_pow2=splitK_pow2, use_mask=use_mask, num_warps=4) + + lse = lse.reshape([B, G, H, M]) + if mqa_swap_seqlen_head: + # H/M dimensions have been swapped + out = out.transpose(1, 3) + lse = lse.transpose(2, 3) + if q.ndim == 4: + # BMGHK -> BMHK + assert G == 1 + out = out[:, :, 0] + lse = lse[:, 0] + if Mk == 0: + out.zero_() + if mqa_swap_seqlen_head: + out = out.reshape(B, -1, M * G, Kq).transpose(1, 2).contiguous() + else: + out = out.reshape(B, H * G, -1, Kq).contiguous() + + return out + + +attention = _attention.apply + + +def get_input_shapes(): + cases = [(max(1, 2**(16 - i)), 1, 2**i, 16, 1, 128) + for i in range(8, 18)] + [(max(1, 2**(16 - i)), 1, 2**i, 16, 2, 128) for i in range(8, 18)] + + return cases + + +@pytest.mark.parametrize('B, Mq, Mkv, Hq, Hkv, K', get_input_shapes()) +def test_op_fwd(B, Mq, Mkv, Hq, Hkv, K, dtype=torch.float16): + torch.manual_seed(20) + q = (torch.empty((B, Mq, Hkv, (Hq + Hkv - 1) // Hkv, K), dtype=dtype, + device="cuda").normal_(mean=0., std=0.5).requires_grad_()) + k = (torch.empty((B, Mkv, Hkv, 1, K), dtype=dtype, + device="cuda").normal_(mean=0., + std=0.5).requires_grad_()).expand(-1, -1, -1, (Hq + Hkv - 1) // Hkv, -1) + v = (torch.empty((B, Mkv, Hkv, 1, K), dtype=dtype, + device="cuda").normal_(mean=0., + std=0.5).requires_grad_()).expand(-1, -1, -1, (Hq + Hkv - 1) // Hkv, -1) + scale = 1 / K**0.5 + tri_out = attention(q, k, v, scale) + + q = q.reshape([B, Mq, -1, K]).permute(0, 2, 1, 3) + k = k.reshape([B, Mkv, -1, K]).permute(0, 2, 1, 3) + v = v.reshape([B, Mkv, -1, K]).permute(0, 2, 1, 3) + attn = (q @ k.transpose(-1, -2) * scale).softmax(-1) + ref_out = attn @ v + + # compare + torch.testing.assert_close(ref_out, tri_out, atol=1e-3, rtol=0) + + +@pytest.mark.parametrize('B, Mq, Mkv, Hq, Hkv, K', get_input_shapes()) +def test_op_fwd_int4_kv(B, Mq, Mkv, Hq, Hkv, K, dtype=torch.float16): + torch.manual_seed(2) + q = (torch.empty((B, Mq, Hkv, (Hq + Hkv - 1) // Hkv, K), dtype=dtype, + device="cuda").normal_(mean=1.0, std=0.5).requires_grad_()) + k = (torch.empty((B, Mkv, Hkv, 1, K), dtype=dtype, + device="cuda").normal_(mean=1.0, + std=0.5).requires_grad_()).expand(-1, -1, -1, (Hq + Hkv - 1) // Hkv, -1) + v = (torch.empty((B, Mkv, Hkv, 1, K), dtype=dtype, + device="cuda").normal_(mean=1.0, + std=0.5).requires_grad_()).expand(-1, -1, -1, (Hq + Hkv - 1) // Hkv, -1) + + num_groups = 1 + quant_k = (quantize_kv_int4(k, num_groups=num_groups).contiguous().view(torch.int32)) + quant_v = (quantize_kv_int4(v, num_groups=num_groups).contiguous().view(torch.int32)) + scale = 1 / K**0.5 + tri_out = attention(q, quant_k, quant_v, scale) + + q = q.reshape([B, Mq, -1, K]).permute(0, 2, 1, 3) + k = k.reshape([B, Mkv, -1, K]).permute(0, 2, 1, 3) + v = v.reshape([B, Mkv, -1, K]).permute(0, 2, 1, 3) + attn = (q @ k.transpose(-1, -2) * scale).softmax(-1) + ref_out = attn @ v + # compare + torch.testing.assert_close(ref_out, tri_out, atol=2.1e-2, rtol=0) + + # since quantization introduces rounding error, use the + # dequantized kv as inputs to the ref implementation to reduce + # the tolerance to 1e-3 + dqk = dequantize_kv_fp16(quant_k, num_groups=num_groups) + dqv = dequantize_kv_fp16(quant_v, num_groups=num_groups) + dqk = dqk.reshape([B, Mkv, -1, K]).permute(0, 2, 1, 3) + dqv = dqv.reshape([B, Mkv, -1, K]).permute(0, 2, 1, 3) + dq_attn = (q @ dqk.transpose(-1, -2) * scale).softmax(-1) + dq_ref_out = dq_attn @ dqv + torch.testing.assert_close(dq_ref_out, tri_out, atol=1e-3, rtol=0) + + +def test_quantization(): + a = torch.randn((2, 4, 32), dtype=torch.float16, device='cuda') + qa = quantize_kv_int4(a, num_groups=4) + dqa = dequantize_kv_fp16(qa, num_groups=4) + torch.testing.assert_close(a, dqa, atol=1.5e-1, rtol=1e-1) + + +try: + FLASH_VER = 2 +except BaseException: + try: + FLASH_VER = 1 + except BaseException: + FLASH_VER = None +HAS_FLASH = FLASH_VER is not None + +configs = [] +for mode in ['fwd']: + # for D_HEAD in [128]: + for causal in [False]: + configs.append( + triton.testing.Benchmark( + x_names=['B', 'Mq', 'Mkv', 'Hq', 'Hkv', 'K'], x_vals=get_input_shapes(), line_arg='provider', + line_vals=['triton'] + (['flash'] if HAS_FLASH else []), + line_names=['Triton'] + ([f'Flash-{FLASH_VER}'] if HAS_FLASH else []), styles=[('red', '-'), + ('blue', '-')], + ylabel='ms', plot_name=f'fused-attention-d{128}-{mode}-causal={causal}', args={ + # 'D_HEAD': D_HEAD, + 'dtype': torch.float16, 'mode': mode, 'causal': causal + })) + + +@triton.testing.perf_report(configs) +def bench_flash_attention(B, Mq, Mkv, Hq, Hkv, K, causal, mode, provider, dtype=torch.float16, device="cuda"): + assert mode in ['fwd', 'bwd'] + warmup = 100 + rep = 400 + ms = 0 + if provider == "triton": + q = torch.randn([B, Mq, Hkv, Hq // Hkv, K], device="cuda", dtype=dtype, requires_grad=False) + k = torch.randn([B, Mkv, Hkv, 1, K], device="cuda", dtype=dtype, + requires_grad=False).expand(-1, -1, -1, Hq // Hkv, -1) + v = torch.randn([B, Mkv, Hkv, 1, K], device="cuda", dtype=dtype, + requires_grad=False).expand(-1, -1, -1, Hq // Hkv, -1) + + sm_scale = 1.3 + fn = lambda: attention(q, k, v, sm_scale) + ms = triton.testing.do_bench(fn, warmup=warmup, rep=rep) + + # flops_per_matmul = 2 * B * Hq * (Mq * K * Mkv + Mq * Mkv * K) + # total_flops = 2 * flops_per_matmul + # totalBytes = ((B * Mkv * Hkv * K * 2) + (B * Mq * Hq * K) + (B * Mq * Hq * K)) * 2 + + # return totalBytes / ms * 1e-9 + return ms * 1000 + + +def main(): + bench_flash_attention.run(save_path='.', print_data=True) + + +if __name__ == '__main__': + sys.exit(main()) diff --git a/python/perf-kernels/06-fused-attention-fwd-transV.py b/python/perf-kernels/06-fused-attention-fwd-transV.py new file mode 100644 index 000000000000..53517a395c8d --- /dev/null +++ b/python/perf-kernels/06-fused-attention-fwd-transV.py @@ -0,0 +1,308 @@ +""" +Fused Attention +=============== + +This is a Triton implementation of the Flash Attention v2 algorithm from Tri Dao (https://tridao.me/publications/flash2/flash2.pdf) + +Extra Credits: +- Original flash attention paper (https://arxiv.org/abs/2205.14135) +- Rabe and Staats (https://arxiv.org/pdf/2112.05682v2.pdf) +- Adam P. Goucher for simplified vector math + +""" + +import pytest +import torch +import sys + +import triton +import triton.language as tl + +# Pick the fp8 data type + +# AMD E5M2B16 +# float8:tl.constexpr = torch.float8_e5m2fnuz + +# AMD E4M3B8 +# Note: When picking this f8 data type, scaling is required when using f8 +# for the second gemm +TORCH_HAS_FP8E4 = hasattr(torch, 'float8_e4m3fnuz') +float8: tl.constexpr = None if not TORCH_HAS_FP8E4 else torch.float8_e4m3fnuz + + +@triton.jit +def max_fn(x, y): + return tl.math.max(x, y) + + +@triton.jit +def _attn_fwd( + Q, + K, + V, + sm_scale, + M, + Out, + stride_qz, + stride_qh, + stride_qm, + stride_qk, + stride_kz, + stride_kh, + stride_kn, + stride_kk, + stride_vz, + stride_vh, + stride_vn, + stride_vk, + stride_oz, + stride_oh, + stride_om, + stride_on, + Z, + H, + N_CTX, + BLOCK_DMODEL: tl.constexpr, + BLOCK_M: tl.constexpr, + BLOCK_N: tl.constexpr, + pre_load_v: tl.constexpr, +): + start_m = tl.program_id(0) + off_hz = tl.program_id(1) + qkv_offset = off_hz * stride_qh + Q_block_ptr = tl.make_block_ptr(base=Q + qkv_offset, shape=(N_CTX, BLOCK_DMODEL), strides=(stride_qm, stride_qk), + offsets=(start_m * BLOCK_M, 0), block_shape=(BLOCK_M, BLOCK_DMODEL), order=(1, 0)) + K_block_ptr = tl.make_block_ptr(base=K + qkv_offset, shape=(BLOCK_DMODEL, N_CTX), strides=(stride_kk, stride_kn), + offsets=(0, 0), block_shape=(BLOCK_DMODEL, BLOCK_N), order=(0, 1)) + V_block_ptr = tl.make_block_ptr(base=V + qkv_offset, shape=(N_CTX, BLOCK_DMODEL), strides=(stride_vk, stride_vn), + offsets=(0, 0), block_shape=(BLOCK_N, BLOCK_DMODEL), order=(0, 1)) + # initialize offsets + # offs_m = start_m * BLOCK_M + tl.arange(0, BLOCK_M) + # offs_n = tl.arange(0, BLOCK_N) + # initialize pointer to m and l + m_i = tl.zeros([BLOCK_M], dtype=tl.float32) - float("inf") + l_i = tl.zeros([BLOCK_M], dtype=tl.float32) + 1.0 + acc = tl.zeros([BLOCK_M, BLOCK_DMODEL], dtype=tl.float32) + # scale sm_scale by log_2(e) and use + # 2^x instead of exp in the loop because CSE and LICM + # don't work as expected with `exp` in the loop + qk_scale = sm_scale * 1.44269504 + q = tl.load(Q_block_ptr) + # it's even better to multiply the qk_scale and convert to f16 + # than doing it inside the loop + # So conversion is quite cheap + q = (q * qk_scale).to(q.dtype) + lo, hi = 0, N_CTX + # loop over k, v and update accumulator + for start_n in range(lo, hi, BLOCK_N): + start_n = tl.multiple_of(start_n, BLOCK_N) + # -- compute qk ---- + k = tl.load(K_block_ptr) + if pre_load_v: + v = tl.load(V_block_ptr) + qk = tl.zeros([BLOCK_M, BLOCK_N], dtype=tl.float32) + qk += tl.dot(q, k) + #qk = (qk * qk_scale) + m_ij = tl.maximum(m_i, tl.max(qk, 1)) + qk = qk - m_ij[:, None] + p = tl.math.exp2(qk) + # -- update output accumulator -- + alpha = tl.math.exp2(m_i - m_ij) + acc = acc * alpha[:, None] + if not pre_load_v: + v = tl.load(V_block_ptr) + acc += tl.dot(p.to(v.dtype), v) + # -- update m_i and l_i + l_ij = tl.sum(p, 1) + l_i = l_i * alpha + l_ij + # update m_i and l_i + m_i = m_ij + V_block_ptr = tl.advance(V_block_ptr, (BLOCK_N, 0)) + K_block_ptr = tl.advance(K_block_ptr, (0, BLOCK_N)) + acc = acc / l_i[:, None] + # write back O + O_block_ptr = tl.make_block_ptr(base=Out + qkv_offset, shape=(N_CTX, BLOCK_DMODEL), strides=(stride_om, stride_on), + offsets=(start_m * BLOCK_M, 0), block_shape=(BLOCK_M, BLOCK_DMODEL), order=(1, 0)) + tl.store(O_block_ptr, acc.to(Out.type.element_ty)) + + +empty = torch.empty(128, device="cuda") + + +class _attention(torch.autograd.Function): + + @staticmethod + def forward(ctx, q, k, v, sm_scale): + # shape constraints + Lq, Lk, Lv = q.shape[-1], k.shape[-1], v.shape[-2] + assert Lq == Lk and Lk == Lv + assert Lk in {16, 32, 64, 128} + o = torch.empty_like(q, dtype=v.dtype) + if torch.version.hip is None: + BLOCK_M = 128 + BLOCK_N = 64 if Lk <= 64 else 32 + num_stages = 4 if Lk <= 64 else 3 + num_warps = 4 if Lk <= 64 else 8 + + ## hardcoded best perf_configs for MI250 + if Lk == 64: + ## D_HEAD = 64 + BLOCK_M = 128 + BLOCK_N = 64 + waves_per_eu = 3 + num_warps = 4 + num_stages = 1 + ## causal=False likes to pre load v but causal=True does not + pre_load_v = False if causal else True + slice_k_tile = 32 + kpack = 1 + else: + ## D_HEAD = 128 + ## For fp16, pick BLOCK_M=256, num_warps=8 + ## For fp8, pick BLOCK_M=128, num_warps=4 + ## TODO (zhanglx): add tuning infra for FA + BLOCK_M = 128 if TORCH_HAS_FP8E4 and q.dtype == torch.float8_e4m3fnuz else 256 + BLOCK_N = 128 + waves_per_eu = 2 + num_warps = BLOCK_M // 32 + num_stages = 1 + pre_load_v = False + slice_k_tile = 32 + kpack = 1 + + grid = (triton.cdiv(q.shape[2], BLOCK_M), q.shape[0] * q.shape[1], 1) + M = torch.empty((q.shape[0] * q.shape[1], q.shape[2]), device=q.device, dtype=torch.float32) + + _attn_fwd[grid]( + q, + k, + v, + sm_scale, + M, + o, + q.stride(0), + q.stride(1), + q.stride(2), + q.stride(3), + k.stride(0), + k.stride(1), + k.stride(2), + k.stride(3), + v.stride(0), + v.stride(1), + v.stride(2), + v.stride(3), + o.stride(0), + o.stride(1), + o.stride(2), + o.stride(3), + q.shape[0], + q.shape[1], + N_CTX=q.shape[2], + BLOCK_DMODEL=Lk, + BLOCK_M=BLOCK_M, + BLOCK_N=BLOCK_N, + waves_per_eu=waves_per_eu, + num_warps=num_warps, + num_stages=num_stages, + pre_load_v=pre_load_v, + slice_k_tile=slice_k_tile, + kpack=kpack, + ) + + return o + + +attention = _attention.apply + +name_to_torch_types = {'fp16': torch.float16, 'bf16': torch.bfloat16, 'fp8': float8} + + +@pytest.mark.parametrize('Z, H, N_CTX, D_HEAD, dtype', + [(*shape, dtype) + for shape in [(4, 48, 1024, 128), (4, 48, 2048, 128), (4, 48, 4096, 128)] + for dtype in ['fp16', 'bf16', 'fp8']]) +def test_op_fwd(Z, H, N_CTX, D_HEAD, dtype): + torch.manual_seed(20) + init_dtype = torch.float16 if dtype == 'fp8' else name_to_torch_types[dtype] + q = (torch.empty((Z, H, N_CTX, D_HEAD), dtype=init_dtype, device="cuda").normal_(mean=0., std=0.5).requires_grad_()) + k = (torch.empty((Z, H, N_CTX, D_HEAD), dtype=init_dtype, device="cuda").normal_(mean=0., std=0.5).requires_grad_()) + v = (torch.empty((Z, H, D_HEAD, N_CTX), dtype=init_dtype, device="cuda").normal_(mean=0., std=0.5).requires_grad_()) + sm_scale = 0.5 + # reference implementation + # M = torch.tril(torch.ones((N_CTX, N_CTX), device="cuda")) + p = torch.matmul(q, k.transpose(2, 3)) * sm_scale + p = torch.softmax(p.float(), dim=-1).to(q.dtype) + ref_out = torch.matmul(p, v.transpose(2, 3)) + # triton implementation + # q,k casting for partial fp8 + q = q.to(name_to_torch_types[dtype]) + k = k.to(name_to_torch_types[dtype]) + # dout = torch.randn_like(q, dtype=torch.float16) + tri_out = attention(q, k, v, sm_scale) + # compare + atol = 1.4e-1 if dtype == 'fp8' else 1e-2 + rtol = 1e-2 if dtype == 'fp8' else 3e-3 + torch.testing.assert_close(ref_out, tri_out, atol=atol, rtol=rtol) + + +try: + FLASH_VER = 2 +except BaseException: + try: + FLASH_VER = 1 + except BaseException: + FLASH_VER = None +HAS_FLASH = FLASH_VER is not None + +# vary seq length for fixed head and batch=4 +configs = [] +for dtype in ['fp16', 'bf16', 'fp8']: + for D_HEAD in [128]: + for causal in [False]: + configs.append( + triton.testing.Benchmark( + x_names=['BATCH', 'H', 'N_CTX'], x_vals=[ + (16, 16, 1024), + (8, 16, 2048), + (4, 16, 4096), + (2, 16, 8192), + (1, 16, 16384), + (4, 48, 1024), + (4, 48, 2048), + (4, 48, 4096), + (4, 48, 8192), + (4, 48, 16384), + ], line_arg='provider', line_vals=['triton'], line_names=['Triton'], + #styles=[('red', '-'), ('blue', '-')], + ylabel='ms', plot_name=f'fused-attention-fwd-d{D_HEAD}-causal={causal}-{dtype}', + args={'D_HEAD': D_HEAD, 'dtype': dtype, 'causal': causal})) + + +@triton.testing.perf_report(configs) +def bench_flash_attention(BATCH, H, N_CTX, D_HEAD, causal, provider, dtype, device="cuda"): + if dtype == 'fp8' and not TORCH_HAS_FP8E4: + sys.exit("fp8 is not available") + warmup = 25 + rep = 100 + init_dtype = torch.float16 if dtype != 'bf16' else torch.bfloat16 + q = torch.randn((BATCH, H, N_CTX, D_HEAD), dtype=init_dtype, device="cuda", requires_grad=True) + k = torch.randn((BATCH, H, N_CTX, D_HEAD), dtype=init_dtype, device="cuda", requires_grad=True) + v = torch.randn((BATCH, H, D_HEAD, N_CTX), dtype=init_dtype, device="cuda", requires_grad=True) + sm_scale = 1.3 + # q,k casting for partial fp8 + q = q.to(name_to_torch_types[dtype]) + k = k.to(name_to_torch_types[dtype]) + fn = lambda: attention(q, k, v, sm_scale) + ms = triton.testing.do_bench(fn, warmup=warmup, rep=rep) + flops_per_matmul = 2. * BATCH * H * N_CTX * N_CTX * D_HEAD + total_flops = 2 * flops_per_matmul + return total_flops / ms * 1e-9 + + +def main(): + bench_flash_attention.run(save_path='.', print_data=True) + + +if __name__ == '__main__': + sys.exit(main()) diff --git a/python/perf-kernels/06-fused-attention-transV.py b/python/perf-kernels/06-fused-attention-transV.py new file mode 100644 index 000000000000..60113d3aa17d --- /dev/null +++ b/python/perf-kernels/06-fused-attention-transV.py @@ -0,0 +1,928 @@ +""" +Fused Attention +=============== + +This is a Triton implementation of the Flash Attention v2 algorithm from Tri Dao (https://tridao.me/publications/flash2/flash2.pdf) + +Extra Credits: +- Original flash attention paper (https://arxiv.org/abs/2205.14135) +- Rabe and Staats (https://arxiv.org/pdf/2112.05682v2.pdf) +- Adam P. Goucher for simplified vector math + +""" + +import pytest +import torch + +import triton +import triton.language as tl + +torch_dtype: tl.constexpr = torch.float16 +TORCH_HAS_FP8E5 = hasattr(torch, 'float8_e5m2fnuz') +if TORCH_HAS_FP8E5: + torch_dtype: tl.constexpr = torch.float8_e5m2fnuz + + +@triton.jit +def max_fn(x, y): + return tl.math.max(x, y) + + +@triton.jit +def _attn_fwd_inner( + acc, + l_i, + m_i, + q, + K_block_ptr, + V_block_ptr, + start_m, + BLOCK_M: tl.constexpr, + BLOCK_DMODEL: tl.constexpr, + BLOCK_N: tl.constexpr, + STAGE: tl.constexpr, + offs_m: tl.constexpr, + offs_n: tl.constexpr, + N_CTX, + pre_load_v: tl.constexpr, +): + # range of values handled by this stage + if STAGE == 1: + lo, hi = 0, start_m * BLOCK_M + elif STAGE == 2: + lo, hi = start_m * BLOCK_M, (start_m + 1) * BLOCK_M + lo = tl.multiple_of(lo, BLOCK_M) + K_block_ptr = tl.advance(K_block_ptr, (0, lo)) + V_block_ptr = tl.advance(V_block_ptr, (lo, 0)) + # causal = False + else: + lo, hi = 0, N_CTX + # loop over k, v and update accumulator + for start_n in range(lo, hi, BLOCK_N): + start_n = tl.multiple_of(start_n, BLOCK_N) + # -- compute qk ---- + k = tl.load(K_block_ptr) + if pre_load_v: + v = tl.load(V_block_ptr) + qk = tl.zeros([BLOCK_M, BLOCK_N], dtype=tl.float32) + if STAGE == 2: + mask = offs_m[:, None] >= (start_n + offs_n[None, :]) + qk = tl.where(mask, qk, float("-inf")) + qk += tl.dot(q, k) + m_ij = tl.maximum(m_i, tl.max(qk, 1)) + qk = qk - m_ij[:, None] + p = tl.math.exp2(qk) + # -- update output accumulator -- + alpha = tl.math.exp2(m_i - m_ij) + acc = acc * alpha[:, None] + if not pre_load_v: + v = tl.load(V_block_ptr) + acc += tl.dot(p.to(v.dtype), v) + # -- update m_i and l_i + l_ij = tl.sum(p, 1) + l_i = l_i * alpha + l_ij + # update m_i and l_i + m_i = m_ij + V_block_ptr = tl.advance(V_block_ptr, (BLOCK_N, 0)) + K_block_ptr = tl.advance(K_block_ptr, (0, BLOCK_N)) + return acc, l_i, m_i + + +@triton.autotune( + configs=[ + triton.Config({'BLOCK_M': 256, 'BLOCK_N': 64, 'waves_per_eu': 2, 'slice_k_tile': 0, 'pre_load_v': False}, + num_stages=1, num_warps=8), + triton.Config({'BLOCK_M': 128, 'BLOCK_N': 128, 'waves_per_eu': 2, 'slice_k_tile': 0, 'pre_load_v': False}, + num_stages=1, num_warps=4), + triton.Config({'BLOCK_M': 256, 'BLOCK_N': 128, 'waves_per_eu': 2, 'slice_k_tile': 0, 'pre_load_v': False}, + num_stages=1, num_warps=8), + triton.Config({'BLOCK_M': 128, 'BLOCK_N': 64, 'waves_per_eu': 3, 'slice_k_tile': 0, 'pre_load_v': True}, + num_stages=1, num_warps=4), # d64-False + triton.Config({'BLOCK_M': 128, 'BLOCK_N': 64, 'waves_per_eu': 3, 'slice_k_tile': 0, 'pre_load_v': False}, + num_stages=1, num_warps=4), # d64-True + triton.Config({'BLOCK_M': 256, 'BLOCK_N': 64, 'waves_per_eu': 2, 'slice_k_tile': 32, 'pre_load_v': False}, + num_stages=1, num_warps=8), + triton.Config({'BLOCK_M': 128, 'BLOCK_N': 128, 'waves_per_eu': 2, 'slice_k_tile': 32, 'pre_load_v': False}, + num_stages=1, num_warps=4), + triton.Config({'BLOCK_M': 256, 'BLOCK_N': 128, 'waves_per_eu': 2, 'slice_k_tile': 32, 'pre_load_v': False}, + num_stages=1, num_warps=8), + triton.Config({'BLOCK_M': 128, 'BLOCK_N': 64, 'waves_per_eu': 3, 'slice_k_tile': 32, 'pre_load_v': True}, + num_stages=1, num_warps=4), # d64-False + triton.Config({'BLOCK_M': 128, 'BLOCK_N': 64, 'waves_per_eu': 3, 'slice_k_tile': 32, 'pre_load_v': False}, + num_stages=1, num_warps=4), # d64-True + triton.Config({'BLOCK_M': 256, 'BLOCK_N': 64, 'waves_per_eu': 2, 'slice_k_tile': 64, 'pre_load_v': False}, + num_stages=1, num_warps=8), + triton.Config({'BLOCK_M': 128, 'BLOCK_N': 128, 'waves_per_eu': 2, 'slice_k_tile': 64, 'pre_load_v': False}, + num_stages=1, num_warps=4), + triton.Config({'BLOCK_M': 256, 'BLOCK_N': 128, 'waves_per_eu': 2, 'slice_k_tile': 64, 'pre_load_v': False}, + num_stages=1, num_warps=8), + triton.Config({'BLOCK_M': 128, 'BLOCK_N': 64, 'waves_per_eu': 3, 'slice_k_tile': 64, 'pre_load_v': True}, + num_stages=1, num_warps=4), # d64-False + triton.Config({'BLOCK_M': 128, 'BLOCK_N': 64, 'waves_per_eu': 3, 'slice_k_tile': 64, 'pre_load_v': False}, + num_stages=1, num_warps=4), # d64-True + ], + key=['Z', 'H', 'N_CTX', 'STAGE', 'BLOCK_DMODEL'], +) +@triton.jit +def _attn_fwd( + Q, + K, + V, + sm_scale, + M, + Out, + stride_qz, + stride_qh, + stride_qm, + stride_qk, + stride_kz, + stride_kh, + stride_kn, + stride_kk, + stride_vz, + stride_vh, + stride_vn, + stride_vk, + stride_oz, + stride_oh, + stride_om, + stride_on, + Z, + H, + N_CTX, + BLOCK_DMODEL: tl.constexpr, + STAGE: tl.constexpr, + BLOCK_M: tl.constexpr, + BLOCK_N: tl.constexpr, + pre_load_v: tl.constexpr, +): + start_m = tl.program_id(0) + off_hz = tl.program_id(1) + qkv_offset = off_hz * stride_qh + Q_block_ptr = tl.make_block_ptr(base=Q + qkv_offset, shape=(N_CTX, BLOCK_DMODEL), strides=(stride_qm, stride_qk), + offsets=(start_m * BLOCK_M, 0), block_shape=(BLOCK_M, BLOCK_DMODEL), order=(1, 0)) + K_block_ptr = tl.make_block_ptr(base=K + qkv_offset, shape=(BLOCK_DMODEL, N_CTX), strides=(stride_kk, stride_kn), + offsets=(0, 0), block_shape=(BLOCK_DMODEL, BLOCK_N), order=(0, 1)) + V_block_ptr = tl.make_block_ptr(base=V + qkv_offset, shape=(N_CTX, BLOCK_DMODEL), strides=(stride_vk, stride_vn), + offsets=(0, 0), block_shape=(BLOCK_N, BLOCK_DMODEL), order=(0, 1)) + # initialize offsets + offs_m = start_m * BLOCK_M + tl.arange(0, BLOCK_M) + offs_n = tl.arange(0, BLOCK_N) + # initialize pointer to m and l + m_i = tl.zeros([BLOCK_M], dtype=tl.float32) - float("inf") + l_i = tl.zeros([BLOCK_M], dtype=tl.float32) + 1.0 + acc = tl.zeros([BLOCK_M, BLOCK_DMODEL], dtype=tl.float32) + # scale sm_scale by log_2(e) and use + # 2^x instead of exp in the loop because CSE and LICM + # don't work as expected with `exp` in the loop + qk_scale = sm_scale * 1.44269504 + # load q: it will stay in SRAM throughout on NV GPUs but in VGPRs on AMD GPUs + q = tl.load(Q_block_ptr) + q = (q * qk_scale).to(q.dtype) + # stage 1: off-band + # For causal = True, STAGE = 3 and _attn_fwd_inner gets 1 as its STAGE + # For causal = False, STAGE = 1, and _attn_fwd_inner gets 3 as its STAGE + if STAGE & 1: + acc, l_i, m_i = _attn_fwd_inner( + acc, + l_i, + m_i, + q, + K_block_ptr, + V_block_ptr, + start_m, + BLOCK_M, + BLOCK_DMODEL, + BLOCK_N, + 4 - STAGE, + offs_m, + offs_n, + N_CTX, + pre_load_v, + ) + # stage 2: on-band + if STAGE & 2: + # barrier makes it easier for compiler to schedule the + # two loops independently + tl.debug_barrier() + acc, l_i, m_i = _attn_fwd_inner( + acc, + l_i, + m_i, + q, + K_block_ptr, + V_block_ptr, + start_m, + BLOCK_M, + BLOCK_DMODEL, + BLOCK_N, + 2, + offs_m, + offs_n, + N_CTX, + pre_load_v, + ) + # epilogue + # write back m + acc = acc / l_i[:, None] + m_ptrs = M + off_hz * N_CTX + offs_m + tl.store(m_ptrs, m_i + tl.math.log2(l_i)) + # write back O + O_block_ptr = tl.make_block_ptr(base=Out + qkv_offset, shape=(N_CTX, BLOCK_DMODEL), strides=(stride_om, stride_on), + offsets=(start_m * BLOCK_M, 0), block_shape=(BLOCK_M, BLOCK_DMODEL), order=(1, 0)) + tl.store(O_block_ptr, acc.to(Out.type.element_ty)) + + +@triton.jit +def _bwd_preprocess( + Out, + DO, + NewDO, + Delta, + BLOCK_M: tl.constexpr, + D_HEAD: tl.constexpr, +): + off_m = tl.program_id(0) * BLOCK_M + tl.arange(0, BLOCK_M) + off_n = tl.arange(0, D_HEAD) + # load + o = tl.load(Out + off_m[:, None] * D_HEAD + off_n[None, :]).to(tl.float32) + do = tl.load(DO + off_m[:, None] * D_HEAD + off_n[None, :]).to(tl.float32) + # compute + delta = tl.sum(o * do, axis=1) + # write-back + tl.store(NewDO + off_m[:, None] * D_HEAD + off_n[None, :], do) + tl.store(Delta + off_m, delta) + + +@triton.jit +def _bwd_kernel( + Q, + K, + V, + sm_scale, + Out, + DO, + DQ, + DK, + DV, + L, + D, + stride_qz, + stride_qh, + stride_qm, + stride_qk, + stride_kz, + stride_kh, + stride_kn, + stride_kk, + stride_vz, + stride_vh, + stride_vk, + stride_vn, + Z, + H, + N_CTX, + P_SEQ, + num_block_q, + num_block_kv, + BLOCK_M: tl.constexpr, + BLOCK_DMODEL: tl.constexpr, + BLOCK_N: tl.constexpr, + CAUSAL: tl.constexpr, +): + off_hz = tl.program_id(0) + off_z = off_hz // H + off_h = off_hz % H + qk_scale = sm_scale * 1.44269504 + # offset pointers for batch/head + Q += off_z * stride_qz + off_h * stride_qh + K += off_z * stride_kz + off_h * stride_kh + V += off_z * stride_vz + off_h * stride_vh + DO += off_z * stride_qz + off_h * stride_qh + DQ += off_z * stride_qz + off_h * stride_qh + DK += off_z * stride_kz + off_h * stride_kh + DV += off_z * stride_vz + off_h * stride_vh + # See fwd pass above for explanation. + qk_scale = sm_scale * 1.44269504 + for start_n in range(0, num_block_kv): + if CAUSAL: + lo = tl.math.max(start_n * BLOCK_M - P_SEQ, 0) + else: + lo = 0 + # initialize row/col offsets + offs_qm = lo + tl.arange(0, BLOCK_M) + offs_n = start_n * BLOCK_M + tl.arange(0, BLOCK_M) + offs_m = tl.arange(0, BLOCK_N) + offs_k = tl.arange(0, BLOCK_DMODEL) + # initialize pointers to value-like data + q_ptrs = Q + (offs_qm[:, None] * stride_qm + offs_k[None, :] * stride_qk) + k_ptrs = K + (offs_n[:, None] * stride_kn + offs_k[None, :] * stride_kk) + v_ptrs = V + (offs_n[None, :] * stride_qm + offs_k[:, None] * stride_qk) + do_ptrs = DO + (offs_qm[:, None] * stride_qm + offs_k[None, :] * stride_qk) + dq_ptrs = DQ + (offs_qm[:, None] * stride_qm + offs_k[None, :] * stride_qk) + # pointer to row-wise quantities in value-like data + D_ptrs = D + off_hz * N_CTX + l_ptrs = L + off_hz * N_CTX + # initialize dk amd dv + dk = tl.zeros([BLOCK_M, BLOCK_DMODEL], dtype=tl.float32) + dv = tl.zeros([BLOCK_M, BLOCK_DMODEL], dtype=tl.float32) + # k and v stay in SRAM throughout + k = tl.load(k_ptrs) + v = tl.load(v_ptrs) + # loop over rows + for start_m in range(lo, num_block_q * BLOCK_M, BLOCK_M): + offs_m_curr = start_m + offs_m + # load q, k, v, do on-chip + q = tl.load(q_ptrs) + # recompute p = softmax(qk, dim=-1).T + if CAUSAL: + qk = tl.where(P_SEQ + offs_m_curr[:, None] >= (offs_n[None, :]), float(0.), float("-inf")) + else: + qk = tl.zeros([BLOCK_M, BLOCK_N], dtype=tl.float32) + qk += tl.dot(q, tl.trans(k)) + l_i = tl.load(l_ptrs + offs_m_curr) + p = tl.math.exp2(qk * qk_scale - l_i[:, None]) + # compute dv + do = tl.load(do_ptrs) + dv += tl.dot(tl.trans(p.to(Q.dtype.element_ty)), do) + # compute dp = dot(v, do) + Di = tl.load(D_ptrs + offs_m_curr) + dp = tl.zeros([BLOCK_M, BLOCK_N], dtype=tl.float32) - Di[:, None] + dp += tl.dot(do, v) + # compute ds = p * (dp - delta[:, None]) + ds = p * dp * sm_scale + # compute dk = dot(ds.T, q) + dk += tl.dot(tl.trans(ds.to(Q.dtype.element_ty)), q) + # compute dq + dq = tl.load(dq_ptrs) + dq += tl.dot(ds.to(Q.dtype.element_ty), k) + tl.store(dq_ptrs, dq) + # increment pointers + dq_ptrs += BLOCK_M * stride_qm + q_ptrs += BLOCK_M * stride_qm + do_ptrs += BLOCK_M * stride_qm + # write-back + dk_ptrs = DK + (offs_n[:, None] * stride_kn + offs_k[None, :] * stride_kk) + dv_ptrs = DV + (offs_n[:, None] * stride_qm + offs_k[None, :] * stride_qk) + tl.store(dk_ptrs, dk) + tl.store(dv_ptrs, dv) + + +@triton.jit +def _bwd_kernel_dk_dv( + Q, + K, + V, + sm_scale, + Out, + DO, + DK, + DV, + L, + D, + stride_qz, + stride_qh, + stride_qm, + stride_qk, + stride_kz, + stride_kh, + stride_kn, + stride_kk, + stride_vz, + stride_vh, + stride_vk, + stride_vn, + Z, + H, + N_CTX, + BLOCK_M: tl.constexpr, + BLOCK_DMODEL: tl.constexpr, + BLOCK_N: tl.constexpr, +): + start_m = tl.program_id(0) + off_hz = tl.program_id(1) + # Q is consumed depending on block ID. Every block uses + # previous block offset by BLOCK_M x D_HEAD. + qvk_offset = off_hz * stride_qh + qdo_offset = qvk_offset + start_m * BLOCK_M * stride_qm + # initialize offsets + offs_m = start_m * BLOCK_M + tl.arange(0, BLOCK_M) + offs_n = tl.arange(0, BLOCK_N) + # offs_d = tl.arange(0, BLOCK_DMODEL) + # Initialize pointers to Q, K, V + Q_block_ptr = tl.make_block_ptr(base=Q + qdo_offset, shape=(N_CTX, BLOCK_DMODEL), strides=(stride_qm, stride_qk), + offsets=(0, 0), block_shape=(BLOCK_N, BLOCK_DMODEL), order=(1, 0)) + K_block_ptr = tl.make_block_ptr(base=K + qvk_offset, shape=(BLOCK_DMODEL, N_CTX), strides=(stride_kk, stride_kn), + offsets=(0, start_m * BLOCK_M), block_shape=(BLOCK_DMODEL, BLOCK_N), order=(0, 1)) + V_block_ptr = tl.make_block_ptr(base=V + qvk_offset, shape=(BLOCK_DMODEL, N_CTX), strides=(stride_vn, stride_vk), + offsets=(0, start_m * BLOCK_M), block_shape=(BLOCK_DMODEL, BLOCK_N), order=(0, 1)) + DO_block_ptr = tl.make_block_ptr(base=DO + qdo_offset, shape=(N_CTX, BLOCK_DMODEL), strides=(stride_qm, stride_qk), + offsets=(0, 0), block_shape=(BLOCK_N, BLOCK_DMODEL), order=(1, 0)) + # pointer to row-wise quantities in value-like data + D_ptrs = D + off_hz * N_CTX + l_ptrs = L + off_hz * N_CTX + qk_scale = sm_scale * 1.44269504 + # load k and v: they will stay in SRAM throughout + k = tl.load(K_block_ptr) + k = (k * qk_scale).to(k.dtype) + v = tl.load(V_block_ptr) + dv = tl.zeros([BLOCK_M, BLOCK_DMODEL], dtype=tl.float32) + dk = tl.zeros([BLOCK_M, BLOCK_DMODEL], dtype=tl.float32) + # This lower loop bound is because of the causal mask. We create a lower triangular + # result. The upper triangular is -inf (becomes 0 when we do e^x). As such, it can + # be ignored in the GEMM. + lo = start_m * BLOCK_M + hi = N_CTX + # loop over q, do + for start_n in range(lo, hi, BLOCK_N): + offs_m_curr = offs_n[:, None] + start_n + # -- load q, do -- + q = tl.load(Q_block_ptr) + do = tl.load(DO_block_ptr) + # -- compute qk ---- + qk = tl.dot(q, k) + qk = tl.where(offs_m_curr >= offs_m[None, :], qk, float("-inf")) + l_i = tl.load(l_ptrs + offs_m_curr) + p = tl.math.exp2(qk - l_i) + # -- compute dv ---- + dv += tl.dot(tl.trans(p.to(Q.dtype.element_ty)), do) + # compute dp = dot(v, do) + Di = tl.load(D_ptrs + offs_m_curr) + dp = tl.zeros([BLOCK_N, BLOCK_M], dtype=tl.float32) - Di + dp += tl.dot(do, v) + # compute ds = p * (dp - delta[:, None]) + ds = p * dp + # compute dk + dk += tl.dot(tl.trans(ds.to(Q.dtype.element_ty)), q) + # update pointers + Q_block_ptr = tl.advance(Q_block_ptr, (BLOCK_N, 0)) + DO_block_ptr = tl.advance(DO_block_ptr, (BLOCK_N, 0)) + # initialize pointers to output + DK_block_ptr = tl.make_block_ptr(base=DK + qvk_offset, shape=(N_CTX, BLOCK_DMODEL), strides=(stride_kn, stride_kk), + offsets=(start_m * BLOCK_M, 0), block_shape=(BLOCK_M, BLOCK_DMODEL), order=(1, 0)) + DV_block_ptr = tl.make_block_ptr(base=DV + qvk_offset, shape=(N_CTX, BLOCK_DMODEL), strides=(stride_vk, stride_vn), + offsets=(start_m * BLOCK_M, 0), block_shape=(BLOCK_M, BLOCK_DMODEL), order=(1, 0)) + tl.store(DK_block_ptr, (dk * sm_scale).to(k.dtype)) + tl.store(DV_block_ptr, dv.to(v.dtype)) + + +@triton.jit +def _bwd_kernel_dq( + Q, + K, + V, + sm_scale, + Out, + DO, + DQ, + L, + D, + stride_qz, + stride_qh, + stride_qm, + stride_qk, + stride_kz, + stride_kh, + stride_kn, + stride_kk, + stride_vz, + stride_vh, + stride_vk, + stride_vn, + Z, + H, + N_CTX, + BLOCK_M: tl.constexpr, + BLOCK_DMODEL: tl.constexpr, + BLOCK_N: tl.constexpr, +): + start_m = tl.program_id(0) + off_hz = tl.program_id(1) + qvk_offset = off_hz * stride_qh + # initialize offsets + offs_m = start_m * BLOCK_M + tl.arange(0, BLOCK_M) + offs_n = tl.arange(0, BLOCK_N) + # offs_d = tl.arange(0, BLOCK_DMODEL) + # Initialize pointers to Q, K, V + Q_block_ptr = tl.make_block_ptr(base=Q + qvk_offset, shape=(N_CTX, BLOCK_DMODEL), strides=(stride_qm, stride_qk), + offsets=(start_m * BLOCK_M, 0), block_shape=(BLOCK_M, BLOCK_DMODEL), order=(1, 0)) + K_block_ptr = tl.make_block_ptr(base=K + qvk_offset, shape=(BLOCK_DMODEL, N_CTX), strides=(stride_kk, stride_kn), + offsets=(0, 0), block_shape=(BLOCK_DMODEL, BLOCK_N), order=(0, 1)) + V_block_ptr = tl.make_block_ptr(base=V + qvk_offset, shape=(BLOCK_DMODEL, N_CTX), strides=(stride_vn, stride_vk), + offsets=(0, 0), block_shape=(BLOCK_DMODEL, BLOCK_N), order=(0, 1)) + DO_block_ptr = tl.make_block_ptr(base=DO + qvk_offset, shape=(N_CTX, BLOCK_DMODEL), strides=(stride_qm, stride_qk), + offsets=(start_m * BLOCK_M, 0), block_shape=(BLOCK_M, BLOCK_DMODEL), order=(1, 0)) + # pointer to row-wise quantities in value-like data + D_ptrs = D + off_hz * N_CTX + l_ptrs = L + off_hz * N_CTX + qk_scale = sm_scale * 1.44269504 + # load q and do: they will stay in SRAM throughout + q = tl.load(Q_block_ptr) + q = (q * qk_scale).to(q.dtype) + do = tl.load(DO_block_ptr) + Di = tl.load(D_ptrs + offs_m) + l_i = tl.load(l_ptrs + offs_m) + dq = tl.zeros([BLOCK_M, BLOCK_DMODEL], dtype=tl.float32) + # loop over k, v + lo = 0 + hi = (start_m + 1) * BLOCK_M + for start_n in range(lo, hi, BLOCK_N): + # -- load k, v -- + k = tl.load(K_block_ptr) + v = tl.load(V_block_ptr) + # -- compute qk ---- + qk = tl.dot(q, k) + qk = tl.where(offs_m[:, None] >= (offs_n[None, :] + start_n), qk, float("-inf")) + p = tl.math.exp2(qk - l_i[:, None]) + # compute dp = dot(v, do) + dp = tl.zeros([BLOCK_M, BLOCK_N], dtype=tl.float32) - Di[:, None] + dp += tl.dot(do, v) + # compute ds = p * (dp - delta[:, None]) + ds = p * dp + # compute dq. Unfortunately we cannot avoid transpose here as this loop + # uses k both normal and transpose. + dq += tl.dot(ds.to(Q.dtype.element_ty), tl.trans(k)) + # update pointers + K_block_ptr = tl.advance(K_block_ptr, (0, BLOCK_N)) + V_block_ptr = tl.advance(V_block_ptr, (0, BLOCK_N)) + # initialize pointers to output + DQ_block_ptr = tl.make_block_ptr(base=DQ + qvk_offset, shape=(N_CTX, BLOCK_DMODEL), strides=(stride_qm, stride_qk), + offsets=(start_m * BLOCK_M, 0), block_shape=(BLOCK_M, BLOCK_DMODEL), order=(1, 0)) + tl.store(DQ_block_ptr, (dq * sm_scale).to(q.dtype)) + + +empty = torch.empty(128, device="cuda") + + +class _attention(torch.autograd.Function): + + @staticmethod + def forward(ctx, q, k, v, causal, sm_scale, split_kernel=False): + # shape constraints + Lq, Lk, Lv = q.shape[-1], k.shape[-1], v.shape[-2] + assert Lq == Lk and Lk == Lv + assert Lk in {16, 32, 64, 128} + o = torch.empty_like(q) + if torch.version.hip is None: + # BLOCK_M = 128 + # BLOCK_N = 64 if Lk <= 64 else 32 + # num_stages = 4 if Lk <= 64 else 3 + # num_warps = 4 if Lk <= 64 else 8 + pass + + stage = 3 if causal else 1 + grid = lambda META: (triton.cdiv(q.shape[2], META['BLOCK_M']), q.shape[0] * q.shape[1], 1) + M = torch.empty((q.shape[0] * q.shape[1], q.shape[2]), device=q.device, dtype=torch.float32) + + _attn_fwd[grid]( + q, + k, + v, + sm_scale, + M, + o, + q.stride(0), + q.stride(1), + q.stride(2), + q.stride(3), + k.stride(0), + k.stride(1), + k.stride(2), + k.stride(3), + v.stride(0), + v.stride(1), + v.stride(2), + v.stride(3), + o.stride(0), + o.stride(1), + o.stride(2), + o.stride(3), + q.shape[0], + q.shape[1], + N_CTX=q.shape[2], + BLOCK_DMODEL=Lk, + STAGE=stage, + ) + + ## restore the grid for bwd kernel + best_config = _attn_fwd.get_best_config() + block_m = int(best_config.__str__().split(",")[0].split("BLOCK_M:")[1]) + grid = (triton.cdiv(q.shape[2], block_m), q.shape[0] * q.shape[1], 1) + + ctx.save_for_backward(q, k, v, o, M) + ctx.grid = grid + ctx.sm_scale = sm_scale + ctx.BLOCK_DMODEL = Lk + ctx.causal = causal + ctx.split_kernel = split_kernel + return o + + @staticmethod + def backward(ctx, do): + # configuration is not supported + assert (not (ctx.split_kernel and not ctx.causal)) + if torch.version.hip is not None: + BLOCK = 64 + else: + BLOCK = 128 + q, k, v, o, L = ctx.saved_tensors + do = do.contiguous() + dq = torch.zeros_like(q, dtype=torch.float32) + dk = torch.empty_like(k) + dv = torch.empty_like(v) + delta = torch.empty_like(L) + do_scaled = torch.empty_like(do) + # Figure out what BLOCK size fwd used and adjust num_blocks accordingly. + # If the two are the same, we don't need this but the bwd pass block size + # is smaller than the fwd so we need this scaling to ensure we loop over all + # values and don't skip some blocks. + # Alternatively we could compute a new grid but this keeps it consistent + # with fwd and easier to reason about. + block_scale = (q.shape[2] // ctx.grid[0]) // BLOCK + _bwd_preprocess[(ctx.grid[0] * ctx.grid[1], )]( + o, + do, + do_scaled, + delta, + BLOCK_M=block_scale * BLOCK, + D_HEAD=ctx.BLOCK_DMODEL, + ) + if not ctx.split_kernel: + _bwd_kernel[(ctx.grid[1], )]( + q, + k, + v, + ctx.sm_scale, + o, + do_scaled, + dq, + dk, + dv, + L, + delta, + q.stride(0), + q.stride(1), + q.stride(2), + q.stride(3), + k.stride(0), + k.stride(1), + k.stride(2), + k.stride(3), + v.stride(0), + v.stride(1), + v.stride(2), + v.stride(3), + q.shape[0], + q.shape[1], + q.shape[2], + block_scale * ctx.grid[0], + BLOCK_M=BLOCK, + BLOCK_N=BLOCK, + BLOCK_DMODEL=ctx.BLOCK_DMODEL, + num_warps=4, + CAUSAL=ctx.causal, + num_stages=1, + ) + else: + dq = torch.zeros_like(q) + _bwd_kernel_dk_dv[(block_scale * ctx.grid[0], ctx.grid[1])]( + q, + k, + v, + ctx.sm_scale, + o, + do_scaled, + dk, + dv, + L, + delta, + q.stride(0), + q.stride(1), + q.stride(2), + q.stride(3), + k.stride(0), + k.stride(1), + k.stride(2), + k.stride(3), + v.stride(0), + v.stride(1), + v.stride(2), + v.stride(3), + q.shape[0], + q.shape[1], + q.shape[2], + BLOCK_M=BLOCK, + BLOCK_N=BLOCK, + BLOCK_DMODEL=ctx.BLOCK_DMODEL, + num_warps=4, + num_stages=1, + ) + _bwd_kernel_dq[ctx.grid]( + q, + k, + v, + ctx.sm_scale, + o, + do_scaled, + dq, + L, + delta, + q.stride(0), + q.stride(1), + q.stride(2), + q.stride(3), + k.stride(0), + k.stride(1), + k.stride(2), + k.stride(3), + v.stride(0), + v.stride(1), + v.stride(2), + v.stride(3), + q.shape[0], + q.shape[1], + q.shape[2], + BLOCK_M=2 * BLOCK, + BLOCK_N=BLOCK, + BLOCK_DMODEL=ctx.BLOCK_DMODEL, + num_warps=4, + waves_per_eu=1, + num_stages=1, + ) + # print(h.asm["ttgir"]) + return dq, dk, dv, None, None, None + + +attention = _attention.apply + + +@pytest.mark.parametrize('Z, H, N_CTX, D_HEAD', [ + (4, 48, 1024, 64), + (4, 48, 2048, 64), + (4, 48, 4096, 64), + (4, 48, 1024, 128), + (4, 48, 2048, 128), + (4, 48, 4096, 128), + #(4, 48, 8192, 64), + #(4, 48, 16384, 64) +]) +@pytest.mark.parametrize('causal', [False, True]) +@pytest.mark.parametrize('dtype', [torch.float16, torch.bfloat16]) +def test_op_fwd(Z, H, N_CTX, D_HEAD, causal, dtype=torch.float16): + torch.manual_seed(20) + q = (torch.empty((Z, H, N_CTX, D_HEAD), dtype=dtype, device="cuda").normal_(mean=0., std=0.5).requires_grad_()) + k = (torch.empty((Z, H, N_CTX, D_HEAD), dtype=dtype, device="cuda").normal_(mean=0., std=0.5).requires_grad_()) + v = (torch.empty((Z, H, D_HEAD, N_CTX), dtype=dtype, device="cuda").normal_(mean=0., std=0.5).requires_grad_()) + sm_scale = 0.5 + # dout = torch.randn_like(q) + # reference implementation + M = torch.tril(torch.ones((N_CTX, N_CTX), device="cuda")) + p = torch.matmul(q, k.transpose(2, 3)) * sm_scale + if causal: + p[:, :, M == 0] = float("-inf") + p = torch.softmax(p.float(), dim=-1).to(v.dtype) + ref_out = torch.matmul(p, v.transpose(2, 3)) + # triton implementation + tri_out = attention(q, k, v, causal, sm_scale) + # compare + assert torch.allclose(ref_out, tri_out, atol=1e-2, rtol=0) + + +@pytest.mark.parametrize('Z, H, N_CTX, D_HEAD', [ + (4, 48, 1024, 64), + (4, 48, 2048, 64), + (4, 48, 4096, 64), + (1, 16, 8192, 64), +]) +@pytest.mark.parametrize('dtype', [torch.float16, torch.bfloat16]) +def test_op_bwd(Z, H, N_CTX, D_HEAD, dtype=torch.float16): + torch.manual_seed(20) + causal = True + q = torch.empty((Z, H, N_CTX, D_HEAD), dtype=dtype, device="cuda").normal_(mean=0., std=0.5).requires_grad_() + k = torch.empty((Z, H, N_CTX, D_HEAD), dtype=dtype, device="cuda").normal_(mean=0., std=0.5).requires_grad_() + v = torch.empty((Z, H, N_CTX, D_HEAD), dtype=dtype, device="cuda").normal_(mean=0., std=0.5).requires_grad_() + sm_scale = 0, 5 + split_kernel = True + dout = torch.randn_like(q) + # reference implementation + M = torch.tril(torch.ones((N_CTX, N_CTX), device="cuda")) + p = torch.matmul(q, k.transpose(2, 3)) * sm_scale + if causal: + p[:, :, M == 0] = float("-inf") + p = torch.softmax(p.float(), dim=-1).to(v.dtype) + ref_out = torch.matmul(p, v) + ref_out.backward(dout) + ref_dv, v.grad = v.grad.clone(), None + ref_dk, k.grad = k.grad.clone(), None + ref_dq, q.grad = q.grad.clone(), None + # # triton implementation + tri_out = attention(q, k, v, causal, sm_scale, split_kernel) + tri_out.backward(dout) + tri_dv, v.grad = v.grad.clone(), None + tri_dk, k.grad = k.grad.clone(), None + tri_dq, q.grad = q.grad.clone(), None + # compare + assert torch.allclose(ref_out, tri_out, atol=1e-2, rtol=0) + if torch.version.hip is None: + assert torch.allclose(ref_dv, tri_dv, atol=1e-2, rtol=0) + # The current block size for MI200 series is 64x64. This results in + # larger differences in float results due to rounding. + else: + assert torch.allclose(ref_dv, tri_dv, atol=5e-2, rtol=0) + assert torch.allclose(ref_dk, tri_dk, atol=5e-2, rtol=0) + assert torch.allclose(ref_dq, tri_dq, atol=5e-2, rtol=0) + + +try: + from flash_attn.flash_attn_interface import \ + flash_attn_qkvpacked_func as flash_attn_func + FLASH_VER = 2 +except BaseException: + try: + from flash_attn.flash_attn_interface import flash_attn_func + FLASH_VER = 1 + except BaseException: + FLASH_VER = None +HAS_FLASH = FLASH_VER is not None + +name_to_torch_types = { + 'fp16': torch.float16, + 'bf16': torch.bfloat16, +} + +# vary seq length for fixed head and batch=4 +configs = [] +for mode in ['fwd']: + for dtype in ["fp16", "bf16"]: + for D_HEAD in [128, 64]: + for causal in [False, True]: + configs.append( + triton.testing.Benchmark( + x_names=['BATCH', 'H', 'N_CTX'], x_vals=[ + (16, 16, 1024), + (8, 16, 2048), + (4, 16, 4096), + (2, 16, 8192), + (1, 16, 16384), + (4, 48, 1024), + (4, 48, 2048), + (4, 48, 4096), + (4, 48, 8192), + (4, 48, 16384), + ], line_arg='provider', line_vals=['triton'] + (['flash'] if HAS_FLASH else []), + line_names=['Triton'] + ([f'Flash-{FLASH_VER}'] if HAS_FLASH else []), styles=[('red', '-'), + ('blue', '-')], + ylabel='ms', plot_name=f'fused-attention-d{D_HEAD}-{mode}-causal={causal}-{dtype}', + args={'D_HEAD': D_HEAD, 'dtype': dtype, 'mode': mode, 'causal': causal})) + + +@triton.testing.perf_report(configs) +def bench_flash_attention(BATCH, H, N_CTX, D_HEAD, causal, mode, provider, dtype, device="cuda"): + assert mode in ['fwd', 'bwd'] + warmup = 25 + rep = 100 + init_dtype = name_to_torch_types[dtype] + split_kernel = False + # Bwd pass only supports causal=True right now + if mode == 'bwd': + causal = True + split_kernel = True + if provider == "triton": + q = torch.randn((BATCH, H, N_CTX, D_HEAD), dtype=init_dtype, device="cuda", requires_grad=True) + k = torch.randn((BATCH, H, N_CTX, D_HEAD), dtype=init_dtype, device="cuda", requires_grad=True) + v = torch.randn((BATCH, H, D_HEAD, N_CTX), dtype=init_dtype, device="cuda", requires_grad=True) + sm_scale = 1.3 + fn = lambda: attention(q, k, v, causal, sm_scale, split_kernel) + if mode == 'bwd': + o = fn() + do = torch.randn_like(o) + fn = lambda: o.backward(do, retain_graph=True) + ms = triton.testing.do_bench(fn, warmup=warmup, rep=rep) + if provider == "flash": + qkv = torch.randn((BATCH, N_CTX, 3, H, D_HEAD), dtype=init_dtype, device=device, requires_grad=True) + if FLASH_VER == 1: + lengths = torch.full((BATCH, ), fill_value=N_CTX, device=device) + cu_seqlens = torch.zeros((BATCH + 1, ), device=device, dtype=torch.int32) + cu_seqlens[1:] = lengths.cumsum(0) + qkv = qkv.reshape(BATCH * N_CTX, 3, H, D_HEAD) + fn = lambda: flash_attn_func(qkv, cu_seqlens, 0., N_CTX, causal=causal) + elif FLASH_VER == 2: + fn = lambda: flash_attn_func(qkv, causal=causal) + else: + raise ValueError(f'unknown {FLASH_VER = }') + if mode == 'bwd': + o = fn() + do = torch.randn_like(o) + fn = lambda: o.backward(do, retain_graph=True) + ms = triton.testing.do_bench(fn, warmup=warmup, rep=rep) + flops_per_matmul = 2. * BATCH * H * N_CTX * N_CTX * D_HEAD + total_flops = 2 * flops_per_matmul + if causal: + total_flops *= 0.5 + if mode == 'bwd': + total_flops *= 2.5 # 2.0(bwd) + 0.5(recompute) + return total_flops / ms * 1e-9 + + +# only works on post-Ampere GPUs right now +bench_flash_attention.run(save_path='.', print_data=True) diff --git a/python/perf-kernels/README.md b/python/perf-kernels/README.md new file mode 100644 index 000000000000..8a247d15fd3d --- /dev/null +++ b/python/perf-kernels/README.md @@ -0,0 +1,82 @@ +# AMD Perf Kernels + +This directory contains customized/tuned/experimental kernels for AMD Instinct series GPUs. +Please make sure your Triton compiler is v2.1 or later, and is from the OpenAI Triton repository +[here](https://github.com/openai/triton). To install Triton, please see +[these](https://github.com/openai/triton/tree/main?tab=readme-ov-file#install-from-source) instructions. + +## `06-fused-attention-transV.py` + +This script is a copy of `tutorials/06-fused-attention.py` with the following +two changes: + +- Tensor V is transposed in the way that seqlen/N_CTX dimension becomes the +fastest changing (a.k.a. leading or least strided) dimension. +This script produces better performance than `tutorials/06-fused-attention.py` +since it has better LDS access efficiency for tensor V. +Note that in the future, we'll improve the LDS access efficiency for +non-transposed tensor V, i.e. head dimension is the fastest changing dimension. +- Only fwd kernel is benchmarked. + +## `06-fused-attention-fwd-transV.py` + +This script is used to produce the best performance for fwd kernel. +It is a copy of `06-fused-attention-transV.py` with the following +changes: + +- All bwd kernels are removed. +- Storing `m` at the end of the fwd kernel is removed. +- Autotuner is removed. All parameters for D=64 ad D=128 are pre-tuned +on MI250X and hard coded. + +Note that this script is also used to benchmark FA performance with 2 GCDs. +Check the [2GCD benchmark script](https://github.com/ROCmSoftwarePlatform/triton/blob/triton-mlir/scripts/amd/benchmark_flash_attention.py) for more details. + +## `flash-attention.py` + +This script contains the Flash Attention kernel with the following support + +- Arbitrary Q and KV sequence lengths, and arbitrary head sizes +- Autoregressive or "causal" masking +- Flash Attention v2 with variable sequence lengths +- Multi and Grouped Query attention +- ALiBi bias +- Matrix bias + +These are currently supported for the forward kernel only. + +## `06-attention-decode.py` + +This contains the Flash Decoding kernel. + +## `hbm-bw-test.py` + +This is a script that measures HBM bandwidth performance on your device. + +## `03-matrix-multiplication-all-types.py` + +This script contains the GEMM kernel that supports int8, int32, fp16, +fp32, bf16 and f8 (both e5m2 and e4m3) datatypes. + +## `03-matrix-multiplication-stream-k.py` + +This script contains the GEMM kernel that implements [stream-k](https://arxiv.org/abs/2301.03598) + +## `multreduce_matmul_kernel.py` + +Kernel that implements GEMM with explicit multiply-reduce instructions for small block sizes. Such +small block sizes aren't natively supported by `tl.dot` operator. + +Despite being numerically correct, this kernel performed worse than a corresponding GEMM kernel that +used `tl.dot` with minimum block size equal to $16$. + +## `softmax.py` + +Kernel that implements Softmax over a row of tensor. + +## `rmsnorm.py` + +Kernel that implements RMS Norm over a row of tensor. + +## `layernorm.py` +Kernel that implements Layer Normalization over a row on tensor diff --git a/python/perf-kernels/flash-attention.py b/python/perf-kernels/flash-attention.py new file mode 100644 index 000000000000..988438340abe --- /dev/null +++ b/python/perf-kernels/flash-attention.py @@ -0,0 +1,1530 @@ +""" +Fused Attention +=============== + +This is a Triton implementation of the Flash Attention v2 algorithm +See https://tridao.me/publications/flash2/flash2.pdf + +Credits: +AMD Triton kernels team +OpenAI kernel team + +Currently only the forward kernel is supported, and contains these features: + +1) Fwd with causal masking +2) Arbitrary Q and KV sequence lengths +3) Arbitrary head sizes +4) Multi and grouped query attention +5) Variable sequence lengths +6) ALiBi and matrix bias + +""" + +import argparse +import pytest +import sys +import torch + +import triton +import triton.language as tl + + +class MetaData(): + cu_seqlens_q = None + cu_seqlens_k = None + max_seqlens_q = 0 + max_seqlens_k = 0 + bias = None + alibi_slopes = None + causal = False + num_contexts = 0 + varlen = False + layout = None + dropout_p, return_encoded_softmax = 0.0, False + + def __init__(self, sm_scale=1.0): + self.sm_scale = sm_scale + + def set_varlen_params(self, cu_seqlens_q, cu_seqlens_k): + self.varlen = True + self.layout = 'thd' + self.cu_seqlens_q = cu_seqlens_q + self.cu_seqlens_k = cu_seqlens_k + # Without "varlen", there should still be one sequence. + assert len(cu_seqlens_q) >= 2 + assert len(cu_seqlens_q) == len(cu_seqlens_k) + self.num_contexts = len(cu_seqlens_q) - 1 + for i in range(0, self.num_contexts): + self.max_seqlens_q = max(cu_seqlens_q[i + 1].item() - cu_seqlens_q[i].item(), self.max_seqlens_q) + self.max_seqlens_k = max(cu_seqlens_k[i + 1].item() - cu_seqlens_k[i].item(), self.max_seqlens_k) + + def need_bias(self, bias, batch, nheads, seqlen_q, seqlen_k): + assert bias.is_cuda + assert bias.dim() == 4 + assert bias.shape[0] == 1 + assert bias.shape[2:] == (seqlen_q, seqlen_k) + self.bias = bias + + def need_alibi(self, alibi_slopes, batch, nheads): + assert alibi_slopes.is_cuda + assert alibi_slopes.dim() == 2 + assert alibi_slopes.shape[0] == batch + assert alibi_slopes.shape[1] == nheads + self.alibi_slopes = alibi_slopes + + def need_causal(self): + self.causal = True + + def need_dropout(self, dropout_p, return_encoded_softmax): + self.dropout_p = dropout_p + self.return_encoded_softmax = return_encoded_softmax + + def check_args(self, q, k, v, o): + assert q.dim() == k.dim() and q.dim() == v.dim() + + batch, nheads_q, nheads_k, head_size = get_shape_from_layout(q, k, self) + if self.varlen: + assert q.dim() == 3 + assert self.cu_seqlens_q is not None + assert self.cu_seqlens_k is not None + assert len(self.cu_seqlens_q) == len(self.cu_seqlens_k) + # TODO: Remove once bias is supported with varlen + assert self.bias is None + # TODO:Remove once dropout is supported with varlen + assert self.dropout_p == 0.0 + assert not self.return_encoded_softmax + else: + assert q.dim() == 4 + assert self.max_seqlens_q > 0 and self.max_seqlens_k > 0 + assert self.cu_seqlens_q is None and self.cu_seqlens_k is None + assert k.shape == v.shape + assert q.shape[-1] == k.shape[-1] and q.shape[-1] == v.shape[-1] + # TODO: Change assert if we support qkl f8 and v f16 + assert q.dtype == k.dtype and q.dtype == v.dtype + assert head_size <= 256 + assert o.shape == q.shape + assert (nheads_q % nheads_k) == 0 + assert self.layout is not None + assert self.layout == 'thd' or not self.varlen + + +@triton.jit +def cdiv_fn(x, y): + return (x + y - 1) // y + + +@triton.jit +def max_fn(x, y): + return tl.math.max(x, y) + + +@triton.jit +def dropout_offsets(philox_seed, philox_offset, dropout_p, m, n, stride): + ms = tl.arange(0, m) + ns = tl.arange(0, n) + return philox_offset + ms[:, None] * stride + ns[None, :] + + +@triton.jit +def dropout_rng(philox_seed, philox_offset, dropout_p, m, n, stride): + rng_offsets = dropout_offsets(philox_seed, philox_offset, dropout_p, m, n, stride).to(tl.uint32) + # TODO: use tl.randint for better performance + return tl.rand(philox_seed, rng_offsets) + + +@triton.jit +def dropout_mask(philox_seed, philox_offset, dropout_p, m, n, stride): + rng_output = dropout_rng(philox_seed, philox_offset, dropout_p, m, n, stride) + rng_keep = rng_output > dropout_p + return rng_keep + + +# Convenience function to load with optional boundary checks. +# "First" is the major dim, "second" is the minor dim. +@triton.jit +def load_fn(ptrs, offset_first, offset_second, boundary_first, boundary_second): + if offset_first is not None and offset_second is not None: + mask = (offset_first[:, None] < boundary_first) & \ + (offset_second[None, :] < boundary_second) + tensor = tl.load(ptrs, mask=mask, other=0.0) + elif offset_first is not None: + mask = offset_first[:, None] < boundary_first + tensor = tl.load(ptrs, mask=mask, other=0.0) + elif offset_second is not None: + mask = offset_second[None, :] < boundary_second + tensor = tl.load(ptrs, mask=mask, other=0.0) + else: + tensor = tl.load(ptrs) + return tensor + + +@triton.jit +def print_gpu(prefix, val=None): + if (tl.program_id(0) == 0) and ((tl.program_id(1) == 0) and (tl.program_id(2) == 0)): + if val is not None: + tl.device_print(prefix, val) + else: + tl.device_print(prefix) + + +@triton.jit +def compute_alibi_block(alibi_slope, seqlen_q, seqlen_k, offs_m, offs_n, transpose=False): + # when seqlen_k and seqlen_q are different we want the diagonal to stick to the bottom right of the attention matrix + # for casual mask we want something like this where (1 is kept and 0 is masked) + # seqlen_q = 2 and seqlen_k = 5 + # 1 1 1 1 0 + # 1 1 1 1 1 + # seqlen_q = 5 and seqlen_k = 2 + # 0 0 + # 0 0 + # 0 0 + # 1 0 + # 1 1 + # for alibi the diagonal is 0 indicating no penalty for attending to that spot and increasing penalty for attending further from the diagonal + # e.g. alibi_slope = 1, seqlen_q = 2, seqlen_k = 5, offs_m = [0, 1, 2, 3], offs_n = [0, 1, 2, 3, 4], transpose = False + # 1. offs_m[:,None] = [[0], + # [1], + # 2. offs_m[:,None] + seqlen_k = [[5], + # [6], + # 3. offs_m[:,None] + seqlen_k - seqlen_q = [[3], + # [4], + # 4. offs_m[:,None] + seqlen_k - seqlen_q - offs_n[None,:] = [[3], - [[0, 1, 2, 3, 4]] = [[ 3, 2, 1, 0,-1], + # [4], [ 4, 3, 2, 1, 0]] + # 5. -1 * alibi_slope * tl.abs(relative_pos_block) = [[ -3, -2, -1, 0,-1], + # [ -4, -3, -2, -1, 0]], + relative_pos_block = offs_m[:, None] + seqlen_k - seqlen_q - offs_n[None, :] + alibi_block = -1 * alibi_slope * tl.abs(relative_pos_block) + if transpose: + return alibi_block.T + else: + return alibi_block + + +def compute_alibi_tensor(alibi_slopes, seqlen_q, seqlen_k): + q_idx = torch.arange(seqlen_q, dtype=torch.int32, device="cuda").unsqueeze(-1) # (N_CTX_Q, 1) + k_idx = torch.arange(seqlen_k, dtype=torch.int32, device="cuda").unsqueeze(0) # (1, N_CTX_K) + relative_pos = torch.abs(q_idx + seqlen_k - seqlen_q - k_idx) # (N_CTX_Q, N_CTX_K) + return -1 * alibi_slopes.unsqueeze(-1).unsqueeze(-1) * relative_pos # (Z, H, N_CTX_Q, N_CTX_K) + + +@triton.jit +def _attn_fwd_inner(acc, l_i, m_i, q, k_ptrs, v_ptrs, bias_ptrs, stride_kn, stride_vk, stride_bn, start_m, + actual_seqlen_k, actual_seqlen_q, dropout_p, philox_seed, batch_philox_offset, encoded_sm_ptrs, + block_min, block_max, offs_n_causal, masked_blocks, n_extra_tokens, alibi_slope, + IS_CAUSAL: tl.constexpr, BLOCK_M: tl.constexpr, BLOCK_DMODEL: tl.constexpr, BLOCK_N: tl.constexpr, + OFFS_M: tl.constexpr, OFFS_N: tl.constexpr, PRE_LOAD_V: tl.constexpr, MASK_STEPS: tl.constexpr, + ENABLE_DROPOUT: tl.constexpr, RETURN_ENCODED_SOFTMAX: tl.constexpr, PADDED_HEAD: tl.constexpr, + ACTUAL_BLOCK_DMODEL: tl.constexpr): + # loop over k, v, and update accumulator + for start_n in range(block_min, block_max, BLOCK_N): + # For padded blocks, we will overrun the tensor size if + # we load all BLOCK_N. For others, the blocks are all within range. + if MASK_STEPS: + k_offs_n = start_n + tl.arange(0, BLOCK_N) + else: + k_offs_n = None + k_offs_k = None if not PADDED_HEAD else tl.arange(0, BLOCK_DMODEL) + k = load_fn(k_ptrs, k_offs_k, k_offs_n, ACTUAL_BLOCK_DMODEL, actual_seqlen_k) + if PRE_LOAD_V: + # We can use the same offsets as k, just with dims transposed. + v = load_fn(v_ptrs, k_offs_n, k_offs_k, actual_seqlen_k, ACTUAL_BLOCK_DMODEL) + qk = tl.zeros([BLOCK_M, BLOCK_N], dtype=tl.float32) + # We start from end of seqlen_k so only the first iteration would need + # to be checked for padding if it is not a multiple of block_n + # TODO: This can be optimized to only be true for the padded block. + if MASK_STEPS: + # If this is the last block / iteration, we want to + # mask if the sequence length is not a multiple of block size + # a solution is to always do BLOCK_M // BLOCK_N + 1 steps if not is_modulo_mn. + # last step might get wasted but that is okay. check if this masking works For + # that case. + if (start_n + BLOCK_N == block_max) and (n_extra_tokens != 0): + boundary_m = tl.full([BLOCK_M], actual_seqlen_k, dtype=tl.int32) + size_n = start_n + OFFS_N[None, :] + mask = size_n < boundary_m[:, None] + qk = tl.where(mask, qk, float("-inf")) + if IS_CAUSAL: + causal_boundary = start_n + offs_n_causal + causal_mask = OFFS_M[:, None] >= causal_boundary[None, :] + qk = tl.where(causal_mask, qk, float("-inf")) + # -- compute qk ---- + qk += tl.dot(q, k) + if bias_ptrs is not None: + bias_offs_n = start_n + tl.arange(0, BLOCK_N) if MASK_STEPS else None + bias = load_fn(bias_ptrs, OFFS_M, bias_offs_n, actual_seqlen_q, actual_seqlen_k) + # While bias is added after multiplying qk with sm_scale, + # our optimization to use 2^x instead of e^x results in an additional + # scale factor of log2(e) which we must also multiply the bias with. + qk += (bias * 1.44269504089) + + if alibi_slope is not None: + # Compute the global position of each token within the sequence + global_m_positions = start_m * BLOCK_M + tl.arange(0, BLOCK_M) + global_n_positions = start_n + tl.arange(0, BLOCK_N) + alibi_block = compute_alibi_block(alibi_slope, actual_seqlen_q, actual_seqlen_k, global_m_positions, + global_n_positions) + qk += (alibi_block * 1.44269504089) # scale factor of log2(e) + + # softmax + m_ij = tl.maximum(m_i, tl.max(qk, 1)) + qk = qk - m_ij[:, None] + p = tl.math.exp2(qk) + + # CAVEAT: Must update l_ij before applying dropout + l_ij = tl.sum(p, 1) + if ENABLE_DROPOUT: + philox_offset = batch_philox_offset + start_m * BLOCK_M * actual_seqlen_k + start_n - BLOCK_N + keep = dropout_mask(philox_seed, philox_offset, dropout_p, BLOCK_M, BLOCK_N, actual_seqlen_k) + if RETURN_ENCODED_SOFTMAX: + tl.store(encoded_sm_ptrs, tl.where(keep, p, -p).to(encoded_sm_ptrs.type.element_ty)) + p = tl.where(keep, p, 0.0) + elif RETURN_ENCODED_SOFTMAX: + tl.store(encoded_sm_ptrs, p.to(encoded_sm_ptrs.type.element_ty)) + # -- update output accumulator -- + alpha = tl.math.exp2(m_i - m_ij) + acc = acc * alpha[:, None] + if not PRE_LOAD_V: + v = load_fn(v_ptrs, k_offs_n, k_offs_k, actual_seqlen_k, ACTUAL_BLOCK_DMODEL) + # -- update m_i and l_i + l_i = l_i * alpha + l_ij + # update m_i and l_i + m_i = m_ij + acc += tl.dot(p.to(v.type.element_ty), v) + k_ptrs += BLOCK_N * stride_kn + v_ptrs += BLOCK_N * stride_vk + if bias_ptrs is not None: + bias_ptrs += BLOCK_N * stride_bn + if RETURN_ENCODED_SOFTMAX: + encoded_sm_ptrs += BLOCK_N + return acc, l_i, m_i + + +@triton.autotune( + configs=[ + triton.Config({'BLOCK_M': 128, 'BLOCK_N': 128, 'waves_per_eu': 2, 'PRE_LOAD_V': False}, num_stages=1, + num_warps=4), + triton.Config({'BLOCK_M': 128, 'BLOCK_N': 64, 'waves_per_eu': 2, 'PRE_LOAD_V': False}, num_stages=1, + num_warps=4), + triton.Config({'BLOCK_M': 128, 'BLOCK_N': 64, 'waves_per_eu': 3, 'PRE_LOAD_V': False}, num_stages=1, + num_warps=4), + triton.Config({'BLOCK_M': 128, 'BLOCK_N': 64, 'waves_per_eu': 1, 'PRE_LOAD_V': False}, num_stages=1, + num_warps=4), + triton.Config({'BLOCK_M': 128, 'BLOCK_N': 32, 'waves_per_eu': 2, 'PRE_LOAD_V': False}, num_stages=1, + num_warps=4), + # Fall-back config. + triton.Config({'BLOCK_M': 16, 'BLOCK_N': 16, 'waves_per_eu': 1, 'PRE_LOAD_V': False}, num_stages=1, + num_warps=4), + ], + key=['IS_CAUSAL', 'dropout_p', 'MAX_SEQLENS_Q', 'MAX_SEQLENS_K', 'ACTUAL_BLOCK_DMODEL', 'VARLEN', 'HQ', 'HK'], + use_cuda_graph=True, +) +@triton.jit +def attn_fwd(Q, K, V, bias, SM_SCALE: tl.constexpr, L, Out, stride_qz, stride_qh, stride_qm, stride_qk, stride_kz, + stride_kh, stride_kn, stride_kk, stride_vz, stride_vh, stride_vk, stride_vn, stride_oz, stride_oh, + stride_om, stride_on, stride_bz, stride_bh, stride_bm, stride_bn, stride_az, stride_ah, cu_seqlens_q, + cu_seqlens_k, dropout_p, philox_seed, philox_offset_base, encoded_softmax, alibi_slopes, HQ: tl.constexpr, + HK: tl.constexpr, ACTUAL_BLOCK_DMODEL: tl.constexpr, MAX_SEQLENS_Q: tl.constexpr, + MAX_SEQLENS_K: tl.constexpr, VARLEN: tl.constexpr, IS_CAUSAL: tl.constexpr, BLOCK_M: tl.constexpr, + BLOCK_DMODEL: tl.constexpr, BLOCK_N: tl.constexpr, PRE_LOAD_V: tl.constexpr, USE_BIAS: tl.constexpr, + ENABLE_DROPOUT: tl.constexpr, RETURN_ENCODED_SOFTMAX: tl.constexpr, USE_ALIBI: tl.constexpr): + start_m = tl.program_id(0) + off_h_q = tl.program_id(1) + off_z = tl.program_id(2) + offs_m = start_m * BLOCK_M + tl.arange(0, BLOCK_M) + offs_n = tl.arange(0, BLOCK_N) + offs_d = tl.arange(0, BLOCK_DMODEL) + if VARLEN: + cu_seqlens_q_start = tl.load(cu_seqlens_q + off_z) + cu_seqlens_q_end = tl.load(cu_seqlens_q + off_z + 1) + seqlen_q = cu_seqlens_q_end - cu_seqlens_q_start + # We have a one-size-fits-all grid in id(0). Some seqlens might be too + # small for all start_m so for those we return early. + if start_m * BLOCK_M > seqlen_q: + return + cu_seqlens_k_start = tl.load(cu_seqlens_k + off_z) + cu_seqlens_k_end = tl.load(cu_seqlens_k + off_z + 1) + seqlen_k = cu_seqlens_k_end - cu_seqlens_k_start + else: + cu_seqlens_q_start = 0 + cu_seqlens_k_start = 0 + seqlen_q = MAX_SEQLENS_Q + seqlen_k = MAX_SEQLENS_K + + # Now we compute whether we need to exit early due to causal masking. + # This is because for seqlen_q > seqlen_k, M rows of the attn scores + # are completely masked, resulting in 0s written to the output, and + # inf written to LSE. We don't need to do any GEMMs in this case. + # This block of code determines what N is, and if this WG is operating + # on those M rows. + n_blocks = cdiv_fn(seqlen_k, BLOCK_N) + if (IS_CAUSAL): + # If seqlen_q == seqlen_k, the attn scores are a square matrix. + # If seqlen_q != seqlen_k, attn scores are rectangular which means + # the causal mask boundary is bottom right aligned, and ends at either + # the top edge (seqlen_q < seqlen_k) or left edge. + # This captures the decrease in n_blocks if we have a rectangular attn matrix + n_blocks_seqlen = cdiv_fn((start_m + 1) * BLOCK_M + seqlen_k - seqlen_q, BLOCK_N) + # This is what adjusts the block_max for the current WG, only + # if IS_CAUSAL. Otherwise we want to always iterate through all n_blocks + n_blocks = min(n_blocks, n_blocks_seqlen) + # If we have no blocks after adjusting for seqlen deltas, this WG is part of + # the blocks that are all 0. We exit early. + if n_blocks <= 0: + o_offset = Out + off_z * stride_oz + off_h_q * stride_oh + cu_seqlens_q_start * stride_om + o_ptrs = o_offset + offs_m[:, None] * stride_om + offs_d[None, :] * stride_on + acc = tl.zeros([BLOCK_M, BLOCK_DMODEL], dtype=Out.type.element_ty) + o_ptrs_mask = offs_m[:, None] < seqlen_q + # We still need to write 0s to the result + tl.store(o_ptrs, acc, mask=o_ptrs_mask) + # The tensor allocated for L is based on MAX_SEQLENS_Q as that is + # statically known. + l_ptrs = L + off_z * HQ * MAX_SEQLENS_Q + off_h_q * MAX_SEQLENS_Q + offs_m + # We store inf to LSE, not -inf because in the bwd pass, we subtract this + # from qk which makes it -inf, such that exp(qk - inf) = 0 for these masked blocks. + l = tl.full([BLOCK_M], value=float("inf"), dtype=tl.float32) + l_ptrs_mask = offs_m < MAX_SEQLENS_Q + tl.store(l_ptrs, l, mask=l_ptrs_mask) + # TODO: Should dropout and return encoded softmax be handled here too? + return + + # If MQA / GQA, set the K and V head offsets appropriately. + GROUP_SIZE: tl.constexpr = HQ // HK + if GROUP_SIZE != 1: + off_h_k = off_h_q // GROUP_SIZE + else: + off_h_k = off_h_q + + n_extra_tokens = 0 + if seqlen_k < BLOCK_N: + n_extra_tokens = BLOCK_N - seqlen_k + elif seqlen_k % BLOCK_N: + n_extra_tokens = seqlen_k % BLOCK_N + PADDED_HEAD: tl.constexpr = (ACTUAL_BLOCK_DMODEL != BLOCK_DMODEL) + + # Compute pointers for all the tensors used in this kernel. + q_offset = Q + off_z * stride_qz + off_h_q * stride_qh + cu_seqlens_q_start * stride_qm + q_ptrs = q_offset + offs_m[:, None] * stride_qm + offs_d[None, :] * stride_qk + k_offset = K + off_z * stride_kz + off_h_k * stride_kh + cu_seqlens_k_start * stride_kn + k_ptrs = k_offset + offs_d[:, None] * stride_kk + offs_n[None, :] * stride_kn + v_offset = V + off_z * stride_vz + off_h_k * stride_vh + cu_seqlens_k_start * stride_vk + v_ptrs = v_offset + offs_n[:, None] * stride_vk + offs_d[None, :] * stride_vn + if USE_BIAS: + # Note: this might get large enough to overflow on some configs + bias_offset = off_h_q * stride_bh + bias_ptrs = bias + bias_offset + offs_m[:, None] * stride_bm + offs_n[None, :] * stride_bn + else: + bias_ptrs = None + + if USE_ALIBI: + a_offset = off_z * stride_az + off_h_q * stride_ah + alibi_slope = tl.load(alibi_slopes + a_offset) + else: + alibi_slope = None + + if ENABLE_DROPOUT: + off_hz = off_z * HQ + off_h_q + batch_philox_offset = philox_offset_base + off_hz * seqlen_q * seqlen_k + else: + batch_philox_offset = 0 + # We can ask to return the dropout mask without actually doing any dropout. In + # this case, we return an invalid pointer so indicate the mask is not valid. + if RETURN_ENCODED_SOFTMAX: + encoded_sm_base = encoded_softmax + off_h_q * seqlen_q * seqlen_k + encoded_sm_ptrs = encoded_sm_base + offs_m[:, None] * seqlen_k + offs_n[None, :] + else: + encoded_sm_ptrs = None + # initialize pointer to m and l + m_i = tl.full([BLOCK_M], float("-inf"), dtype=tl.float32) + l_i = tl.full([BLOCK_M], 1.0, dtype=tl.float32) + acc = tl.zeros([BLOCK_M, BLOCK_DMODEL], dtype=tl.float32) + # scale sm_scale by log_2(e) and use 2^x in the loop as we do not + # have native e^x support in HW. + QK_SCALE: tl.constexpr = SM_SCALE * 1.44269504089 + # Q is loaded once at the beginning and shared by all N blocks. + q_ptrs_mask = offs_m[:, None] < seqlen_q + if PADDED_HEAD: + q_ptrs_mask = q_ptrs_mask & (offs_d[None, :] < ACTUAL_BLOCK_DMODEL) + q = tl.load(q_ptrs, mask=q_ptrs_mask, other=0.0) + q = (q * QK_SCALE).to(q.type.element_ty) + + # Here we compute how many full and masked blocks we have. + padded_block_k = n_extra_tokens != 0 + is_modulo_mn = not padded_block_k and (seqlen_q % BLOCK_M == 0) + if IS_CAUSAL: + # There are always at least BLOCK_M // BLOCK_N masked blocks. + # Additionally there might be one more due to dissimilar seqlens. + masked_blocks = BLOCK_M // BLOCK_N + (not is_modulo_mn) + else: + # Padding on Q does not need to be masked in the FA loop. + masked_blocks = padded_block_k + # if IS_CAUSAL, not is_modulo_mn does not always result in an additional block. + # In this case we might exceed n_blocks so pick the min. + masked_blocks = min(masked_blocks, n_blocks) + n_full_blocks = n_blocks - masked_blocks + block_min = 0 + block_max = n_blocks * BLOCK_N + # Compute for full blocks. Here we set causal to false regardless of its actual + # value because there is no masking. Similarly we do not need padding. + if n_full_blocks > 0: + block_max = (n_blocks - masked_blocks) * BLOCK_N + acc, l_i, m_i = _attn_fwd_inner(acc, l_i, m_i, q, k_ptrs, v_ptrs, bias_ptrs, stride_kn, stride_vk, stride_bn, + start_m, seqlen_k, seqlen_q, dropout_p, philox_seed, batch_philox_offset, + encoded_sm_ptrs, + # _, _, offs_n_causal, masked_blocks, n_extra_tokens, _ + block_min, block_max, 0, 0, 0, alibi_slope, + # IS_CAUSAL, .... + False, BLOCK_M, BLOCK_DMODEL, BLOCK_N, offs_m, offs_n, + # _, MASK_STEPS, ... + PRE_LOAD_V, False, ENABLE_DROPOUT, RETURN_ENCODED_SOFTMAX, PADDED_HEAD, + ACTUAL_BLOCK_DMODEL) + block_min = block_max + block_max = n_blocks * BLOCK_N + + tl.debug_barrier() + # Remaining blocks, if any, are full / not masked. + if (masked_blocks > 0): + if IS_CAUSAL: + offs_n_causal = offs_n + (seqlen_q - seqlen_k) + else: + offs_n_causal = 0 + k_ptrs += n_full_blocks * BLOCK_N * stride_kn + v_ptrs += n_full_blocks * BLOCK_N * stride_vk + if USE_BIAS: + bias_ptrs += n_full_blocks * BLOCK_N * stride_bn + if RETURN_ENCODED_SOFTMAX: + encoded_sm_ptrs += n_full_blocks * BLOCK_N + acc, l_i, m_i = _attn_fwd_inner(acc, l_i, m_i, q, k_ptrs, v_ptrs, bias_ptrs, stride_kn, stride_vk, stride_bn, + start_m, seqlen_k, seqlen_q, dropout_p, philox_seed, batch_philox_offset, + encoded_sm_ptrs, block_min, block_max, offs_n_causal, masked_blocks, + n_extra_tokens, alibi_slope, IS_CAUSAL, BLOCK_M, BLOCK_DMODEL, BLOCK_N, offs_m, + offs_n, + # _, MASK_STEPS, ... + PRE_LOAD_V, True, ENABLE_DROPOUT, RETURN_ENCODED_SOFTMAX, PADDED_HEAD, + ACTUAL_BLOCK_DMODEL) + # epilogue + # This helps the compiler do Newton Raphson on l_i vs on acc which is much larger. + l_recip = 1 / l_i[:, None] + acc = acc * l_recip + + if ENABLE_DROPOUT: + acc = acc / (1 - dropout_p) + # If seqlen_q > seqlen_k but the delta is not a multiple of BLOCK_M, + # then we have one block with a row of all NaNs which come from computing + # softmax over a row of all -infs (-inf - inf = NaN). We check for that here + # and store 0s where there are NaNs as these rows should've been zeroed out. + end_m_idx = (start_m + 1) * BLOCK_M + start_m_idx = start_m * BLOCK_M + causal_start_idx = seqlen_q - seqlen_k + acc = acc.to(Out.type.element_ty) + if IS_CAUSAL: + if causal_start_idx > start_m_idx and causal_start_idx < end_m_idx: + out_mask_boundary = tl.full((BLOCK_DMODEL, ), causal_start_idx, dtype=tl.int32) + mask_m_offsets = start_m_idx + tl.arange(0, BLOCK_M) + out_ptrs_mask = mask_m_offsets[:, None] >= out_mask_boundary[None, :] + z = 0.0 + acc = tl.where(out_ptrs_mask, acc, z.to(acc.type.element_ty)) + # write back LSE + l_ptrs = L + off_z * HQ * MAX_SEQLENS_Q + off_h_q * MAX_SEQLENS_Q + offs_m + # If seqlen_q not multiple of BLOCK_M, we need to mask out the last few rows. + # This is only true for the last M block. For others, overflow_size will be -ve + overflow_size = end_m_idx - seqlen_q + if overflow_size > 0: + boundary = tl.full((BLOCK_M, ), BLOCK_M - overflow_size, dtype=tl.int32) + l_ptrs_mask = tl.arange(0, BLOCK_M) < boundary + tl.store(l_ptrs, m_i + tl.math.log2(l_i), mask=l_ptrs_mask) + else: + tl.store(l_ptrs, m_i + tl.math.log2(l_i)) + + # write back O + o_offset = Out + off_z * stride_oz + off_h_q * stride_oh + cu_seqlens_q_start * stride_om + o_ptrs = o_offset + offs_m[:, None] * stride_om + offs_d[None, :] * stride_on + o_ptrs_mask = tl.full([BLOCK_M, BLOCK_DMODEL], 1, dtype=tl.int1) + if overflow_size > 0: + o_ptrs_mask = o_ptrs_mask & (offs_m[:, None] < seqlen_q) + if PADDED_HEAD: + o_ptrs_mask = o_ptrs_mask & (offs_d[None, :] < ACTUAL_BLOCK_DMODEL) + tl.store(o_ptrs, acc.to(Out.dtype.element_ty), mask=o_ptrs_mask) + + +@triton.jit +def _attn_bwd_preprocess( + Out, + DO, + Delta, + stride_oz, + stride_oh, + stride_om, + stride_on, + stride_doz, + stride_doh, + stride_dom, + stride_don, + seqlen_q, + head_dim, + BLOCK_M: tl.constexpr, + D_HEAD: tl.constexpr, +): + # off_m = tl.program_id(0) * BLOCK_M + tl.arange(0, BLOCK_M) + # off_n = tl.arange(0, D_HEAD) + off_m = tl.program_id(0) * BLOCK_M + off_h = tl.program_id(1) # head index + off_z = tl.program_id(2) # batch index + num_h = tl.num_programs(1) + o_offset = off_h * stride_oh + off_z * stride_oz + O_block_ptr = tl.make_block_ptr(base=Out + o_offset, shape=(seqlen_q, head_dim), strides=(stride_om, stride_on), + offsets=(off_m, 0), block_shape=(BLOCK_M, D_HEAD), order=(1, 0)) + do_offset = off_h * stride_doh + off_z * stride_doz + DO_block_ptr = tl.make_block_ptr(base=DO + do_offset, shape=(seqlen_q, head_dim), strides=(stride_dom, stride_don), + offsets=(off_m, 0), block_shape=(BLOCK_M, D_HEAD), order=(1, 0)) + # load + # o = tl.load(Out + off_m[:, None] * D_HEAD + off_n[None, :]).to(tl.float32) + # do = tl.load(DO + off_m[:, None] * D_HEAD + off_n[None, :]).to(tl.float32) + o = tl.load(O_block_ptr, boundary_check=(0, 1), padding_option="zero").to(tl.float32) + do = tl.load(DO_block_ptr, boundary_check=(0, 1), padding_option="zero").to(tl.float32) + # compute + delta = tl.sum(o * do, axis=1) + # write-back, shape (q.shape[0] * q.shape[1], q.shape[2]) + off_zh = off_z * num_h + off_h * 1 + # Check for OOB accesses + delta_ptrs = Delta + off_zh * seqlen_q + off_m + tl.arange(0, BLOCK_M) + overflow = off_m + BLOCK_M - seqlen_q + if overflow > 0: + boundary = tl.full((BLOCK_M, ), BLOCK_M - overflow, dtype=tl.int32) + mask = boundary > tl.arange(0, BLOCK_M) + tl.store(delta_ptrs, delta, mask=mask) + else: + tl.store(delta_ptrs, delta) + + +@triton.jit +def _bwd_kernel_dk_dv(dk, dv, Q, k, v, sm_scale, alibi_slope, DO, M, D, + # shared by Q/K/V/DO. + stride_tok, stride_d, H, N_CTX, BLOCK_M1: tl.constexpr, BLOCK_N1: tl.constexpr, + BLOCK_DMODEL: tl.constexpr, + # Filled in by the wrapper. + start_n, start_m, num_steps, MASK: tl.constexpr): + offs_m = start_m + tl.arange(0, BLOCK_M1) + offs_n = start_n + tl.arange(0, BLOCK_N1) + # offs_k = tl.arange(0, BLOCK_DMODEL) + QT_block_ptr = tl.make_block_ptr(base=Q, shape=(BLOCK_DMODEL, N_CTX), strides=(stride_d, stride_tok), + offsets=(0, start_m), block_shape=(BLOCK_DMODEL, BLOCK_M1), order=(0, 1)) + DO_block_ptr = tl.make_block_ptr(base=DO, shape=(N_CTX, BLOCK_DMODEL), strides=(stride_tok, stride_d), + offsets=(start_m, 0), block_shape=(BLOCK_M1, BLOCK_DMODEL), order=(1, 0)) + # BLOCK_N1 must be a multiple of BLOCK_M1, otherwise the code wouldn't work. + tl.static_assert(BLOCK_N1 % BLOCK_M1 == 0) + curr_m = start_m + step_m = BLOCK_M1 + for blk_idx in range(num_steps): + qT = tl.load(QT_block_ptr) + # Load m before computing qk to reduce pipeline stall. + offs_m = curr_m + tl.arange(0, BLOCK_M1) + m = tl.load(M + offs_m) + kqT = tl.dot(k, qT) + if alibi_slope is not None: + alibi_block = compute_alibi_block(alibi_slope, N_CTX, N_CTX, offs_m, offs_n, True) + kqT += alibi_block * 1.44269504089 + + pT = tl.math.exp2(kqT - m[None, :]) + # Autoregressive masking. + if MASK: + mask = (offs_m[None, :] >= offs_n[:, None]) + pT = tl.where(mask, pT, 0.0) + do = tl.load(DO_block_ptr) + # Compute dV. + ppT = pT + ppT = ppT.to(tl.float16) + dv += tl.dot(ppT, do) + # D (= delta) is pre-divided by ds_scale. + Di = tl.load(D + offs_m) + # Compute dP and dS. + dpT = tl.dot(v, tl.trans(do)) + dsT = pT * (dpT - Di[None, :]) + dsT = dsT.to(tl.float16) + dk += tl.dot(dsT, tl.trans(qT)) + # Increment pointers. + curr_m += step_m + QT_block_ptr = tl.advance(QT_block_ptr, (0, step_m)) + DO_block_ptr = tl.advance(DO_block_ptr, (step_m, 0)) + return dk, dv + + +@triton.jit +def _bwd_kernel_dq(dq, q, K, V, do, m, D, alibi_slope, + # shared by Q/K/V/DO. + stride_tok, stride_d, H, N_CTX, BLOCK_M2: tl.constexpr, BLOCK_N2: tl.constexpr, + BLOCK_DMODEL: tl.constexpr, + # Filled in by the wrapper. + start_m, start_n, num_steps, MASK: tl.constexpr): + offs_m = start_m + tl.arange(0, BLOCK_M2) + offs_n = start_n + tl.arange(0, BLOCK_N2) + # offs_k = tl.arange(0, BLOCK_DMODEL) + KT_block_ptr = tl.make_block_ptr(base=K, shape=(BLOCK_DMODEL, N_CTX), strides=(stride_d, stride_tok), + offsets=(0, start_n), block_shape=(BLOCK_DMODEL, BLOCK_N2), order=(0, 1)) + VT_block_ptr = tl.make_block_ptr(base=V, shape=(BLOCK_DMODEL, N_CTX), strides=(stride_d, stride_tok), + offsets=(0, start_n), block_shape=(BLOCK_DMODEL, BLOCK_N2), order=(0, 1)) + # D (= delta) is pre-divided by ds_scale. + Di = tl.load(D + offs_m) + # BLOCK_M2 must be a multiple of BLOCK_N2, otherwise the code wouldn't work. + tl.static_assert(BLOCK_M2 % BLOCK_N2 == 0) + curr_n = start_n + step_n = BLOCK_N2 + for blk_idx in range(num_steps): + kT = tl.load(KT_block_ptr) + qk = tl.dot(q, kT) + if alibi_slope is not None: + alibi_block = compute_alibi_block(alibi_slope, N_CTX, N_CTX, offs_m, offs_n) + qk += alibi_block * 1.44269504089 + + p = tl.math.exp2(qk - m) + # Autoregressive masking. + if MASK: + offs_n = curr_n + tl.arange(0, BLOCK_N2) + mask = (offs_m[:, None] >= offs_n[None, :]) + p = tl.where(mask, p, 0.0) + # Compute dP and dS. + vT = tl.load(VT_block_ptr) + dp = tl.dot(do, vT).to(tl.float32) + ds = p * (dp - Di[:, None]) + ds = ds.to(tl.float16) + # Compute dQ.0. + # NOTE: We need to de-scale dq in the end, because kT was pre-scaled. + dq += tl.dot(ds, tl.trans(kT)) + # Increment pointers. + curr_n += step_n + KT_block_ptr = tl.advance(KT_block_ptr, (0, step_n)) + VT_block_ptr = tl.advance(VT_block_ptr, (0, step_n)) + return dq + + +@triton.jit +def _attn_bwd(Q, K, V, sm_scale, alibi_slopes, DO, DQ, DK, DV, M, D, + # shared by Q/K/V/DO. + stride_z, stride_h, stride_tok, stride_d, + # H = 16, N_CTX = 1024 + H, N_CTX, BLOCK_DMODEL: tl.constexpr, BLOCK_M1: tl.constexpr, BLOCK_N1: tl.constexpr, + BLOCK_M2: tl.constexpr, BLOCK_N2: tl.constexpr, BLK_SLICE_FACTOR: tl.constexpr, USE_ALIBI: tl.constexpr): + LN2: tl.constexpr = 0.6931471824645996 # = ln(2) + + bhid = tl.program_id(2) + off_chz = (bhid * N_CTX).to(tl.int64) + adj = (stride_h * (bhid % H) + stride_z * (bhid // H)).to(tl.int64) + pid = tl.program_id(0) + + # offset pointers for batch/head + Q += adj + K += adj + V += adj + DO += adj + DQ += adj + DK += adj + DV += adj + M += off_chz + D += off_chz + + # offs_k = tl.arange(0, BLOCK_DMODEL) + + start_n = pid * BLOCK_N1 + # This assignment is important. It is what allows us to pick the diagonal + # blocks. Later, when we want to do the lower triangular, we update start_m + # after the first dkdv call. + start_m = start_n + + MASK_BLOCK_M1: tl.constexpr = BLOCK_M1 // BLK_SLICE_FACTOR + # offs_n = start_n + tl.arange(0, BLOCK_N1) + + dv = tl.zeros([BLOCK_N1, BLOCK_DMODEL], dtype=tl.float32) + dk = tl.zeros([BLOCK_N1, BLOCK_DMODEL], dtype=tl.float32) + + K_block_ptr = tl.make_block_ptr( + base=K, + shape=(N_CTX, BLOCK_DMODEL), + strides=(stride_tok, stride_d), + offsets=(start_n, 0), + block_shape=(BLOCK_N1, BLOCK_DMODEL), + order=(1, 0), + ) + V_block_ptr = tl.make_block_ptr( + base=V, + shape=(N_CTX, BLOCK_DMODEL), + strides=(stride_tok, stride_d), + offsets=(start_n, 0), + block_shape=(BLOCK_N1, BLOCK_DMODEL), + order=(1, 0), + ) + + # load K and V: they stay in SRAM throughout the inner loop for dkdv. + k = tl.load(K_block_ptr) + v = tl.load(V_block_ptr) + + if USE_ALIBI: + a_offset = bhid + alibi_slope = tl.load(alibi_slopes + a_offset) + else: + alibi_slope = None + + # compute dK and dV for blocks close to the diagonal that need to be masked + num_steps = BLOCK_N1 // MASK_BLOCK_M1 + dk, dv = _bwd_kernel_dk_dv(dk, dv, Q, k, v, sm_scale, alibi_slope, DO, M, D, stride_tok, stride_d, H, N_CTX, + MASK_BLOCK_M1, BLOCK_N1, BLOCK_DMODEL, start_n, start_m, num_steps, MASK=True) + + # compute dK and dV for blocks that don't need masking further from the diagonal + start_m += num_steps * MASK_BLOCK_M1 + num_steps = (N_CTX - start_m) // BLOCK_M1 + + dk, dv = _bwd_kernel_dk_dv(dk, dv, Q, k, v, sm_scale, alibi_slope, DO, M, D, stride_tok, stride_d, H, N_CTX, + BLOCK_M1, BLOCK_N1, BLOCK_DMODEL, start_n, start_m, num_steps, MASK=False) + + DV_block_ptrs = tl.make_block_ptr(base=DV, shape=(N_CTX, BLOCK_DMODEL), strides=(stride_tok, stride_d), + offsets=(start_n, 0), block_shape=(BLOCK_N1, BLOCK_DMODEL), order=(1, 0)) + tl.store(DV_block_ptrs, dv.to(v.dtype)) + + # Write back dK. + dk *= sm_scale + DK_block_ptrs = tl.make_block_ptr(base=DK, shape=(N_CTX, BLOCK_DMODEL), strides=(stride_tok, stride_d), + offsets=(start_n, 0), block_shape=(BLOCK_N1, BLOCK_DMODEL), order=(1, 0)) + tl.store(DK_block_ptrs, dk.to(k.dtype)) + + # THIS BLOCK DOES DQ: + start_m = pid * BLOCK_M2 + end_n = start_m + BLOCK_M2 + + MASK_BLOCK_N2: tl.constexpr = BLOCK_N2 // BLK_SLICE_FACTOR + offs_m = start_m + tl.arange(0, BLOCK_M2) + + Q_block_ptr = tl.make_block_ptr(base=Q, shape=(N_CTX, BLOCK_DMODEL), strides=(stride_tok, stride_d), + offsets=(start_m, 0), block_shape=(BLOCK_M2, BLOCK_DMODEL), order=(1, 0)) + + DO_block_ptr = tl.make_block_ptr(base=DO, shape=(N_CTX, BLOCK_DMODEL), strides=(stride_tok, stride_d), + offsets=(start_m, 0), block_shape=(BLOCK_M2, BLOCK_DMODEL), order=(1, 0)) + q = tl.load(Q_block_ptr) + do = tl.load(DO_block_ptr) + dq = tl.zeros([BLOCK_M2, BLOCK_DMODEL], dtype=tl.float32) + + m = tl.load(M + offs_m) + m = m[:, None] + + # Compute dQ for masked (diagonal) blocks. + # NOTE: This code scans each row of QK^T backward (from right to left, + # but inside each call to _attn_bwd_dq, from left to right), but that's + # not due to anything important. I just wanted to reuse the loop + # structure for dK & dV above as much as possible. + num_steps = BLOCK_M2 // MASK_BLOCK_N2 + dq = _bwd_kernel_dq(dq, q, K, V, do, m, D, alibi_slope, stride_tok, stride_d, H, N_CTX, BLOCK_M2, MASK_BLOCK_N2, + BLOCK_DMODEL, start_m, end_n - num_steps * MASK_BLOCK_N2, num_steps, MASK=True) + end_n -= num_steps * MASK_BLOCK_N2 + # stage 2 + num_steps = end_n // BLOCK_N2 + dq = _bwd_kernel_dq(dq, q, K, V, do, m, D, alibi_slope, stride_tok, stride_d, H, N_CTX, BLOCK_M2, BLOCK_N2, + BLOCK_DMODEL, start_m, end_n - num_steps * BLOCK_N2, num_steps, MASK=False) + # Write back dQ. + DQ_block_ptr = tl.make_block_ptr(base=DQ, shape=(N_CTX, BLOCK_DMODEL), strides=(stride_tok, stride_d), + offsets=(start_m, 0), block_shape=(BLOCK_M2, BLOCK_DMODEL), order=(1, 0)) + dq *= LN2 + tl.store(DQ_block_ptr, dq.to(q.dtype)) + + +empty = torch.empty(128, device="cuda") + + +def get_shape_from_layout(q, k, metadata): + if metadata.layout == 'thd': + nheads_q, nheads_k = q.shape[1], k.shape[1] + head_size = q.shape[-1] + batch = metadata.num_contexts + elif metadata.layout == 'bhsd': + batch, nheads_q, _, head_size = q.shape + nheads_k = k.shape[1] + elif metadata.layout == 'bshd': + batch, _, nheads_q, head_size = q.shape + nheads_k = k.shape[2] + else: + assert False, "Got unsupported layout." + return batch, nheads_q, nheads_k, head_size + + +# TODO: This can probably optimized to have fewer lines of code. +def get_strides_from_layout(q, k, v, o, metadata): + if metadata.layout == 'thd': + q_strides = (0, q.stride(1), q.stride(0), q.stride(2)) + k_strides = (0, k.stride(1), k.stride(0), k.stride(2)) + v_strides = (0, v.stride(1), v.stride(0), v.stride(2)) + o_strides = (0, o.stride(1), o.stride(0), o.stride(2)) + elif metadata.layout == 'bhsd': + q_strides = (q.stride(0), q.stride(1), q.stride(2), q.stride(3)) + k_strides = (k.stride(0), k.stride(1), k.stride(2), k.stride(3)) + v_strides = (v.stride(0), v.stride(1), v.stride(2), v.stride(3)) + o_strides = (o.stride(0), o.stride(1), o.stride(2), o.stride(3)) + elif metadata.layout == 'bshd': + q_strides = (q.stride(0), q.stride(2), q.stride(1), q.stride(3)) + k_strides = (k.stride(0), k.stride(2), k.stride(1), k.stride(3)) + v_strides = (v.stride(0), v.stride(2), v.stride(1), v.stride(3)) + o_strides = (o.stride(0), o.stride(2), o.stride(1), o.stride(3)) + else: + assert False, 'Got unsupported layout.' + return q_strides, k_strides, v_strides, o_strides + + +class _attention(torch.autograd.Function): + + @staticmethod + def forward(ctx, q, k, v, o, metadata): + # NOTE: a large bias tensor leads to overflow during pointer arithmetic + if (metadata.bias is not None): + assert (metadata.bias.numel() < 2**31) + + if o is None: + o = torch.empty_like(q, dtype=v.dtype) + metadata.check_args(q, k, v, o) + + batch, nheads_q, nheads_k, head_size = get_shape_from_layout(q, k, metadata) + q_strides, k_strides, v_strides, o_strides = get_strides_from_layout(q, k, v, o, metadata) + + # Get closest power of 2 over or equal to 32. + padded_d_model = 1 << (head_size - 1).bit_length() + # Smallest head_dim supported is 16. If smaller, the tile in the + # kernel is padded - there is no padding in memory for any dims. + padded_d_model = max(padded_d_model, 16) + + grid = lambda META: (triton.cdiv(metadata.max_seqlens_q, META['BLOCK_M']), nheads_q, batch) + + # encoded_softmax is used to validate dropout behavior vs the PyTorch SDPA math backend reference. We zero this out + # to give a consistent starting point and then populate it with the output of softmax with the sign bit set according + # to the dropout mask. The resulting return allows this mask to be fed into the reference implementation for testing + # only. This return holds no useful output aside from debugging. + if metadata.return_encoded_softmax: + encoded_softmax = torch.zeros((q.shape[0], q.shape[1], q.shape[2], k.shape[2]), device=q.device, + dtype=torch.float32) + else: + encoded_softmax = None + + M = torch.empty((batch, nheads_q, metadata.max_seqlens_q), device=q.device, dtype=torch.float32) + + # Seed the RNG so we get reproducible results for testing. + philox_seed = 0x1BF52 + philox_offset = 0x1D4B42 + + if metadata.bias is not None: + bias_strides = (metadata.bias.stride(0), metadata.bias.stride(1), metadata.bias.stride(2), + metadata.bias.stride(3)) + else: + bias_strides = (0, 0, 0, 0) + + if metadata.alibi_slopes is not None: + alibi_strides = (metadata.alibi_slopes.stride(0), metadata.alibi_slopes.stride(1)) + else: + alibi_strides = (0, 0) + + attn_fwd[grid](q, k, v, metadata.bias, metadata.sm_scale, M, o, *q_strides, *k_strides, *v_strides, *o_strides, + *bias_strides, *alibi_strides, metadata.cu_seqlens_q, metadata.cu_seqlens_k, + dropout_p=metadata.dropout_p, philox_seed=philox_seed, philox_offset_base=philox_offset, + encoded_softmax=encoded_softmax, alibi_slopes=metadata.alibi_slopes, HQ=nheads_q, HK=nheads_k, + ACTUAL_BLOCK_DMODEL=head_size, MAX_SEQLENS_Q=metadata.max_seqlens_q, + MAX_SEQLENS_K=metadata.max_seqlens_k, IS_CAUSAL=metadata.causal, VARLEN=metadata.varlen, + BLOCK_DMODEL=padded_d_model, USE_BIAS=False if metadata.bias is None else True, + USE_ALIBI=False if metadata.alibi_slopes is None else True, ENABLE_DROPOUT=metadata.dropout_p + > 0.0, RETURN_ENCODED_SOFTMAX=metadata.return_encoded_softmax) + + ctx.save_for_backward(q, k, v, o, M) + ctx.grid = grid + ctx.sm_scale = metadata.sm_scale + ctx.BLOCK_DMODEL = head_size + ctx.causal = metadata.causal + ctx.alibi_slopes = metadata.alibi_slopes + ctx.dropout_p = metadata.dropout_p + ctx.philox_seed = philox_seed + ctx.philox_offset = philox_offset + ctx.encoded_softmax = encoded_softmax + ctx.return_encoded_softmax = metadata.return_encoded_softmax + return o, encoded_softmax + + @staticmethod + def backward(ctx, do, _): + if torch.version.hip is not None: + BLOCK = 64 + else: + BLOCK = 128 + q, k, v, o, M = ctx.saved_tensors + assert do.is_contiguous() + assert q.stride() == k.stride() == v.stride() == o.stride() == do.stride() + seqlen_q = q.shape[2] + dq = torch.empty_like(q) + dk = torch.empty_like(k) + dv = torch.empty_like(v) + BATCH, N_HEAD, N_CTX = q.shape[:3] + PRE_BLOCK = 128 + # NUM_WARPS, NUM_STAGES = 4, 1 + BLOCK_M1, BLOCK_N1, BLOCK_M2, BLOCK_N2 = 32, 64, 64, 32 + BLK_SLICE_FACTOR = 2 + RCP_LN2 = 1.4426950408889634 # = 1.0 / ln(2) + arg_k = k + arg_k = arg_k * (ctx.sm_scale * RCP_LN2) + assert N_CTX % PRE_BLOCK == 0 + delta = torch.empty_like(M) + _, Lk, _ = q.shape[-1], k.shape[-1], v.shape[-1] + # padded_head = (Lk != ctx.BLOCK_DMODEL) + grid_preprocess = (triton.cdiv(do.shape[2], BLOCK), do.shape[1], do.shape[0]) + _attn_bwd_preprocess[grid_preprocess]( + o, + do, + delta, + o.stride(0), + o.stride(1), + o.stride(2), + o.stride(3), + do.stride(0), + do.stride(1), + do.stride(2), + do.stride(3), + seqlen_q, + head_dim=Lk, + BLOCK_M=BLOCK, + D_HEAD=ctx.BLOCK_DMODEL, + ) + grid = lambda META: (triton.cdiv(N_CTX, META['BLOCK_N1']), 1, BATCH * N_HEAD) + _attn_bwd[grid]( + q, + arg_k, + v, + ctx.sm_scale, + ctx.alibi_slopes, + do, + dq, + dk, + dv, + M, + delta, + q.stride(0), + q.stride(1), + q.stride(2), + q.stride(3), + N_HEAD, + N_CTX, + BLOCK_DMODEL=ctx.BLOCK_DMODEL, + BLOCK_M1=BLOCK_M1, + BLOCK_N1=BLOCK_N1, + BLOCK_M2=BLOCK_M2, + BLOCK_N2=BLOCK_N2, + BLK_SLICE_FACTOR=BLK_SLICE_FACTOR, + USE_ALIBI=False if ctx.alibi_slopes is None else True, + ) + + return dq, dk, dv, None, None + + +attention = _attention.apply + + +def input_helper(Z, HQ, HK, N_CTX_Q, N_CTX_K, D_HEAD, dtype, layout): + torch.manual_seed(20) + + # Initialize q, k, v + if layout == 'bhsd': + q_tensor_shape = (Z, HQ, N_CTX_Q, D_HEAD) + k_tensor_shape = (Z, HK, N_CTX_K, D_HEAD) + elif layout == 'bshd': + q_tensor_shape = (Z, N_CTX_Q, HQ, D_HEAD) + k_tensor_shape = (Z, N_CTX_K, HK, D_HEAD) + else: + assert False, 'Got unsupported tensor layout' + q = torch.randn(q_tensor_shape, dtype=dtype, device="cuda", requires_grad=True) + k = torch.randn(k_tensor_shape, dtype=dtype, device="cuda", requires_grad=True) + v = torch.randn(k_tensor_shape, dtype=dtype, device="cuda", requires_grad=True) + sm_scale = D_HEAD**-0.5 + input_metadata = MetaData(sm_scale=sm_scale) + input_metadata.max_seqlens_q = N_CTX_Q + input_metadata.max_seqlens_k = N_CTX_K + input_metadata.layout = layout + return q, k, v, input_metadata + + +def varlen_input_helper(Z, HQ, HK, N_CTX_Q, N_CTX_K, D_HEAD, dtype, equal_seqlens=False): + torch.manual_seed(20) + + # Random sequence lengths. Using N_CTX as kind of max of sum of individual seqs + if not equal_seqlens: + max_seqlens_q = N_CTX_Q // Z + max_seqlens_k = N_CTX_K // Z + seqlens_q = torch.randint(1, max_seqlens_q + 1, (Z, ), dtype=torch.int32) + seqlens_k = torch.randint(1, max_seqlens_k + 1, (Z, ), dtype=torch.int32) + else: + seqlens_q = torch.full((Z, ), N_CTX_Q // Z) + seqlens_k = torch.full((Z, ), N_CTX_K // Z) + + # Calculate cumulative sequence lengths + cu_seqlens_q = torch.cat([torch.tensor([0], dtype=torch.int32), seqlens_q.cumsum(dim=0, dtype=torch.int32)]) + cu_seqlens_k = torch.cat([torch.tensor([0], dtype=torch.int32), seqlens_k.cumsum(dim=0, dtype=torch.int32)]) + cu_seqlens_q = cu_seqlens_q.to(device="cuda") + cu_seqlens_k = cu_seqlens_k.to(device="cuda") + + # Initialize q, k, v with variable lengths + total_q = cu_seqlens_q[-1].item() + total_k = cu_seqlens_k[-1].item() + q = torch.randn((total_q, HQ, D_HEAD), dtype=dtype, device="cuda").normal_(mean=0., std=0.5).requires_grad_() + k = torch.randn((total_k, HK, D_HEAD), dtype=dtype, device="cuda").normal_(mean=0., std=0.5).requires_grad_() + v = torch.randn((total_k, HK, D_HEAD), dtype=dtype, device="cuda").normal_(mean=0., std=0.5).requires_grad_() + sm_scale = D_HEAD**-0.5 + input_metadata = MetaData(sm_scale=sm_scale) + input_metadata.set_varlen_params(cu_seqlens_q, cu_seqlens_k) + return q, k, v, input_metadata + + +@pytest.mark.parametrize('Z, HQ, HK, N_CTX_Q, N_CTX_K, D_HEAD', [ + (4, 48, 24, 1024, 1024, 64), + (1, 24, 6, 8192, 8192, 64), + (1, 4, 2, 16384, 16384, 128), + (2, 16, 4, 1020, 987, 128), + (2, 16, 4, 15498, 2, 128), + (2, 16, 2, 7, 16219, 64), + (4, 48, 12, 1, 1, 64), + (4, 48, 48, 1, 1, 128), + (4, 48, 24, 3, 3, 128), + (4, 48, 48, 1001, 990, 64), + (1, 8, 8, 8081, 7099, 64), + (1, 4, 4, 16330, 15989, 128), + (4, 4, 1, 1024, 1024, 33), + (4, 4, 2, 65, 1018, 65), + (4, 4, 4, 128, 128, 65), + (4, 4, 4, 113, 123, 1), +]) +@pytest.mark.parametrize('causal', [True, False]) +@pytest.mark.parametrize('use_alibi', [True, False]) +@pytest.mark.parametrize('layout', ['bshd', 'bhsd']) +def test_op_fwd(Z, HQ, HK, N_CTX_Q, N_CTX_K, D_HEAD, causal, use_alibi, layout, dtype=torch.float16): + torch.manual_seed(20) + q, k, v, input_metadata = input_helper(Z, HQ, HK, N_CTX_Q, N_CTX_K, D_HEAD, dtype, layout) + if causal: + input_metadata.need_causal() + + if use_alibi: + # for n heads the set of slopes is the geometric sequence that starts 2^(-8/n) + alibi_slopes = torch.tensor([2**(-8 / HQ * i) for i in range(1, HQ + 1)], dtype=torch.float32, + device="cuda").repeat(Z, 1) + input_metadata.need_alibi(alibi_slopes, Z, HQ) + else: + alibi_slopes = None + + o = torch.empty_like(q) + + # triton implementation + tri_out, _ = attention(q, k, v, o, input_metadata) + + # Transpose here if layout is bshd so we have same reference code for all layouts + if layout == 'bshd': + q = q.transpose(1, 2).clone() + k = k.transpose(1, 2).clone() + v = v.transpose(1, 2).clone() + # Replicate K and V if using MQA/GQA + if HQ != HK: + k = k.view(k.shape[0], k.shape[1], -1, k.shape[2], + k.shape[3]).expand(-1, -1, HQ // HK, -1, -1).reshape(k.shape[0], -1, k.shape[2], k.shape[3]) + v = v.view(v.shape[0], v.shape[1], -1, v.shape[2], + v.shape[3]).expand(-1, -1, HQ // HK, -1, -1).reshape(v.shape[0], -1, v.shape[2], v.shape[3]) + + scores = torch.einsum('bhqd,bhkd->bhqk', q, k).float() * input_metadata.sm_scale + if causal: + mask = torch.tril(torch.ones(N_CTX_Q, N_CTX_K, device="cuda"), diagonal=N_CTX_K - N_CTX_Q) + scores[:, :, mask == 0] = float("-inf") + if use_alibi: + scores += compute_alibi_tensor(alibi_slopes, N_CTX_Q, N_CTX_K) + + p = torch.softmax(scores, dim=-1) + if causal: + # If N_CTX_Q > N_CTX_K, there is at least one row of all -infs going into + # the softmax. This produces a row of NaNs as -inf - -inf == NaN. So we fix + # this by converting the NaNs to 0s, which is what they should be out of the softmax. + nan_mask = torch.isnan(p) + p[nan_mask == 1] = 0 + ref_out = torch.einsum('bhqk,bhkd->bhqd', p.half(), v) + # compare + if layout == 'bshd': + ref_out = ref_out.transpose(1, 2).clone() + torch.testing.assert_close(ref_out, tri_out, atol=2e-2, rtol=2e-2) + + +@pytest.mark.parametrize('Z, H, N_CTX_Q, N_CTX_K, D_HEAD', [ + (4, 48, 1024, 1024, 64), + (4, 12, 8192, 8192, 64), + (2, 4, 16384, 16384, 128), + (2, 16, 1020, 987, 128), + (2, 16, 15498, 2, 128), + (2, 4, 7, 16219, 64), + (4, 48, 1, 1, 64), + (4, 48, 1, 1, 128), + (4, 48, 3, 3, 128), + (4, 48, 1001, 990, 64), + (1, 8, 8081, 7099, 64), + (1, 8, 16330, 15989, 128), + (4, 4, 1024, 1024, 33), + (4, 4, 65, 1019, 65), + (4, 4, 128, 128, 65), + # TODO: This config fails. Disabled until triaged and fixed. + # (4, 4, 113, 123, 1), +]) +@pytest.mark.parametrize('causal', [True, False]) +@pytest.mark.parametrize('use_bias', [True]) +@pytest.mark.parametrize('dtype', [torch.float16, torch.bfloat16]) +def test_op_fwd_bias(Z, H, N_CTX_Q, N_CTX_K, D_HEAD, causal, use_bias, dtype): + torch.manual_seed(20) + sm_scale = D_HEAD**-0.5 + input_metadata = MetaData(sm_scale=sm_scale) + q, k, v, input_metadata = input_helper(Z, H, H, N_CTX_Q, N_CTX_K, D_HEAD, dtype, layout='bhsd') + if causal: + input_metadata.need_causal() + if use_bias: + bias = torch.randn((1, H, N_CTX_Q, N_CTX_K), dtype=dtype, device="cuda") + input_metadata.need_bias(bias, Z, H, N_CTX_Q, N_CTX_K) + else: + bias = None + o = torch.empty_like(q) + + # triton implementation + tri_out, _ = attention(q, k, v, o, input_metadata) + # reference implementation:171 + + scores = torch.einsum('bhqd,bhkd->bhqk', q, k).float() * sm_scale + if causal: + mask = torch.tril(torch.ones(N_CTX_Q, N_CTX_K, device="cuda"), diagonal=N_CTX_K - N_CTX_Q) + scores[:, :, mask == 0] = float("-inf") + if use_bias: + scores += input_metadata.bias + p = torch.softmax(scores, dim=-1) + if causal: + # If N_CTX_Q > N_CTX_K, there is at least one row of all -infs going into + # the softmax. This produces a row of NaNs as -inf - -inf == NaN. So we fix + # this by converting the NaNs to 0s, which is what they should be out of the softmax. + nan_mask = torch.isnan(p) + p[nan_mask == 1] = 0 + + ref_out = torch.einsum('bhqk,bhkd->bhqd', p.to(dtype), v) + # compare + torch.testing.assert_close(ref_out, tri_out, atol=2e-2, rtol=2e-2) + + +@pytest.mark.parametrize('Z, H, N_CTX, D_HEAD', [(4, 48, 8192, 64), (4, 48, 256, 64), (4, 48, 512, 64), + (4, 48, 1024, 64), (8, 48, 4096, 64), (4, 48, 8192, 64), + (4, 48, 128, 128), (4, 48, 4096, 128), (4, 48, 16384, 128), + (4, 16, 1024, 128), (4, 16, 8192, 128), (32, 48, 8192, 128)]) +@pytest.mark.parametrize('causal', [True, False]) +def test_op_varlen_fwd(Z, H, N_CTX, D_HEAD, causal, dtype=torch.float16): + + q, k, v, input_metadata = varlen_input_helper(Z, H, H, N_CTX, N_CTX, D_HEAD, dtype) + + tri_out = torch.empty_like(q) + ref_out = torch.empty_like(q) + + for i in range(0, input_metadata.num_contexts): + start_q, start_k = input_metadata.cu_seqlens_q[i], input_metadata.cu_seqlens_k[i] + end_q, end_k = input_metadata.cu_seqlens_q[i + 1], input_metadata.cu_seqlens_k[i + 1] + scores = torch.einsum('qhd,khd->qhk', q[start_q:end_q], k[start_k:end_k]).float() + p = torch.softmax(scores * input_metadata.sm_scale, dim=-1).half() + ref_out[start_q:end_q] = torch.einsum('qhk,khd->qhd', p, v[start_k:end_k]) + attention(q, k, v, tri_out, input_metadata) + torch.testing.assert_close(ref_out, tri_out, atol=1e-2, rtol=1e-2) + + +@pytest.mark.parametrize('Z, HQ, HK, N_CTX, D_HEAD', [(2, 48, 24, 128, 64), (4, 48, 12, 256, 64), (4, 48, 4, 512, 64), + (4, 48, 2, 1024, 64), (8, 48, 6, 4096, 64), (4, 48, 8, 16384, 64), + (4, 64, 16, 128, 128), (4, 64, 4, 4096, 128), + (4, 64, 8, 16384, 128), (4, 16, 4, 1024, 128), + (4, 16, 2, 8192, 128), (32, 128, 32, 8192, 128)]) +@pytest.mark.parametrize('causal', [False]) +def test_op_varlen_mqa_fwd(Z, HQ, HK, N_CTX, D_HEAD, causal, dtype=torch.float16): + q, k, v, input_metadata = varlen_input_helper(Z, HQ, HK, N_CTX, N_CTX, D_HEAD, dtype) + ref_out = torch.empty_like(q) + tri_out = torch.empty_like(q) + # Make KV look like HQ/HK "groups" of HK. Later, we will reshape so the + # size aligns with Q. + k_ref = k.view(k.shape[0], k.shape[1], 1, k.shape[2]).expand(-1, -1, HQ // HK, -1) + v_ref = v.view(v.shape[0], v.shape[1], 1, v.shape[2]).expand(-1, -1, HQ // HK, -1) + for i in range(0, input_metadata.num_contexts): + start_q, start_k = input_metadata.cu_seqlens_q[i], input_metadata.cu_seqlens_k[i] + end_q, end_k = input_metadata.cu_seqlens_q[i + 1], input_metadata.cu_seqlens_k[i + 1] + k_curr = k_ref[start_k:end_k] + k_curr = k_curr.reshape(k_curr.shape[0], -1, k_curr.shape[3]) + v_curr = v_ref[start_k:end_k] + v_curr = v_curr.reshape(v_curr.shape[0], -1, v_curr.shape[3]) + scores = torch.einsum('qhd,khd->qhk', q[start_q:end_q], k_curr).float() + p = torch.softmax(scores * input_metadata.sm_scale, dim=-1).half() + ref_out[start_q:end_q] = torch.einsum('qhk,khd->qhd', p, v_curr) + attention(q, k, v, tri_out, input_metadata) + torch.testing.assert_close(ref_out, tri_out, atol=1e-2, rtol=1e-2) + + +@pytest.mark.parametrize('Z, H, N_CTX, D_HEAD', [ + (4, 48, 1024, 64), + (4, 48, 2048, 64), + (2, 48, 4096, 64), + (1, 16, 1024, 64), + (1, 16, 1024, 128), + #(1, 16, 8192, 63), + #(1, 16, 1022, 64), +]) +@pytest.mark.parametrize('qseqlen_not_equal_kseqlen', [None]) +@pytest.mark.parametrize('torch_sdpa_test', [False, True]) +@pytest.mark.parametrize('causal', [True]) +@pytest.mark.parametrize('use_alibi', [False, True]) +def test_op_bwd(Z, H, N_CTX, D_HEAD, qseqlen_not_equal_kseqlen, causal, torch_sdpa_test, use_alibi, + dtype=torch.float16): + pytest.skip() + torch.manual_seed(20) + if qseqlen_not_equal_kseqlen is not None: + seqlen_q = qseqlen_not_equal_kseqlen + else: + seqlen_q = N_CTX + seqlen_k = N_CTX + + if causal and ((N_CTX - 1) & N_CTX): + pytest.skip() + if causal and seqlen_q != seqlen_k: + pytest.skip() + + sm_scale = D_HEAD**-0.5 + input_metadata = MetaData(sm_scale=sm_scale) + input_metadata.max_seqlens_q = seqlen_q + input_metadata.max_seqlens_k = seqlen_k + + dropout_p = 0 + q = (torch.empty((Z, H, seqlen_q, D_HEAD), dtype=dtype, device="cuda").normal_(mean=0.0, std=0.5).requires_grad_()) + k = (torch.empty((Z, H, seqlen_k, D_HEAD), dtype=dtype, device="cuda").normal_(mean=0.0, std=0.5).requires_grad_()) + v = (torch.empty((Z, H, seqlen_k, D_HEAD), dtype=dtype, device="cuda").normal_(mean=0.0, std=0.5).requires_grad_()) + o = torch.empty_like(q) + + if causal: + input_metadata.need_causal() + + if use_alibi and not torch_sdpa_test: + # for n heads the set of slopes is the geometric sequence that starts 2^(-8/n) + alibi_slopes = torch.tensor([2**(-8 / H * i) for i in range(1, H + 1)], dtype=torch.float32, + device="cuda").repeat(Z, 1) + input_metadata.need_alibi(alibi_slopes, Z, H) + dout = torch.randn_like(q) + # reference implementation + if torch_sdpa_test: + ref_out, ref_softmax = torch.ops.aten._scaled_dot_product_attention_math(q, k, v, dropout_p=dropout_p, + is_causal=causal, scale=sm_scale, + dropout_mask=None) + ref_out.backward(dout.to(device=ref_out.device, dtype=ref_out.dtype)) + ref_dv, v.grad = v.grad.clone(), None + ref_dk, k.grad = k.grad.clone(), None + ref_dq, q.grad = q.grad.clone(), None + else: + M = torch.tril(torch.ones((seqlen_q, seqlen_k), device="cuda")) + p = torch.matmul(q, k.transpose(2, 3)) * sm_scale + if use_alibi: + p += compute_alibi_tensor(alibi_slopes, N_CTX, N_CTX) + if causal: + p[:, :, M == 0] = float("-inf") + + p = torch.softmax(p.float(), dim=-1).type(dtype=p.dtype) + ref_out = torch.matmul(p, v) + ref_out.backward(dout) + ref_dv, v.grad = v.grad.clone(), None + ref_dk, k.grad = k.grad.clone(), None + ref_dq, q.grad = q.grad.clone(), None + + # # triton implementation + tri_out, _ = attention(q, k, v, o, input_metadata) + tri_out.backward(dout) + tri_dv, v.grad = v.grad.clone(), None + tri_dk, k.grad = k.grad.clone(), None + tri_dq, q.grad = q.grad.clone(), None + # test + #print("reference") + #print(ref_dv) + #print("tri") + #print(tri_dv) + # compare + torch.testing.assert_close(ref_out, tri_out, atol=1e-2, rtol=0) + # The current block size for MI200 series is 64x64. This results in + # larger differences in float results due to rounding. + + if dtype == torch.bfloat16: + ATOL = 1e-1 * max(1.0, (seqlen_q + D_HEAD) / 64.0) + if dtype == torch.float32: + ATOL = 1e-3 * max(1.0, (seqlen_q + D_HEAD) / 64.0) + else: + ATOL = 1e-1 * max(1.0, (seqlen_q + D_HEAD) / 64.0) + + RTOL = 0 + + torch.testing.assert_close(ref_dv, tri_dv, atol=ATOL, rtol=RTOL) + torch.testing.assert_close(ref_dk, tri_dk, atol=ATOL, rtol=RTOL) + torch.testing.assert_close(ref_dq, tri_dq, atol=ATOL, rtol=RTOL) + + +def nonvarlen_benchmark_configs(): + configs = [ + (16, 16, 16, 1024, 1024), + (8, 16, 16, 2048, 2048), + (4, 16, 16, 4096, 4096), + (2, 16, 16, 8192, 8192), + (1, 16, 16, 16384, 16384), + (2, 48, 48, 1024, 1024), + (2, 48, 48, 2048, 1024), + (2, 48, 48, 4096, 8192), + (2, 48, 48, 8192, 4096), + (2, 48, 48, 16384, 8192), + (8, 16, 16, 1989, 15344), + (4, 16, 16, 4097, 163), + (2, 16, 16, 8122, 2159), + (1, 16, 16, 16281, 7), + (2, 48, 48, 1021, 1020), + (2, 48, 48, 2001, 2048), + (2, 48, 48, 3996, 9639), + (2, 48, 48, 8181, 1021), + ] + return configs + + +def varlen_benchmark_configs(): + configs = [ + (2, 16, 4, 1024, 1024), + (8, 16, 2, 2048, 2048), + (4, 16, 8, 4096, 4096), + (2, 16, 4, 8192, 8192), + (2, 16, 8, 16384, 16384), + (2, 48, 12, 1024, 1024), + (2, 48, 24, 2048, 2048), + (2, 48, 8, 4096, 4096), + (2, 48, 4, 8192, 8192), + (2, 48, 2, 16384, 16384), + (2, 64, 32, 1024, 1024), + (4, 64, 16, 2048, 2048), + (4, 64, 8, 4096, 4096), + (4, 64, 32, 8192, 8192), + (4, 128, 16, 16384, 16384), + ] + return configs + + +def run_benchmark(custom, args): + + dtype = arg_to_torch_dtype[args.dtype] + hk = args.hq if not args.hk else args.hk + sk = args.sq if not args.sk else args.sk + head_size = 128 if not args.d else args.d + mode = 'fwd' + x_names = ['BATCH', 'HQ', 'HK', 'N_CTX_Q', 'N_CTX_K'] + causal = args.causal + varlen = args.layout == 'thd' + configs = [] + if custom: + x_vals_list = [(args.b, args.hq, hk, args.sq, sk)] + else: + if varlen: + x_vals_list = varlen_benchmark_configs() + else: + x_vals_list = nonvarlen_benchmark_configs() + print_time = args.return_time + line_names = 'Time (ms)' if print_time else 'TFLOPS' + configs.append( + triton.testing.Benchmark(x_names=x_names, x_vals=x_vals_list, line_arg='provider', line_vals=['triton'], + line_names=[line_names], styles=[('red', '-')], ylabel='ms', + plot_name=f'fused-attention-{mode}-d{head_size}-layout{args.layout}', + args={'D_HEAD': head_size, 'dtype': dtype, 'causal': causal, 'mode': mode})) + + @triton.testing.perf_report(configs) + def bench_flash_attention(BATCH, HQ, HK, N_CTX_Q, N_CTX_K, D_HEAD, dtype, causal, mode, provider, device="cuda"): + assert mode in ["fwd", "bwd"] + warmup = 25 + rep = 100 + # TODO: Enable bias after testing. + # if use_bias: + # bias = torch.randn((1, H, N_CTX, N_CTX), dtype=torch.float32, device="cuda") + # input_metadata.need_bias(bias, BATCH, H, N_CTX, N_CTX) + # else: + # bias = None + # bias = None + + # Bwd pass only supports causal=True right now + if mode == 'bwd': + causal = True + + flops_per_matmul = 0 + if varlen: + q, k, v, input_metadata = varlen_input_helper(BATCH, HQ, HK, N_CTX_Q, N_CTX_K, D_HEAD, dtype, + args.equal_seqlens) + for i in range(0, input_metadata.num_contexts): + seqlen_q = input_metadata.cu_seqlens_q[i + 1] - input_metadata.cu_seqlens_q[i] + seqlen_k = input_metadata.cu_seqlens_k[i + 1] - input_metadata.cu_seqlens_k[i] + # x2 for 2 GEMMs + flops_per_matmul += seqlen_q.item() * seqlen_k.item() * HQ * D_HEAD * 2 + else: + q, k, v, input_metadata = input_helper(BATCH, HQ, HK, N_CTX_Q, N_CTX_K, D_HEAD, dtype, args.layout) + flops_per_matmul = 2.0 * BATCH * HQ * N_CTX_Q * N_CTX_K * D_HEAD + if causal: + input_metadata.need_causal() + o = torch.empty_like(q) + fn = lambda: attention(q, k, v, o, input_metadata) + if mode == 'bwd': + o, _ = fn() + do = torch.randn_like(o) + fn = lambda: o.backward(do, retain_graph=True) + ms = triton.testing.do_bench(fn, warmup=warmup, rep=rep) + total_flops = 2 * flops_per_matmul + # TODO: This needs to be fixed for unequal Q/K seqlens + if causal: + total_flops *= 0.5 + if mode == "bwd": + total_flops *= 2.5 # 2.0(bwd) + 0.5(recompute) + if print_time: + return ms + else: + return total_flops / ms * 1e-9 + + bench_flash_attention.run(save_path=".", print_data=True) + + +def supported_layouts(): + layouts = \ + 'bhsd: Q, K, V are individual tensors of [batch, num_heads, seqlen_q/k, head_size]' \ + 'bshd: Q, K, V are individual tensors of [batch, seqlen_q/k, num_heads, head_size]' \ + 'thd: Q, K, V are individual tensors of [total_q/k, num_heads, head_size]' \ + 'This layout is sometimes called "varlen" or "grouped" layout.' + return layouts + + +def parse_args(): + parser = argparse.ArgumentParser( + prog="Benchmark FlashAttention", + allow_abbrev=False, + ) + parser.add_argument("-b", type=int, default=0) + parser.add_argument("-hq", type=int, default=0) + parser.add_argument("-hk", type=int, default=0) + parser.add_argument("-sq", type=int, default=0) + parser.add_argument("-sk", type=int, default=0) + parser.add_argument("-equal_seqlens", action='store_true', default=False, + help='If specified, each context within the thd layout' \ + ' has same seqlen as sq and sk') + parser.add_argument("-d", type=int, default=0) + parser.add_argument("-causal", action='store_true', default=False) + parser.add_argument("-dtype", default='fp16') + parser.add_argument("-return_time", action='store_true', default=False) + parser.add_argument("-layout", type=str, default='bhsd', help=supported_layouts()) + return parser.parse_args() + + +arg_to_torch_dtype = {'fp16': torch.float16, 'bf16': torch.bfloat16, 'fp32': torch.float32} + + +def main(): + args = parse_args() + custom_config = False + assert args.layout == 'thd' or not args.equal_seqlens, \ + "Equal sequence lengths arg must be used with the thd layout." + if args.b or args.hq or args.hk or args.sq or args.sk or args.d: + custom_config = True + assert args.b and args.hq and args.sq and args.d, \ + "If custom config is specified, please provide \ + all of batch, number of Q heads, Q sequence length \ + and head size." + + assert args.dtype in arg_to_torch_dtype, \ + "Only fp16, bf16 and f32 types currently supported." + + run_benchmark(custom_config, args) + + +if __name__ == '__main__': + sys.exit(main()) diff --git a/python/perf-kernels/hbm-bw-test.py b/python/perf-kernels/hbm-bw-test.py new file mode 100644 index 000000000000..a20ce044eaee --- /dev/null +++ b/python/perf-kernels/hbm-bw-test.py @@ -0,0 +1,200 @@ +""" +Simple test to measure achieved HBM bandwidth. +This kernel moves N bytes of data from one region in HBM to another, using Triton. +""" + +# %% +# Compute Kernel +# -------------- + +import argparse +import sys +import torch + +import triton +import triton.language as tl + + +@triton.jit +def copy_kernel( + input_ptr, # *Pointer* to input vector. + output_ptr, # *Pointer* to output vector. + NUM_ELEMENTS: tl.constexpr, # Total elements to move. + BLOCK_SIZE: tl.constexpr, # Elements to load / store per iteration + VECTOR_SIZE: tl.constexpr, # Size of the entire vector being moved. + READ_ONLY: tl.constexpr, +): + pid = tl.program_id(axis=0) + # Offset at which to start for this WG. + lo = pid * NUM_ELEMENTS + # Offset until which to read for this WG. + hi = lo + NUM_ELEMENTS + # NUM_ITERS: tl.constexpr = triton.cdiv(NUM_ELEMENTS, BLOCK_SIZE) + IRREGULAR_SIZE: tl.constexpr = NUM_ELEMENTS % BLOCK_SIZE + acc = tl.zeros([BLOCK_SIZE], dtype=tl.float32) + if IRREGULAR_SIZE: + hi = hi - IRREGULAR_SIZE + # Move buffer in chunks of block_size + for idx in range(lo, hi, BLOCK_SIZE): + offsets = idx + tl.arange(0, BLOCK_SIZE) + in_vals = tl.load(input_ptr + offsets) + acc += in_vals + if not READ_ONLY: + tl.store(output_ptr + offsets, in_vals) + # Unroll last irregular iter in case the total sized moved by this WG + # is not a multiple of block size. + if IRREGULAR_SIZE: + lo = hi + hi = hi + IRREGULAR_SIZE + offsets = lo + tl.arange(0, BLOCK_SIZE) + mask = offsets < hi + in_vals = tl.load(input_ptr + offsets, mask=mask) + if not READ_ONLY: + tl.store(output_ptr + offsets, in_vals, mask=mask) + + if READ_ONLY: + tl.store(output_ptr + tl.arange(0, BLOCK_SIZE), acc) + + +def copy(src: torch.Tensor, block_size, wgs, dst: torch.Tensor): + assert src.is_cuda + vector_size = src.numel() + assert dst.numel() == vector_size or dst.numel() == block_size + size_per_wg = vector_size / wgs + assert size_per_wg >= block_size, \ + "Too many WGS. Please increase the size of the buffer using -size." \ + f" We want a buffer of size {wgs * block_size} f32 elements or larger." + grid = (wgs, 1, 1) + # Each WG will move these many elements + n_elements = triton.cdiv(vector_size, wgs) + # If we want to read only, we do a dummy write of a single block size back to HBM + read_only = dst.numel() != src.numel() + copy_kernel[grid]( + src, + dst, + NUM_ELEMENTS=n_elements, + BLOCK_SIZE=block_size, + VECTOR_SIZE=vector_size, + READ_ONLY=read_only, + num_warps=4, + ) + + +def get_reference(x, wgs, gbps): + ms = triton.testing.do_bench(lambda: torch.clone(x)) + bw = gbps(ms) + triton_output = torch.empty_like(x) + copy(x, block_size=16384, wgs=wgs, dst=triton_output) + err = triton_output - x + if torch.count_nonzero(err): + assert False, f"Torch and Triton do not match - max error is "\ + f"{torch.max(torch.abs(err))}" + return bw + + +def align_size_to_wgs(size, wgs): + return (size // wgs) * wgs + + +def run_benchmark_suite(vector_size, block_size, num_cores, read_only): + configs = [] + # Define WGs in powers of 2 from 1 - 2048. + x_vals = [(2**i) for i in range(0, 12)] + num_cu_aligned_wgs = [(num_cores * i) for i in range(1, 5)] + import bisect + for i in num_cu_aligned_wgs: + bisect.insort(x_vals, i) + configs.append( + triton.testing.Benchmark( + x_names=['wgs'], # Argument names to use as an x-axis for the plot. + x_vals=x_vals, x_log=True, # x axis is logarithmic. + line_arg='provider', # Argument name whose value corresponds to a different line in the plot. + line_vals=['triton'], # Possible values for `line_arg`. + line_names=['Triton'], # Label name for the lines. + styles=[('blue', '-'), ('green', '-')], # Line styles. + ylabel='GiB/s', # Label name for the y-axis. + plot_name=f'size={vector_size}', # Name for the plot. Used also as a file name for saving the plot. + args={'size': vector_size}, # Values for function arguments not in `x_names` and `y_name`. + )) + + @triton.testing.perf_report(configs) + def benchmark(size, provider, wgs): + aligned_size = align_size_to_wgs(size, wgs) + src_tensor = torch.randn(aligned_size, device='cuda') + dst_tensor = torch.empty(block_size, device='cuda') + if not read_only: + dst_tensor = torch.empty_like(src_tensor) + ms = triton.testing.do_bench(lambda: copy(src_tensor, block_size, wgs, dst_tensor)) + # 8 because 4 bytes from load, 4 from store. + if read_only: + gbps = lambda ms: 4 * size / ms * 1e3 / 1024**3 + else: + gbps = lambda ms: 8 * size / ms * 1e3 / 1024**3 + return gbps(ms) + + benchmark.run(print_data=True, show_plots=True) + + +def parse_args(): + parser = argparse.ArgumentParser( + prog="HBM Bandwidth Benchmark", + allow_abbrev=False, + ) + parser.add_argument("-direction", type=str, default="read-only", + help="Data movement direction: read-only, read-write") + parser.add_argument("-size", type=int, default=1024, help="Size of buffer moved, in MiB") + parser.add_argument("-num_wgs", type=int, default=0, help="Number of workgroups to use") + parser.add_argument("-block_size", type=int, default=16384, help="Block size per iteration to load / store") + parser.add_argument("-run_sweep", action='store_true', default=False, help="Run sweep of B/W vs workgroups") + return parser.parse_args() + + +def main(): + args = parse_args() + torch.manual_seed(0) + num_cores = torch.cuda.get_device_properties(torch.cuda.current_device()).multi_processor_count + size = args.size + rw = args.direction == "read_write" + num_elements = size * 1024 * 1024 // 4 + if args.run_sweep: + assert args.num_wgs == 0, "If running the benchmark suite, please do not specify the number of WGs to use." + run_benchmark_suite(num_elements, args.block_size, num_cores, not rw) + return + if args.num_wgs == 0: + # num_wgs not user specified - get from device properties + num_wgs = num_cores + print(f"Using {num_wgs} workgroups. It is recommended to "\ + "use -num_wgs to provide this number.") + else: + assert args.num_wgs > 0, "Please provide a positive, non-zero number of workgroups!" + num_wgs = args.num_wgs + if num_wgs % num_cores: + print(f"Note! Your device has {num_cores} cores. It is recommended to use"\ + " a number for workgroups that is a multiple of this number."\ + f" You have currently chosen {num_wgs}.") + num_elements_rounded = align_size_to_wgs(num_elements, num_wgs) + if num_elements != num_elements_rounded: + print(f"Removing last {num_elements - num_elements_rounded} elements to "\ + "get a tensor size aligned to multiple of number of workgroups.") + num_elements = num_elements_rounded + src_tensor = torch.randn(num_elements, device="cuda") + if rw: + # 8 because 4B for read. 4B for write. + gbps = lambda ms: 8 * num_elements / ms * 1e3 / 1024**3 + ref_bw = get_reference(src_tensor, num_wgs, gbps) + print(f"Reference PyTorch bandwidth = {ref_bw} GiB/s") + else: + gbps = lambda ms: 4 * num_elements / ms * 1e3 / 1024**3 + if size < 1024: + print("Note! It is recommended to use a buffer larger than 1 GiB.") + if num_elements % args.block_size: + print("Note! This config is suboptimal. It is recommended to use a buffer that"\ + f" is a multiple of wgs x block size = {num_wgs * args.block_size} elements.") + dst_tensor = torch.empty_like(src_tensor) if rw else torch.empty(args.block_size, device='cuda') + triton_ms = triton.testing.do_bench(lambda: copy(src_tensor, args.block_size, num_wgs, dst=dst_tensor), warmup=1, + rep=1) + print(f"Triton bandwidth = {gbps(triton_ms)} GiB/s") + + +if __name__ == '__main__': + sys.exit(main()) diff --git a/python/perf-kernels/layernorm.py b/python/perf-kernels/layernorm.py new file mode 100644 index 000000000000..a320f168af5d --- /dev/null +++ b/python/perf-kernels/layernorm.py @@ -0,0 +1,253 @@ +import argparse +import sys +import pytest + +import torch +import triton +import triton.language as tl + + +def is_cuda(): + return triton.runtime.driver.active.get_current_target().backend == "cuda" + + +def is_hip(): + return triton.runtime.driver.active.get_current_target().backend == "hip" + + +def get_cuda_autotune_config(): + return [ + triton.Config({}, num_warps=4, num_stages=1), + triton.Config({}, num_warps=8, num_stages=1), + triton.Config({}, num_warps=16, num_stages=1), + ] + + +def get_hip_autotune_config(): + return [ + triton.Config({'waves_per_eu': 1}, num_warps=4, num_stages=1), + triton.Config({'waves_per_eu': 1}, num_warps=8, num_stages=1), + triton.Config({'waves_per_eu': 1}, num_warps=16, num_stages=1), + triton.Config({'waves_per_eu': 2}, num_warps=4, num_stages=1), + triton.Config({'waves_per_eu': 2}, num_warps=8, num_stages=1), + triton.Config({'waves_per_eu': 2}, num_warps=16, num_stages=1), + triton.Config({'waves_per_eu': 4}, num_warps=4, num_stages=1), + triton.Config({'waves_per_eu': 4}, num_warps=8, num_stages=1), + triton.Config({'waves_per_eu': 4}, num_warps=16, num_stages=1), + ] + + +def get_autotune_config(): + if is_cuda(): + return get_cuda_autotune_config() + else: + return get_hip_autotune_config() + + +@triton.autotune(configs=get_autotune_config(), key=['n_rows', 'n_cols'], use_cuda_graph=True) +@triton.jit +def layernorm_kernel(x_ptr, y_ptr, w_ptr, b_ptr, x_row_stride, y_row_stride, n_rows, n_cols, eps, + BLOCK_SIZE: tl.constexpr): + + #program id + row = tl.program_id(0) + x_ptr_start = x_ptr + (row * x_row_stride) + y_ptr_start = y_ptr + (row * y_row_stride) + + loop_num = tl.cdiv(n_cols, BLOCK_SIZE) - 1 + + #calculate mean + mean = 0 + _mean = tl.zeros([BLOCK_SIZE], dtype=tl.float32) + loop_num_l = loop_num + for b in range(0, loop_num_l): + col_offsets = b * BLOCK_SIZE + tl.arange(0, BLOCK_SIZE) + x_block = tl.load(x_ptr_start + col_offsets).to(tl.float32) #Unmasked loads + _mean += x_block + + #For last iteration, do masked load + col_offsets = loop_num_l * BLOCK_SIZE + tl.arange(0, BLOCK_SIZE) + x_block = tl.load(x_ptr_start + col_offsets, mask=col_offsets < n_cols, other=0.).to(tl.float32) + _mean += x_block + + mean = tl.sum(_mean, axis=0) / n_cols + + #variance + _var = tl.zeros([BLOCK_SIZE], dtype=tl.float32) + loop_num_l = loop_num + for b in range(0, loop_num_l): + col_offsets = b * BLOCK_SIZE + tl.arange(0, BLOCK_SIZE) + x_block = tl.load(x_ptr_start + col_offsets).to(tl.float32) #Unmasked loads + x_block = x_block - mean + _var += x_block * x_block + + #For last iteration, do masked load + col_offsets = loop_num_l * BLOCK_SIZE + tl.arange(0, BLOCK_SIZE) + x_block = tl.load(x_ptr_start + col_offsets, mask=col_offsets < n_cols, other=0.).to(tl.float32) + x_block = tl.where(col_offsets < n_cols, x_block - mean, 0.) + _var += x_block * x_block + + var = tl.sum(_var, axis=0) / n_cols + rstd = tl.rsqrt(var + eps) + + #Normalize and store + loop_num_l = loop_num + for b in range(0, loop_num_l): + col_offsets = b * BLOCK_SIZE + tl.arange(0, BLOCK_SIZE) + w_block = tl.load(w_ptr + col_offsets) + b_block = tl.load(b_ptr + col_offsets) + x_block = tl.load(x_ptr_start + col_offsets).to(tl.float32) + y_block = (x_block - mean) * rstd + y_block = y_block * w_block + b_block + tl.store(y_ptr_start + col_offsets, y_block) + + #For last iteration, do masked load and store + col_offsets = loop_num_l * BLOCK_SIZE + tl.arange(0, BLOCK_SIZE) + mask = col_offsets < n_cols + w_block = tl.load(w_ptr + col_offsets, mask=mask, other=0.0) + b_block = tl.load(b_ptr + col_offsets, mask=mask, other=0.0) + x_block = tl.load(x_ptr_start + col_offsets, mask=mask, other=0.0).to(tl.float32) + y_block = (x_block - mean) * rstd + y_block = y_block * w_block + b_block + tl.store(y_ptr_start + col_offsets, y_block, mask=mask) + + +def layernorm(x, w, b, eps=1e-5): + n_rows, n_cols = x.shape + + MAX_FUSED_SIZE = 65536 // x.element_size() + BLOCK_SIZE = min(MAX_FUSED_SIZE, triton.next_power_of_2(n_cols)) + y = torch.empty_like(x) + + num_programs = n_rows + + grid = lambda meta: (num_programs, ) + layernorm_kernel[grid](x, y, w, b, x.stride(0), y.stride(0), n_rows, n_cols, eps, BLOCK_SIZE) + + return y + + +def torch_layernorm(x, w, b): + M, N = x.shape + w_shape = (N, ) + y_torch = torch.nn.functional.layer_norm(x, w_shape, w, b, eps=1e-5) + return y_torch + + +def run_layernorm(M, N): + print(f"Running Layernorm on shape ({M},{N})") + torch.manual_seed(0) + x = torch.randn(M, N, device='cuda') + w_shape = (N, ) + w = torch.rand(w_shape, device='cuda') + b = torch.rand(w_shape, device='cuda') + y_triton = layernorm(x, w, b) + + return y_triton + + +#pytest +@pytest.mark.parametrize('M, N', [(1823, 781), (2, 128), (1, 4), (128, 2), (1, 128), (8192, 8192), (4096, 8192), + (359, 1), (1, 359), (1, 131072), (1, 89999)]) +def test_layernorm(M, N, eps=1e-5): + torch.manual_seed(0) + x = torch.randn(M, N, device='cuda') + w_shape = (N, ) + w = torch.rand(w_shape, device='cuda') + b = torch.rand(w_shape, device='cuda') + y_triton = layernorm(x, w, b, eps) + y_torch = torch.nn.functional.layer_norm(x, w_shape, w, b, eps) + + assert torch.allclose(y_triton, y_torch, rtol=1e-05, atol=1e-06) + + +#Benchmark +arg_to_torch_dtype = {'fp16': torch.float16, 'bf16': torch.bfloat16, 'fp32': torch.float32} + + +def run_benchmark(args): + config = [] + if (args.M_benchmark): + val = args.M_start + x_vals_list = [] + while val <= args.M_end: + x_vals_list.append(val) + val *= args.M_step + mn_args = {'N': args.N_start} + plot_name = str("layernorm-performance_" + args.dtype + "_N" + str(args.N_start) + "_M" + str(args.M_start) + + "-" + str(args.M_end) + "-" + str(args.M_step)) + x_names = ['M'] + else: + x_vals_list = [i for i in range(args.N_start, args.N_end, args.N_step)] + mn_args = {'M': args.M_start} + plot_name = str("layernorm-performance_" + args.dtype + "_M" + str(args.M_start) + "_N" + str(args.N_start) + + "-" + str(args.N_end) + "-" + str(args.N_step)) + x_names = ['N'] + dtype = arg_to_torch_dtype[args.dtype] + + print(plot_name) + config.append( + triton.testing.Benchmark( + x_names=x_names, + x_vals=x_vals_list, + line_arg='provider', + line_vals=['triton', 'torch'], + line_names=[ + "Triton", + "Torch", + ], + styles=[('blue', '-'), ('green', '-')], + ylabel="GB/s", + plot_name=plot_name, + args=mn_args, + )) + + @triton.testing.perf_report(config) + def benchmark(M, N, provider): + x = torch.randn(M, N, device='cuda', dtype=dtype) + w_shape = (N, ) + w = torch.rand(w_shape, device='cuda', dtype=dtype) + b = torch.rand(w_shape, device='cuda', dtype=dtype) + stream = torch.cuda.Stream() + torch.cuda.set_stream(stream) + if provider == 'torch': + ms = triton.testing.do_bench(lambda: torch_layernorm(x, w, b)) + if provider == 'triton': + ms = triton.testing.do_bench(lambda: layernorm(x, w, b)) + gbps = lambda ms: 2 * x.nelement() * x.element_size() * 1e-9 / (ms * 1e-3) + return gbps(ms) + + benchmark.run(save_path=".", show_plots=True, print_data=True) + + +def parse_args(): + parser = argparse.ArgumentParser( + prog="Benchmark Layernorm", + allow_abbrev=False, + ) + + parser.add_argument('-M', "--M_start", default="1", type=int) + parser.add_argument('-Ms', "--M_step", default="2", type=int) + parser.add_argument('-Me', "--M_end", default="512", type=int) + parser.add_argument('-Mb', "--M_benchmark", default=False, type=bool) + + parser.add_argument('-N', "--N_start", default="1024", type=int) + parser.add_argument('-Ns', "--N_step", default="2048", type=int) + parser.add_argument('-Ne', "--N_end", default="65536", type=int) + + parser.add_argument('-d', "--dtype", default="fp16") + parser.add_argument('-nb', "--no_benchmark", default=False, type=bool) + + return parser.parse_args() + + +def main(): + args = parse_args() + if args.no_benchmark: + run_layernorm(args.M_start, args.N_start) + else: + run_benchmark(args) + + +if __name__ == "__main__": + sys.exit(main()) diff --git a/python/perf-kernels/multreduce_matmul_kernel.py b/python/perf-kernels/multreduce_matmul_kernel.py new file mode 100644 index 000000000000..61535d5bcdd3 --- /dev/null +++ b/python/perf-kernels/multreduce_matmul_kernel.py @@ -0,0 +1,45 @@ +import triton +import triton.language as tl + + +# Kernel that implements GEMM with explicit multiply-reduce instructions for small block sizes. +# Based on **tune_gemm** `matmul_kernel` from commit `cf44637` (see `triton-mlir` branch). +@triton.jit +def multreduce_matmul_kernel(a_ptr, b_ptr, c_ptr, bias_ptr, M, N, K, stride_am, stride_ak, stride_bk, stride_bn, + stride_cm, stride_cn, stride_bias, BLOCK_SIZE_M: tl.constexpr, BLOCK_SIZE_N: tl.constexpr, + BLOCK_SIZE_K: tl.constexpr, BIAS: tl.constexpr, EVEN_K: tl.constexpr): + pid = tl.program_id(axis=0) + num_pid_n = tl.cdiv(N, BLOCK_SIZE_N) + pid_m = pid // num_pid_n + pid_n = pid % num_pid_n + offs_k = tl.arange(0, BLOCK_SIZE_K) + offs_am = (pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M)) + offs_bn = (pid_n * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N)) + a_ptrs = a_ptr + offs_am[:, None] * stride_am + offs_k[None, :] * stride_ak + b_ptrs = b_ptr + offs_k[:, None] * stride_bk + offs_bn[None, :] * stride_bn + if BIAS: + bias_ptrs = bias_ptr + offs_am * stride_bias + bias = tl.load(bias_ptrs, mask=offs_am < M, other=0.0) + acc_dtype = tl.float32 if a_ptr.type.element_ty != tl.int8 else tl.int32 + accumulator = tl.zeros((BLOCK_SIZE_M, BLOCK_SIZE_N), dtype=acc_dtype) + for k in range(0, tl.cdiv(K, BLOCK_SIZE_K)): + if EVEN_K: + a = tl.load(a_ptrs) + b = tl.load(b_ptrs) + else: + a = tl.load(a_ptrs, mask=offs_k[None, :] < K - k * BLOCK_SIZE_K, other=0.0) + b = tl.load(b_ptrs, mask=offs_k[:, None] < K - k * BLOCK_SIZE_K, other=0.0) + # Dot product implemented as explicit multiply-reduce: + a = tl.reshape(a, (BLOCK_SIZE_M, BLOCK_SIZE_K, 1)).to(acc_dtype) + b = tl.reshape(b, (1, BLOCK_SIZE_K, BLOCK_SIZE_N)).to(acc_dtype) + accumulator += tl.sum(a * b, axis=1) + a_ptrs += BLOCK_SIZE_K * stride_ak + b_ptrs += BLOCK_SIZE_K * stride_bk + c = accumulator.to(c_ptr.type.element_ty) + if BIAS: + c += bias[:, None] + offs_cm = pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M) + offs_cn = pid_n * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N) + c_ptrs = c_ptr + stride_cm * offs_cm[:, None] + stride_cn * offs_cn[None, :] + c_mask = (offs_cm[:, None] < M) & (offs_cn[None, :] < N) + tl.store(c_ptrs, c, mask=c_mask) diff --git a/python/perf-kernels/rmsnorm.py b/python/perf-kernels/rmsnorm.py new file mode 100644 index 000000000000..0df4e2a62517 --- /dev/null +++ b/python/perf-kernels/rmsnorm.py @@ -0,0 +1,215 @@ +import argparse +import torch +import sys +import pytest + +import triton +import triton.language as tl + + +def is_cuda(): + return triton.runtime.driver.active.get_current_target().backend == "cuda" + + +def is_hip(): + return triton.runtime.driver.active.get_current_target().backend == "hip" + + +def get_cuda_autotune_config(): + return [ + triton.Config({}, num_warps=4, num_stages=1), + triton.Config({}, num_warps=8, num_stages=1), + triton.Config({}, num_warps=16, num_stages=1), + ] + + +def get_hip_autotune_config(): + return [ + triton.Config({'waves_per_eu': 1}, num_warps=4, num_stages=1), + triton.Config({'waves_per_eu': 1}, num_warps=8, num_stages=1), + triton.Config({'waves_per_eu': 1}, num_warps=16, num_stages=1), + triton.Config({'waves_per_eu': 2}, num_warps=4, num_stages=1), + triton.Config({'waves_per_eu': 2}, num_warps=8, num_stages=1), + triton.Config({'waves_per_eu': 2}, num_warps=16, num_stages=1), + triton.Config({'waves_per_eu': 4}, num_warps=4, num_stages=1), + triton.Config({'waves_per_eu': 4}, num_warps=8, num_stages=1), + triton.Config({'waves_per_eu': 4}, num_warps=16, num_stages=1), + ] + + +def get_autotune_config(): + if is_cuda(): + return get_cuda_autotune_config() + else: + return get_hip_autotune_config() + + +@triton.autotune(configs=get_autotune_config(), key=['n_rows', 'n_cols'], use_cuda_graph=True) +@triton.jit +def rms_kernel(output_ptr, input_ptr, g_ptr, input_row_stride, output_row_stride, n_rows, n_cols, eps, + BLOCK_SIZE: tl.constexpr): + row_start = tl.program_id(0) + row_idx = row_start + + #Calculate squared mean by block + row_start_ptr = input_ptr + row_idx * input_row_stride + row_sum = 0.0 + for b in tl.range(0, n_cols, BLOCK_SIZE): + col_offsets = b + tl.arange(0, BLOCK_SIZE) + input_ptrs = row_start_ptr + col_offsets + mask = col_offsets < n_cols + row_block = tl.load(input_ptrs, mask=mask, other=0.0, cache_modifier=".cg") + row_block = row_block * row_block #square every value the block + row_sum += (tl.sum(row_block, axis=-1) / n_cols + ) #tl.sum across row, divide by block_size and add it running sum + + row_norm = row_sum + eps + row_norm = tl.rsqrt(row_norm) + + #Blocked normalization + output_row_start_ptr = output_ptr + row_idx * output_row_stride + for b in tl.range(0, n_cols, BLOCK_SIZE): + col_offsets = b + tl.arange(0, BLOCK_SIZE) + input_ptrs = row_start_ptr + col_offsets + mask = col_offsets < n_cols + row_block = tl.load(input_ptrs, mask=mask, other=0.0, cache_modifier=".cg") #load block of input + g = tl.load(g_ptr + col_offsets, mask=mask, other=0.0, cache_modifier=".cg") #load block of g + output = row_block * row_norm #element wise multiply with rms_norm + output = output * g #element wise multiplication with g + + output_ptrs = output_row_start_ptr + col_offsets + tl.store(output_ptrs, output, mask=mask) + + +def rmsnorm(x, epsilon=1e-6): + n_rows, n_cols = x.shape + #Restricting BLOCK_SIZE to 64Kb is an important optimization. Otherwise, + #performance can drop significantly for larger n_cols. + MAX_FUSED_SIZE = 65536 // x.element_size() + BLOCK_SIZE = min(MAX_FUSED_SIZE, triton.next_power_of_2(n_cols)) + + y = torch.empty_like(x, device='cuda') + g = torch.ones((1, n_cols), device='cuda') + + num_programs = n_rows + grid = lambda meta: (num_programs, ) + rms_kernel[grid](y, x, g, x.stride(0), y.stride(0), n_rows, n_cols, epsilon, BLOCK_SIZE) + + return y + + +def run_rmsnorm(M, N): + print(f"Running RMSNorm for shape ({M}, {N})") + torch.manual_seed(0) + x = torch.randn(M, N, device='cuda') + y_triton = rmsnorm(x) + + return y_triton + + +@pytest.mark.parametrize('M, N', [(1, 4), (2, 10), (8192, 4096), (4096, 8192), (1, 8192), (873, 1245), (1, 98304)]) +def test_rmsnorm(M, N): + torch.manual_seed(0) + x = torch.randn(M, N, device='cuda') + y_triton = rmsnorm(x) + + rms_norm = torch.nn.RMSNorm(N, device='cuda') + y_torch = rms_norm(x) + + print(f"y_triton={y_triton}") + assert torch.allclose(y_triton, y_torch), (y_triton, y_torch) + + +#Benchmark +arg_to_torch_dtype = {'fp16': torch.float16, 'bf16': torch.bfloat16, 'fp32': torch.float32} + + +def torch_rmsnorm(x): + M, N = x.shape + rms_norm = torch.nn.RMSNorm(N, device='cuda') + y_torch = rms_norm(x) + + return y_torch + + +def run_benchmark(args): + config = [] + if (args.M_benchmark): + val = args.M_start + x_vals_list = [] + while val <= args.M_end: + x_vals_list.append(val) + val *= args.M_step + mn_args = {'N': args.N_start} + plot_name = str("rmsnorm-performance_" + args.dtype + "_N" + str(args.N_start) + "_M" + str(args.M_start) + + "-" + str(args.M_end) + "-" + str(args.M_step)) + x_names = ['M'] + else: + x_vals_list = [i for i in range(args.N_start, args.N_end, args.N_step)] + mn_args = {'M': args.M_start} + x_names = ['N'] + plot_name = str("rmsnorm-performance_" + args.dtype + "_M" + str(args.M_start) + "_N" + str(args.N_start) + + "-" + str(args.N_end) + "-" + str(args.N_step)) + + dtype = arg_to_torch_dtype[args.dtype] + + print(plot_name) + config.append( + triton.testing.Benchmark( + x_names=x_names, + x_vals=x_vals_list, + line_arg='provider', + line_vals=['triton', 'torch'], + line_names=["Triton", "Torch"], + styles=[('blue', '-'), ('green', '-')], + ylabel="GB/s", + plot_name=plot_name, + args=mn_args, + )) + + @triton.testing.perf_report(config) + def benchmark(M, N, provider): + x = torch.randn(M, N, device='cuda', dtype=dtype) + stream = torch.cuda.Stream() + torch.cuda.set_stream(stream) + if provider == 'torch': + ms = triton.testing.do_bench(lambda: torch_rmsnorm(x)) + if provider == 'triton': + ms = triton.testing.do_bench(lambda: rmsnorm(x)) + gbps = lambda ms: 2 * x.nelement() * x.element_size() * 1e-9 / (ms * 1e-3) + return gbps(ms) + + benchmark.run(save_path=".", show_plots=True, print_data=True) + + +def parse_args(): + parser = argparse.ArgumentParser( + prog="Benchmark RMSNorm", + allow_abbrev=False, + ) + + parser.add_argument('-M', "--M_start", default="1", type=int) + parser.add_argument('-Ms', "--M_step", default="2", type=int) #This is multiplicative step + parser.add_argument('-Me', "--M_end", default="512", type=int) + parser.add_argument('-Mb', "--M_benchmark", default=False, type=bool) + + parser.add_argument('-N', "--N_start", default="8192", type=int) + parser.add_argument('-Ns', "--N_step", default="1024", type=int) + parser.add_argument('-Ne', "--N_end", default="32768", type=int) + + parser.add_argument('-d', "--dtype", default="fp16") + parser.add_argument('-nb', "--no_benchmark", default=False, type=bool) + + return parser.parse_args() + + +def main(): + args = parse_args() + if args.no_benchmark: + run_rmsnorm(args.M_start, args.N_start) + else: + run_benchmark(args) + + +if __name__ == "__main__": + sys.exit(main()) diff --git a/python/perf-kernels/softmax.py b/python/perf-kernels/softmax.py new file mode 100644 index 000000000000..60eefb91986f --- /dev/null +++ b/python/perf-kernels/softmax.py @@ -0,0 +1,218 @@ +import argparse +import torch +import sys +import pytest + +import triton +import triton.language as tl + + +def is_cuda(): + return triton.runtime.driver.active.get_current_target().backend == "cuda" + + +def is_hip(): + return triton.runtime.driver.active.get_current_target().backend == "hip" + + +def is_cdna(): + return is_hip() and triton.runtime.driver.active.get_current_target().arch in ('gfx940', 'gfx941', 'gfx942', + 'gfx90a', 'gfx908') + + +def get_cuda_autotune_config(): + return [ + triton.Config({}, num_warps=4, num_stages=1), + triton.Config({}, num_warps=8, num_stages=1), + triton.Config({}, num_warps=16, num_stages=1), + ] + + +def get_hip_autotune_config(): + return [ + triton.Config({'waves_per_eu': 1}, num_warps=4, num_stages=1), + triton.Config({'waves_per_eu': 1}, num_warps=8, num_stages=1), + triton.Config({'waves_per_eu': 1}, num_warps=16, num_stages=1), + triton.Config({'waves_per_eu': 2}, num_warps=4, num_stages=1), + triton.Config({'waves_per_eu': 2}, num_warps=8, num_stages=1), + triton.Config({'waves_per_eu': 2}, num_warps=16, num_stages=1), + triton.Config({'waves_per_eu': 4}, num_warps=4, num_stages=1), + triton.Config({'waves_per_eu': 4}, num_warps=8, num_stages=1), + triton.Config({'waves_per_eu': 4}, num_warps=16, num_stages=1), + ] + + +def get_autotune_config(): + if is_cuda(): + return get_cuda_autotune_config() + else: + return get_hip_autotune_config() + + +@triton.autotune(configs=get_autotune_config(), key=['n_rows', 'n_cols'], use_cuda_graph=True) +@triton.jit +def softmax_kernel_online(output_ptr, input_ptr, input_row_stride, output_row_stride, n_rows, n_cols, + BLOCK_SIZE: tl.constexpr): + + row_start = tl.program_id(0) + row_idx = row_start + + #loop 1, find max and sum + m = -float('inf') #Initial value of max + row_sum = 0.0 + row_start_ptr = input_ptr + row_idx * input_row_stride + for b in tl.range(0, n_cols, BLOCK_SIZE): + col_offsets = b + tl.arange(0, BLOCK_SIZE) + input_ptrs = row_start_ptr + col_offsets + mask = col_offsets < n_cols + row_block = tl.load(input_ptrs, mask=mask, other=-float('inf'), cache_modifier=".cg") #load block + m_p = tl.max(row_block, axis=0) #find block max + m_p = tl.maximum(m, m_p) #Find new max across all blocks so far + row_sum = row_sum * tl.exp(m - m_p) #Adjust previous sum + row_sum += tl.sum(tl.exp(row_block - m_p)) #Add to exponentiated sum of this block + m = m_p #save max + + output_row_start_ptr = output_ptr + row_idx * output_row_stride + #Loop 2 + for b in tl.range(0, n_cols, BLOCK_SIZE): + col_offsets = b + tl.arange(0, BLOCK_SIZE) + input_ptrs = row_start_ptr + col_offsets + mask = col_offsets < n_cols + row_block = tl.load(input_ptrs, mask=mask, other=-float('inf'), cache_modifier=".cg") #load block + #subtract, exponentiate and divide by sum + softmax_output = tl.exp(row_block - m) / row_sum + #store + output_ptrs = output_row_start_ptr + col_offsets + tl.store(output_ptrs, softmax_output, mask=mask) + + +def softmax(x): + n_rows, n_cols = x.shape + + MAX_FUSED_SIZE = 65536 // x.element_size() + BLOCK_SIZE = min(MAX_FUSED_SIZE, triton.next_power_of_2(n_cols)) + y = torch.empty_like(x) + + num_programs = n_rows + + grid = lambda meta: (num_programs, ) + softmax_kernel_online[grid]( + y, + x, + x.stride(0), + y.stride(0), + n_rows, + n_cols, + BLOCK_SIZE, + ) + + return y + + +def run_softmax(M, N): + print(f"Running Softmax on shape ({M},{N})") + torch.manual_seed(0) + x = torch.randn(M, N, device='cuda') + y_triton = softmax(x) + + return y_triton + + +#pytest +@pytest.mark.parametrize('M, N', [(1823, 781), (1, 1), (128, 1), (1, 128), (8192, 8192), (4096, 8192), (359, 1), + (1, 359), (1, 131072), (1, 89999)]) +def test_softmax(M, N): + torch.manual_seed(0) + x = torch.randn(M, N, device='cuda') + y_triton = softmax(x) + y_torch = torch.softmax(x, axis=1) + assert torch.allclose(y_triton, y_torch), (y_triton, y_torch) + + +#Benchmark +arg_to_torch_dtype = {'fp16': torch.float16, 'bf16': torch.bfloat16, 'fp32': torch.float32} + + +def run_benchmark(args): + config = [] + if (args.M_benchmark): + val = args.M_start + x_vals_list = [] + while val <= args.M_end: + x_vals_list.append(val) + val *= args.M_step + mn_args = {'N': args.N_start} + plot_name = str("softmax-performance_" + args.dtype + "_N" + str(args.N_start) + "_M" + str(args.M_start) + + "-" + str(args.M_end) + "-" + str(args.M_step)) + x_names = ['M'] + else: + x_vals_list = [i for i in range(args.N_start, args.N_end, args.N_step)] + mn_args = {'M': args.M_start} + plot_name = str("softmax-performance_" + args.dtype + "_M" + str(args.M_start) + "_N" + str(args.N_start) + + "-" + str(args.N_end) + "-" + str(args.N_step)) + x_names = ['N'] + dtype = arg_to_torch_dtype[args.dtype] + + print(plot_name) + config.append( + triton.testing.Benchmark( + x_names=x_names, + x_vals=x_vals_list, + line_arg='provider', + line_vals=['triton', 'torch'], + line_names=[ + "Triton", + "Torch", + ], + styles=[('blue', '-'), ('green', '-')], + ylabel="GB/s", + plot_name=plot_name, + args=mn_args, + )) + + @triton.testing.perf_report(config) + def benchmark(M, N, provider): + x = torch.randn(M, N, device='cuda', dtype=dtype) + stream = torch.cuda.Stream() + torch.cuda.set_stream(stream) + if provider == 'torch': + ms = triton.testing.do_bench(lambda: torch.softmax(x, axis=-1)) + if provider == 'triton': + ms = triton.testing.do_bench(lambda: softmax(x)) + gbps = lambda ms: 2 * x.nelement() * x.element_size() * 1e-9 / (ms * 1e-3) + return gbps(ms) + + benchmark.run(save_path=".", show_plots=True, print_data=True) + + +def parse_args(): + parser = argparse.ArgumentParser( + prog="Benchmark Softmax", + allow_abbrev=False, + ) + + parser.add_argument('-M', "--M_start", default="1", type=int) + parser.add_argument('-Ms', "--M_step", default="2", type=int) + parser.add_argument('-Me', "--M_end", default="512", type=int) + parser.add_argument('-Mb', "--M_benchmark", default=False, type=bool) + + parser.add_argument('-N', "--N_start", default="1024", type=int) + parser.add_argument('-Ns', "--N_step", default="2048", type=int) + parser.add_argument('-Ne', "--N_end", default="65536", type=int) + + parser.add_argument('-d', "--dtype", default="fp16") + parser.add_argument('-nb', "--no_benchmark", default=False, type=bool) + + return parser.parse_args() + + +def main(): + args = parse_args() + if args.no_benchmark: + run_softmax(args.M_start, args.N_start) + else: + run_benchmark(args) + + +if __name__ == "__main__": + sys.exit(main()) diff --git a/python/perf-kernels/streamk/README.md b/python/perf-kernels/streamk/README.md new file mode 100644 index 000000000000..aa0b11d41b73 --- /dev/null +++ b/python/perf-kernels/streamk/README.md @@ -0,0 +1,43 @@ +# streamk gemm script v0.1 + +The plan is to use this version as the base version for the future triton streamk gemm development. + +### Main features +- comparable performance with tune gemm + +- use the persistent loop so that a WG may work on multiple output tiles, and also allowing workgroups to do part of the work for an output tile. + +- use atomics for spinning lock to replace atomic_add for the final output. + +- pid renumbering based on chiplet structure of MI300X + +- dynamic grid setting + +- tuning script adapt from tune_gemm + +### Usage + +Go to the script dir +```bash +cd triton/python/perf_kernels/streamk +``` + +1. Tune gemm sizes given in a yaml file and check correctness on the way +```bash +python tune_streamk.py --gemm_size_file input_gemm_sizes.yaml --compare +``` + +2. Tune a single gemm size +```bash +python tune_streamk.py -m 16 -n 16 -k 16 +``` + +3. Choose the file to store tuning results +```bash +python tune_streamk.py --gemm_size_file input_gemm_sizes.yaml --o output_tuning.yaml +``` + +4. Only check correctness given the tuning results +```bash +python tune_streamk.py --gemm_size_file output_tuning.yaml --compare_wo_tuning +``` diff --git a/python/perf-kernels/streamk/streamk_kernel.py b/python/perf-kernels/streamk/streamk_kernel.py new file mode 100644 index 000000000000..42b861950a9b --- /dev/null +++ b/python/perf-kernels/streamk/streamk_kernel.py @@ -0,0 +1,207 @@ +import triton +import triton.language as tl + + +@triton.jit() +def get_new_pid(current_pid, num_cus): + # Number of XCDs + num_xcds = 8 + # Number of pids per XCD in the new arrangement + pids_per_xcd = num_cus // num_xcds + # Compute current XCD and local pid within the XCD + xcd = current_pid % num_xcds + local_pid = current_pid // num_xcds + + # Calculate new pid based on the new grouping + new_pid = xcd * pids_per_xcd + local_pid + return new_pid + + +@triton.jit() +def get_tiles_config( + M, + N, + K, + num_cus, + BLOCK_SIZE_M: tl.constexpr, + BLOCK_SIZE_N: tl.constexpr, + BLOCK_SIZE_K: tl.constexpr, +): + total_blocks_M = tl.cdiv(M, BLOCK_SIZE_M) + total_blocks_N = tl.cdiv(N, BLOCK_SIZE_N) + iters_per_tile = tl.cdiv(K, BLOCK_SIZE_K) + + total_tiles = total_blocks_M * total_blocks_N + if num_cus > 0 and total_tiles > num_cus: # Stream-K + total_streamk_tiles = total_tiles % num_cus + total_full_tiles = total_tiles - total_streamk_tiles + total_streamk_iters = total_streamk_tiles * iters_per_tile + # iterations related to full waves + streamk_iters_pcu = total_streamk_iters // num_cus + # iterations related to last (partial) wave + streamk_remainder_iters = total_streamk_iters % num_cus + + else: # all tiles are computed using classical blocking + total_full_tiles = total_tiles + total_streamk_tiles = 0 + streamk_iters_pcu = 0 + streamk_remainder_iters = 0 + total_streamk_iters = 0 + + return iters_per_tile, total_full_tiles, total_streamk_tiles, streamk_iters_pcu, streamk_remainder_iters + + +@triton.jit() +def streamk_gemm( + A, + B, + C, + P, + locks, + M, + N, + K, + num_cus, + stride_am, + stride_ak, + stride_bk, + stride_bn, + stride_cm, + stride_cn, + BLOCK_SIZE_M: tl.constexpr, + BLOCK_SIZE_N: tl.constexpr, + BLOCK_SIZE_K: tl.constexpr, + GROUP_SIZE_M: tl.constexpr, + EVEN_K: tl.constexpr, +): + pid = tl.program_id(0) + pid = get_new_pid(pid, num_cus) + num_pid_m = tl.cdiv(M, BLOCK_SIZE_M) + num_pid_n = tl.cdiv(N, BLOCK_SIZE_N) + + iters_per_tile, total_full_tiles, total_streamk_tiles, streamk_iters_pcu, streamk_remainder_iters = get_tiles_config( + M, N, K, num_cus, BLOCK_SIZE_M, BLOCK_SIZE_N, BLOCK_SIZE_K) + + acc_dtype = tl.float32 if C.type.element_ty != tl.int8 else tl.int32 + rk = tl.arange(0, BLOCK_SIZE_K) + + for tile_id in range(pid, total_full_tiles, num_cus): + if GROUP_SIZE_M == 1: + pid_m = tile_id // num_pid_n + pid_n = tile_id % num_pid_n + else: + num_pid_in_group = GROUP_SIZE_M * num_pid_n + group_id = tile_id // num_pid_in_group + first_pid_m = group_id * GROUP_SIZE_M + group_size_m = min(num_pid_m - first_pid_m, GROUP_SIZE_M) + pid_m = first_pid_m + (tile_id % group_size_m) + pid_n = (tile_id % num_pid_in_group) // group_size_m + + rm = (pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M)) % M + rn = (pid_n * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N)) % N + rm = tl.max_contiguous(tl.multiple_of(rm, BLOCK_SIZE_M), BLOCK_SIZE_M) + rn = tl.max_contiguous(tl.multiple_of(rn, BLOCK_SIZE_N), BLOCK_SIZE_N) + A_BASE = A + rm[:, None] * stride_am + rk[None, :] * stride_ak + B_BASE = B + rk[:, None] * stride_bk + rn[None, :] * stride_bn + + acc = tl.zeros((BLOCK_SIZE_M, BLOCK_SIZE_N), dtype=acc_dtype) + for k in range(0, tl.cdiv(K, BLOCK_SIZE_K)): + if EVEN_K: + a = tl.load(A_BASE) + b = tl.load(B_BASE) + else: + a = tl.load(A_BASE, mask=rk[None, :] < K - k * BLOCK_SIZE_K, other=0.0) + b = tl.load(B_BASE, mask=rk[:, None] < K - k * BLOCK_SIZE_K, other=0.0) + acc += tl.dot(a, b) + A_BASE += BLOCK_SIZE_K * stride_ak + B_BASE += BLOCK_SIZE_K * stride_bk + + c = acc.to(C.type.element_ty) + + rm = pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M) + rn = pid_n * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N) + C_ = C + rm[:, None] * stride_cm + rn[None, :] * stride_cn + mask = (rm < M)[:, None] & (rn < N)[None, :] + tl.store(C_, c, mask=mask) + + start_iter = total_full_tiles * iters_per_tile + pid * streamk_iters_pcu + tl.minimum(pid, streamk_remainder_iters) + last_iter = total_full_tiles * iters_per_tile + (pid + 1) * streamk_iters_pcu + tl.minimum( + pid + 1, streamk_remainder_iters) + while start_iter < last_iter: + remainder = start_iter % iters_per_tile + end_iter = tl.minimum(start_iter + (iters_per_tile - remainder), last_iter) + # where are we in the grid + tile_id = start_iter // iters_per_tile + if GROUP_SIZE_M == 1: + pid_m = tile_id // num_pid_n + pid_n = tile_id % num_pid_n + else: + num_pid_in_group = GROUP_SIZE_M * num_pid_n + group_id = tile_id // num_pid_in_group + first_pid_m = group_id * GROUP_SIZE_M + group_size_m = min(num_pid_m - first_pid_m, GROUP_SIZE_M) + pid_m = first_pid_m + (tile_id % group_size_m) + pid_n = (tile_id % num_pid_in_group) // group_size_m + + rm = (pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M)) % M + rn = (pid_n * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N)) % N + rm = tl.max_contiguous(tl.multiple_of(rm, BLOCK_SIZE_M), BLOCK_SIZE_M) + rn = tl.max_contiguous(tl.multiple_of(rn, BLOCK_SIZE_N), BLOCK_SIZE_N) + # rk = tl.arange(0, BLOCK_SIZE_K) + A_BASE = A + rm[:, None] * stride_am + rk[None, :] * stride_ak + BLOCK_SIZE_K * stride_ak * remainder + B_BASE = B + rk[:, None] * stride_bk + rn[None, :] * stride_bn + BLOCK_SIZE_K * stride_bk * remainder + + acc = tl.zeros((BLOCK_SIZE_M, BLOCK_SIZE_N), dtype=acc_dtype) + for current_iter in range(start_iter, end_iter): + if EVEN_K: + a = tl.load(A_BASE) + b = tl.load(B_BASE) + else: + global_k_offset = (current_iter % iters_per_tile) * BLOCK_SIZE_K + k_mask = global_k_offset + rk < K + a = tl.load(A_BASE, mask=k_mask[None, :], other=0.0) + b = tl.load(B_BASE, mask=k_mask[:, None], other=0.0) + acc += tl.dot(a, b) + A_BASE += BLOCK_SIZE_K * stride_ak + B_BASE += BLOCK_SIZE_K * stride_bk + + tile_iter = tile_id * iters_per_tile + if start_iter == tile_iter: + tile_iter_end = tile_iter + iters_per_tile + next_pid = pid + 1 + end = end_iter + while (end < tile_iter_end and next_pid < num_cus): + # todo: try use tl.load once cache modifier landed upstream + while tl.atomic_cas(locks + next_pid, 1, 1) != 1: + pass + rm1 = tl.arange(0, BLOCK_SIZE_M) + rn1 = tl.arange(0, BLOCK_SIZE_N) + rm1 = tl.max_contiguous(tl.multiple_of(rm1, BLOCK_SIZE_M), BLOCK_SIZE_M) + rn1 = tl.max_contiguous(tl.multiple_of(rn1, BLOCK_SIZE_N), BLOCK_SIZE_N) + P_ = P + next_pid * BLOCK_SIZE_M * BLOCK_SIZE_N + rm1[:, None] * BLOCK_SIZE_N + rn1[None, :] + acc += tl.load(P_) + end += streamk_iters_pcu + (next_pid < streamk_remainder_iters) + + next_pid += 1 + + c = acc.to(C.type.element_ty) + + rm = (pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M)) % M + rn = (pid_n * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N)) % N + rm = tl.max_contiguous(tl.multiple_of(rm, BLOCK_SIZE_M), BLOCK_SIZE_M) + rn = tl.max_contiguous(tl.multiple_of(rn, BLOCK_SIZE_N), BLOCK_SIZE_N) + C_ = C + rm[:, None] * stride_cm + rn[None, :] * stride_cn + mask = (rm < M)[:, None] & (rn < N)[None, :] + tl.store(C_, c, mask=mask) + + else: + rm1 = tl.arange(0, BLOCK_SIZE_M) + rn1 = tl.arange(0, BLOCK_SIZE_N) + rm1 = tl.max_contiguous(tl.multiple_of(rm1, BLOCK_SIZE_M), BLOCK_SIZE_M) + rn1 = tl.max_contiguous(tl.multiple_of(rn1, BLOCK_SIZE_N), BLOCK_SIZE_N) + P_ = P + pid * BLOCK_SIZE_M * BLOCK_SIZE_N + rm1[:, None] * BLOCK_SIZE_N + rn1[None, :] + tl.store(P_, acc) + tl.debug_barrier() + tl.atomic_xchg(locks + pid, 1) + + start_iter = end_iter diff --git a/python/perf-kernels/streamk/tune_streamk.py b/python/perf-kernels/streamk/tune_streamk.py new file mode 100644 index 000000000000..3b0fbdb960c7 --- /dev/null +++ b/python/perf-kernels/streamk/tune_streamk.py @@ -0,0 +1,847 @@ +# fp8 +import argparse +import sys +import yaml +import os +import glob +import subprocess + +import torch +import triton +import triton.language as tl + +from streamk_kernel import streamk_gemm + +from datetime import datetime +import multiprocessing +import pandas as pd + +device_oi = 650. / 3.0 + + +def get_full_tuning_space(): + configs = [] + + block_mn_range = [16, 32, 64, 128, 256] + block_k_range = [16, 32, 64, 128, 256] + num_warps_range = [1, 2, 4, 8] + group_m_range = [1, 4, 8, 16, 32] + # For now we see better perf with num_stages=0 for all gemm configs we care + # But keep this explicit so that we do not forget we may need to set it to + # other values in the future + num_stage_range = [0] + waves_per_eu_range = [0] + matrix_instr_nonkdim_range = [16, 32] + kpack_range = [1, 2] + + for block_m in block_mn_range: + for block_n in block_mn_range: + for block_k in block_k_range: + for num_warps in num_warps_range: + for group_m in group_m_range: + for num_stages in num_stage_range: + for waves_per_eu in waves_per_eu_range: + for matrix_instr_nonkdim in matrix_instr_nonkdim_range: + for kpack in kpack_range: + configs.append({ + 'BLOCK_SIZE_M': block_m, 'BLOCK_SIZE_N': block_n, 'BLOCK_SIZE_K': block_k, + 'GROUP_SIZE_M': group_m, 'num_warps': num_warps, 'num_stages': num_stages, + 'waves_per_eu': waves_per_eu, 'matrix_instr_nonkdim': matrix_instr_nonkdim, + 'kpack': kpack + }) + + return configs + + +def get_gemm_oi(M, N, K): + FLOPs = 2 * M * N * K + # 4 for fp32 + # to do check dtype for bytesmoved + bytesmoved = (M * K + K * N + 2 * M * N) * 4 + return FLOPs / bytesmoved + + +def prune_configs(M, N, K, configs, elemBytes_a, elemBytes_b): + pruned_configs = [] + + if M < 32 or N < 32: + mfma = 16 + else: + mfma = 32 + + # TODO (zhanglx): figure out the boundary between large and small gemms + large_gemm = False + if M >= 2048 and N >= 2048: + large_gemm = True + + for config in configs: + BLOCK_SIZE_M = config.get("BLOCK_SIZE_M") + BLOCK_SIZE_N = config.get("BLOCK_SIZE_N") + BLOCK_SIZE_K = config.get("BLOCK_SIZE_K") + num_warps = config.get("num_warps") + matrix_instr_nonkdim = config.get("matrix_instr_nonkdim") + kpack = config.get("kpack") + if matrix_instr_nonkdim > mfma: + continue + if mfma == 4 and BLOCK_SIZE_K < 64: + continue + # some layouts could not work properly in case + # number elemens per thread is less 1 + if BLOCK_SIZE_M * BLOCK_SIZE_N < 64: + continue + GROUP_M = config.get("GROUP_SIZE_M") + if BLOCK_SIZE_M < matrix_instr_nonkdim or BLOCK_SIZE_N < matrix_instr_nonkdim: + continue + if BLOCK_SIZE_K == 16 and matrix_instr_nonkdim == 16 and kpack == 2: + continue + if M <= matrix_instr_nonkdim and BLOCK_SIZE_M != matrix_instr_nonkdim: + continue + if N <= matrix_instr_nonkdim and BLOCK_SIZE_N != matrix_instr_nonkdim: + continue + # Skip BLOCK_SIZE that is too large compare to M/N + # unless BLOCK_SIZE is already small enough + if BLOCK_SIZE_M > M * 2 and BLOCK_SIZE_M != 16: + continue + if BLOCK_SIZE_N > N * 2 and BLOCK_SIZE_N != 16: + continue + # skip large GROUP_M + if GROUP_M * BLOCK_SIZE_M > M and GROUP_M != 1: + continue + # out of shared memory resource + # TODO (zhanglx): This does not consider the LDS usage in the epilogue + LDS = BLOCK_SIZE_K * BLOCK_SIZE_M * elemBytes_a + BLOCK_SIZE_K * BLOCK_SIZE_N * elemBytes_b + if LDS > 65536: + continue + # Skip small block sizes and num_warps for large gemm + # For fp16 and f8, we want to only use BLOCK_SIZE >= 64 + if large_gemm: + if BLOCK_SIZE_M < 64 or BLOCK_SIZE_N < 64: + continue + if BLOCK_SIZE_K < 64: + continue + if num_warps < 4: + continue + + pruned_configs.append(config) + + return pruned_configs + + +def run_bash_command_wrapper(commandstring, capture=True): + try: + run_bash_command(commandstring, capture) + except subprocess.CalledProcessError: + if not capture: + print(f"running {commandstring} one more time") + run_bash_command(commandstring, capture) + + +def run_bash_command(commandstring, capture=True): + if capture: + proc = subprocess.run(commandstring, shell=True, check=True, executable='/bin/bash', stdout=subprocess.PIPE) + return proc.stdout.splitlines() + proc = subprocess.run(commandstring, shell=True, check=True, executable='/bin/bash') + return None + + +def read_config(config): + block_m = config.get('BLOCK_SIZE_M') + block_n = config.get('BLOCK_SIZE_N') + block_k = config.get('BLOCK_SIZE_K') + group_m = config.get('GROUP_SIZE_M') + num_warps = config.get('num_warps') + num_stages = config.get('num_stages') + waves_per_eu = config.get('waves_per_eu') + mfma_instr_size = config.get('matrix_instr_nonkdim') + kpack = config.get('kpack') + return block_m, block_n, block_k, group_m, num_warps, num_stages, waves_per_eu, mfma_instr_size, kpack + + +def gen_kernel_and_configStr_from_config(M, N, K, num_cus, EVEN_K, config, dtype_a, dtype_b, dtype_c, dtype_p, + dtype_lock): + block_m, block_n, block_k, group_m, num_warps, num_stages, waves_per_eu, mfmaInstrSize, kpack = read_config(config) + torch_dtype_a = 'fp16' + torch_dtype_b = 'fp16' + torch_dtype_c = 'fp16' + torch_dtype_p = 'fp32' + torch_dtype_lock = 'int32' + if dtype_a: + torch_dtype_a = tl_to_torch_types[name_to_tl_types[dtype_a]] + if dtype_b: + torch_dtype_b = tl_to_torch_types[name_to_tl_types[dtype_b]] + if dtype_c: + torch_dtype_c = tl_to_torch_types[name_to_tl_types[dtype_c]] + if dtype_p: + torch_dtype_p = tl_to_torch_types[name_to_tl_types[dtype_p]] + if dtype_lock: + torch_dtype_lock = tl_to_torch_types[name_to_tl_types[dtype_lock]] + configStr = f"M{M}_N{N}_K{K}_BM{block_m}_BN{block_n}_BK{block_k}_GM{group_m}_nW{num_warps}_nS{num_stages}_EU{waves_per_eu}_kP{kpack}_mfma{mfmaInstrSize}" + + matmul_def_str = f""" +def matmul_{configStr}(a, b, c, P, locks, M, N, K, num_cus, am, ak, bk, bn, cm, cn, warmup=False): + grid = num_cus + #print(f'config: streamk_gemm_{configStr}', flush=True) + if warmup: + streamk_gemm_{configStr}.warmup( + {torch_dtype_a}, {torch_dtype_b}, {torch_dtype_c}, {torch_dtype_p}, {torch_dtype_lock}, + M, N, K, num_cus, + am, ak, bk, bn, cm, cn, + BLOCK_SIZE_M = {block_m}, + BLOCK_SIZE_N = {block_n}, + BLOCK_SIZE_K = {block_k}, + GROUP_SIZE_M = {group_m}, + num_warps = {num_warps}, + num_stages = {num_stages}, + waves_per_eu = {waves_per_eu}, + matrix_instr_nonkdim = {mfmaInstrSize}, + kpack = {kpack}, + EVEN_K = {EVEN_K}, + grid=(1,) + ) + return None + else: + streamk_gemm_{configStr}[grid,]( + a, b, c, P, locks, + M, N, K, num_cus, + am, ak, bk, bn, cm, cn, + BLOCK_SIZE_M = {block_m}, + BLOCK_SIZE_N = {block_n}, + BLOCK_SIZE_K = {block_k}, + GROUP_SIZE_M = {group_m}, + num_warps = {num_warps}, + num_stages = {num_stages}, + waves_per_eu = {waves_per_eu}, + matrix_instr_nonkdim = {mfmaInstrSize}, + kpack = {kpack}, + EVEN_K = {EVEN_K} + ) + return c + +def try_config_{configStr}(M, N, K, num_cus, am, ak, bk, bn, cm, cn): + try: + matmul_{configStr}(None, None, None, None, None, M, N, K, num_cus, am, ak, bk, bn, cm, cn, True) + return True + except Exception as e: + print(f'invalid config(compilation): {configStr}: ', e, flush=True) + return False +""" + return configStr, matmul_def_str + + +def generated_kernel_name(M, N, K, gpu_id): + return f"generated_kernel{M}-{N}-{K}-{gpu_id}.py" + + +# Open {len(gpus)} files +# generated_kernelM-N-K-{gpus[0]}.py, generated_kernelM-N-K-{gpus[1]}.py, ..., generated_kernelM-N-K-{gpus[-1]}.py +# and generate +# 1. matmul kernels of all configs +# 2. wrapper function matmul to invoke all the generated kernels +# 3. Another wraper function try_config to invoke matmul function +# 4. test_gemm to invoke +# 4.1 run try_config in parallel +# 4.2 matmul in a loop of 10 iterations +def generate_kernel(M, N, K, num_cus, col_a, col_b, dtype_a, dtype_b, dtype_c, dtype_p, dtype_lock, init_type, configs, + jobs, iters, run_bench): + filenames = [] + for i in range(jobs): + filenames.append(generated_kernel_name(M, N, K, i)) + f_kernel = [open(path, 'w') for path in filenames] + + # write imports + import_str = """import torch +import triton +import triton.language as tl +import argparse +import sys +import multiprocessing +from tune_streamk import gen_input +""" + for fi in range(jobs): + f_kernel[fi].write(import_str + "\n") + + # write definitions of streamk_gemm_xxx + # and matmul_xxx and try_config + with open("streamk_kernel.py") as file: + streamk_gemm_code = file.read() + idx = 0 + for config in configs: + file_idx = idx % jobs + EVEN_K = True if K % config.get('BLOCK_SIZE_K') == 0 else False + configStr, matmul_def_str = gen_kernel_and_configStr_from_config(M, N, K, num_cus, EVEN_K, config, dtype_a, + dtype_b, dtype_c, dtype_p, dtype_lock) + # Copy the streamk_gemm with name replaced + streamk_gemm_config = streamk_gemm_code.replace("streamk_gemm", f"streamk_gemm_{configStr}") + streamk_gemm_config = streamk_gemm_config.replace("import triton.language as tl", "") + streamk_gemm_config = streamk_gemm_config.replace("import triton", "") + f_kernel[file_idx].write(streamk_gemm_config + "\n\n") + f_kernel[file_idx].write(matmul_def_str + "\n") + idx += 1 + + # write test_gemm + # pre string + block_m = config.get('BLOCK_SIZE_M') + block_n = config.get('BLOCK_SIZE_N') + test_gemm_pre_str = f"""def test_gemm(M, N, K, num_cus, num_threads): + thread_pool = multiprocessing.Pool(processes=num_threads) + a, a_fp16 = gen_input(M, K, '{dtype_a}', {col_a}, 1, '{init_type}', device='cuda') + b, b_fp16 = gen_input(K, N, '{dtype_b}', {col_b}, 2, '{init_type}', device='cuda') + c = torch.zeros((M, N), device=a.device, dtype={tl_to_torch_types[name_to_tl_types[dtype_c]]}) + task_args = (M, N, K, num_cus, + a.stride(0), a.stride(1), + b.stride(0), b.stride(1), + c.stride(0), c.stride(1)) + + if num_threads > 1: + results = [] + config_names = [] +""" + for fi in range(jobs): + f_kernel[fi].write(test_gemm_pre_str + "\n") + + # warm up call of all matmul functions in parallel + idx = 0 + for config in configs: + EVEN_K = True if K % config.get('BLOCK_SIZE_K') == 0 else False + configStr, _ = gen_kernel_and_configStr_from_config(M, N, K, num_cus, EVEN_K, config, None, None, None, None, + None) + task_str = f" results += [thread_pool.apply_async(try_config_{configStr}, args=task_args)]\n" + \ + f" config_names += ['{configStr}']\n" + f_kernel[idx % jobs].write(task_str) + idx += 1 + + for fi in range(jobs): + threadpool_str = """ + failed_configs = [] + for i in range(len(results)): + results[i].wait() + res = results[i].get() + if not res: + failed_configs += [config_names[i]] + thread_pool.close() + thread_pool.join() + with open("{filename}.failed_configs", "w") as f: + for cfg in failed_configs: + f.write(cfg + "\\n") + else: + try: + with open("{filename}.failed_configs", "r") as f: + failed_configs = [cfg.strip() for cfg in f.readlines()] + except Exception: + failed_configs = [] + """.format(filename=filenames[fi]) + f_kernel[fi].write(threadpool_str) + # call all matmul_xxx functions + idx = 0 + runs = iters if run_bench else 200 + for config in configs: + EVEN_K = True if K % config.get('BLOCK_SIZE_K') == 0 else False + configStr, _ = gen_kernel_and_configStr_from_config(M, N, K, num_cus, EVEN_K, config, None, None, None, None, + None) + block_m = config.get('BLOCK_SIZE_M') + block_n = config.get('BLOCK_SIZE_N') + matmul_call_str = f""" + if '{configStr}' not in failed_configs: + print(f"{configStr}") + for i in range({runs}): + locks = torch.zeros((num_cus,), device = "cuda", dtype = torch.int32) + P = torch.zeros((num_cus, {block_m}*{block_n}), device="cuda", dtype=torch.float32) + d = matmul_{configStr}(a, b, c, P, locks, M, N, K, num_cus, a.stride(0), a.stride(1), b.stride(0), b.stride(1), c.stride(0), c.stride(1))""" + f_kernel[idx % jobs].write(matmul_call_str + "\n") + idx += 1 + # post string + for fi in range(jobs): + f_kernel[fi].write(" return d\n") + + # def main and call test_gemm + def_main_str = """ +def main(): + parser = argparse.ArgumentParser( + prog="tune a specific gemm size", + allow_abbrev=False,) + parser.add_argument("-n", type=int, default=1, help='number of threads') + args = parser.parse_args() + numThreads = args.n + num_cus = 304 + """ + test_gemm_call_str = f'test_gemm({M}, {N}, {K}, num_cus, numThreads)' + for fi in range(jobs): + f_kernel[fi].write(def_main_str) + f_kernel[fi].write(test_gemm_call_str + "\n\n") + f_kernel[fi].write("""if __name__ == '__main__': + sys.exit(main())""") + f_kernel[fi].close() + + +def extract_kernel_time(M, N, K, num_cus, EVEN_K, config, df): + # Correct the header by removing 'sig' and 'obj' to reduce number from 21 to 19 + # once the bug is fixed, we should not need below two lines + cols = [ + 'Index', 'KernelName', 'gpu-id', 'queue-id', 'queue-index', 'pid', 'tid', 'grd', 'wgr', 'lds', 'scr', + 'arch_vgpr', 'accum_vgpr', 'sgpr', 'wave_size', 'DispatchNs', 'BeginNs', 'EndNs', 'CompleteNs' + ] + df.columns = cols + + configStr, _ = gen_kernel_and_configStr_from_config(M, N, K, num_cus, EVEN_K, config, None, None, None, None, None) + + filtered_df = df[df['KernelName'].str.contains(configStr, na=False)].copy() + filtered_df['DurationNs'] = filtered_df['EndNs'] - filtered_df['BeginNs'] + meanTime = filtered_df['DurationNs'].tail(100).mean() + return config, meanTime + + +def profile_batch_kernels(M, N, K, num_cus, gpuid, gpus, jobs, verbose): + ngpus = len(gpus) + gpuIdx = gpus.index(gpuid) + if gpuIdx + 1 > jobs: + return + os.environ['ROCR_VISIBLE_DEVICES'] = str(gpuid) + jobId = gpuIdx + while jobId < jobs: + if verbose: + print(f"profiling {generated_kernel_name(M, N, K, jobId)} on GPU {gpuid}") + run_bash_command_wrapper( + f"rocprofv2 --plugin file --plugin-version 1 --kernel-trace -o {jobId} python {generated_kernel_name(M, N, K, jobId)}", + capture=(verbose < 2)) + jobId += ngpus + + +def tune_gemm_config(M, N, K, num_cus, col_a, col_b, dtype_a, dtype_b, dtype_c, dtype_p, dtype_lock, init_type, configs, + run_bench, jobs, iters, skipWarmup, verbose=0, num_threads=16, gpus=[0]): + # Generate kernel out of all configs + generate_kernel(M, N, K, num_cus, col_a, col_b, dtype_a, dtype_b, dtype_c, dtype_p, dtype_lock, init_type, configs, + jobs, iters, run_bench) + + # remove any compiled kernel in the cache + run_bash_command("rm -rf ~/.triton/cache") + + # precompile the kernels in parallel + start_time = datetime.now() + if not skipWarmup: + for i in range(jobs): + run_bash_command(f"python {generated_kernel_name(M, N, K, i)} -n {num_threads}", capture=(verbose < 2)) + compile_end = datetime.now() + compile_time = compile_end - start_time + if verbose: + print(f"compile time: {compile_time}", flush=True) + + # profile generated kernels + running = [ + multiprocessing.Process(target=profile_batch_kernels, args=(M, N, K, num_cus, gpu_id, gpus, jobs, verbose)) + for gpu_id in gpus + ] + for p in running: + p.start() + for p in running: + p.join() + + profile_end = datetime.now() + profile_time = profile_end - compile_end + if verbose: + print(f"profile time: {profile_time}", flush=True) + + # post process results.csv to get the best config and minTime + # TODO: process the file in parallel + minTime = 1024 * 1024 * 1024 + thread_pool = multiprocessing.Pool(processes=num_threads) + tasks = [] + idx = 0 + df_prof = [ + pd.read_csv(f"results_{i}.csv", skiprows=1, header=None, delimiter=',', quotechar='"', escapechar='\\') + for i in range(jobs) + ] + for config in configs: + EVEN_K = True if K % config.get('BLOCK_SIZE_K') == 0 else False + file_idx = idx % jobs + tasks += [ + thread_pool.apply_async(extract_kernel_time, args=(M, N, K, num_cus, EVEN_K, config, df_prof[file_idx])) + ] + idx += 1 + thread_pool.close() + thread_pool.join() + + for task in tasks: + config, myTime = task.get() + if myTime: + min_us = myTime / 1000 + if min_us < minTime: + minTime = min_us + bestConfig = config + else: + min_us = -1 + print(f"invalid config(post processing): SIZE {M} {N} {K}: {config}", flush=True) + post_end = datetime.now() + post_time = post_end - profile_end + if verbose: + print(f"post procesing time: {post_time}", flush=True) + return minTime, bestConfig, compile_time, profile_time, post_time + + +def gen_input(M, N, ty_name, needTrans, seed, init_type, device='cuda'): + d_type = name_to_tl_types[ty_name] + torch.manual_seed(seed) + torch.cuda.manual_seed(seed) + + @triton.jit + def copy_kernel(input_ptr, output_ptr, n_elements, BLOCK_SIZE: tl.constexpr): + offsets = tl.program_id(axis=0) * BLOCK_SIZE + tl.arange(0, BLOCK_SIZE) + mask = offsets < n_elements + input = tl.load(input_ptr + offsets, mask=mask) + output = input + tl.store(output_ptr + offsets, output, mask=mask) + + def init_by_size_and_type(size, dtype, init_type): + if init_type == 'hpl': + return torch.empty(size, device='cuda', dtype=dtype).uniform_(-0.5, 0.5) + # This init type has element[i] in row[j] equal to sin(i+j*N) + elif init_type == 'trig_float': + M, N = size + return torch.reshape(torch.arange(0, M * N), (M, N)).sin().to(dtype=dtype, device='cuda') + elif init_type == 'zeros': + return torch.zeros(size, dtype=dtype, device='cuda') + elif init_type == "randn": + temp = torch.randn(size, dtype=dtype, device='cuda') + return temp + else: + raise ValueError("Bad matrix initialization type.") + + raw_data = init_by_size_and_type((N, M) if needTrans else (M, N), torch.float32, init_type) + if needTrans: + raw_data = raw_data.T + if (d_type == tl.float8e4b8 and TORCH_HAS_FP8E4B8) or \ + (d_type == tl.float8e5b16 and TORCH_HAS_FP8E5B16) or not d_type.is_fp8(): + input = raw_data.to(tl_to_torch_types[d_type]) + input_f16 = input.to(torch.float16) + else: + f8_tensor = raw_data.to(torch.int8) + # keep only two bits of exponent to avoid overflow + f8_tensor = f8_tensor & 0b00111111 + input = triton.reinterpret(f8_tensor, d_type) + input_f16 = torch.empty_like(f8_tensor, dtype=torch.float16) + grid = lambda meta: (triton.cdiv(n_elements, meta['BLOCK_SIZE']), ) + n_elements = raw_data.numel() + copy_kernel[grid](input, input_f16, n_elements, BLOCK_SIZE=1024) + + return input, input_f16 + + +def matmul(a, b, c, P, locks, num_cus, block_m, block_n, block_k, group_m, num_warps, num_stages, waves_per_eu, + mfmaInstrSize, kpack, EVEN_K): + # Check constraints. + assert a.shape[1] == b.shape[0], "Incompatible dimensions" + #assert a.is_contiguous(), "Matrix A must be contiguous" + #assert b.is_contiguous(), "Matrix B must be contiguous" + M, K = a.shape + K, N = b.shape + # 1D launch kernel where each block gets its own program. + + grid = num_cus + + streamk_gemm[ + grid, + ](a, b, c, P, locks, M, N, K, num_cus, a.stride(0), a.stride(1), b.stride(0), b.stride(1), c.stride(0), c.stride(1), + BLOCK_SIZE_M=block_m, BLOCK_SIZE_N=block_n, BLOCK_SIZE_K=block_k, GROUP_SIZE_M=group_m, num_warps=num_warps, + num_stages=num_stages, waves_per_eu=waves_per_eu, matrix_instr_nonkdim=mfmaInstrSize, kpack=kpack, EVEN_K=EVEN_K) + return c + + +def test_correctness(M, N, K, num_cus, col_a, col_b, dtype_a, dtype_b, dtype_c, init_type, config, verbose): + block_m, block_n, block_k, group_m, num_warps, num_stages, waves_per_eu, mfmaInstrSize, kpack = read_config(config) + torch.manual_seed(0) + #a = torch.randn((M, K), device='cuda', dtype=datatype) + #b = torch.randn((K, N), device='cuda', dtype=datatype) + a, a_fp16 = gen_input(M, K, dtype_a, col_a, 1, init_type, device='cuda') + b, b_fp16 = gen_input(K, N, dtype_b, col_b, 2, init_type, device='cuda') + # Allocates output. + print(f"{block_k}") + EVEN_K = K % block_k == 0 + c = torch.zeros((M, N), device=a.device, dtype=tl_to_torch_types[name_to_tl_types[dtype_c]]) + locks = torch.zeros((num_cus, ), device="cuda", dtype=torch.int32) + P = torch.zeros((num_cus, block_m * block_n), device="cuda", dtype=torch.float32) + triton_output = matmul(a, b, c, P, locks, num_cus, block_m, block_n, block_k, group_m, num_warps, num_stages, + waves_per_eu, mfmaInstrSize, kpack, EVEN_K) + torch_output = torch.matmul(a_fp16, b_fp16) + # print(f"triton_output={triton_output}") + # print(f"torch_output={torch_output}") + rtol = 0 if torch.version.hip is None else 1e-2 + atol = 1e-3 + row_a_str = 'N' if col_a else 'T' + row_b_str = 'N' if col_b else 'T' + size_str = '' + if verbose: + size_str = f'SIZE M: {M}, N: {N}, K: {K}, trans: {row_a_str}{row_b_str}' + if torch.allclose(triton_output.to(torch.float16), torch_output, atol=atol, rtol=rtol): + print(f'{size_str} Correct✅') + else: + print(f'{size_str} Incorrect❌') + + +def get_default_tuning_result_filename(): + git_branch_name = run_bash_command("git rev-parse --abbrev-ref HEAD") + git_branch_name = git_branch_name[0].decode() + git_commit_hash = run_bash_command("git rev-parse --short HEAD") + git_commit_hash = git_commit_hash[0].decode() + + dt_string = datetime.now().strftime("%m-%d-%Y-%H:%M:%S") + defaultName = f"tuning_results_{git_branch_name}@{git_commit_hash}_{dt_string}.yaml" + return defaultName + + +def parse_args(): + parser = argparse.ArgumentParser( + prog="tune a specific gemm size", + allow_abbrev=False, + ) + + parser.add_argument("-m", type=int, default=0) + parser.add_argument("-n", type=int, default=0) + parser.add_argument("-k", type=int, default=0) + parser.add_argument("-col_a", action='store_true', default=False, help='whether matrix a is column major') + parser.add_argument("-col_b", action='store_true', default=False, help='whether matrix b is column major') + parser.add_argument("-dtype_a", type=str, default='fp16', help="matrix a element data type") + parser.add_argument("-dtype_b", type=str, default='fp16', help="matrix b element data type") + parser.add_argument("-dtype_c", type=str, default='fp16', help="output element data type") + parser.add_argument("--ngpus", type=int, default=0, help='number of GPUs used in the profiling step') + parser.add_argument("--gpu_ids", type=lambda s: [int(id) for id in s.split(',')], default=[], + help='list of gpu ids to use for tuning') + parser.add_argument("--gemm_size_file", type=str, default="", help='yaml file to indicate matrix size') + parser.add_argument("--o", type=str, default=get_default_tuning_result_filename(), + help='yaml file to store tuning results') + parser.add_argument("--keep", action='store_true', default=False, help='keep generated files') + parser.add_argument("--compare", action='store_true', default=False, help="Whether check result correctness") + parser.add_argument("--compare_wo_tuning", action='store_true', default=False, + help="Whether check result correctness") + parser.add_argument("--benchmark", action='store_true', default=False, help="Benchmark the given config") + parser.add_argument("--time_breakdown", action='store_true', default=False, + help="Show detailed time breakdown of each step during the tuning") + parser.add_argument("--verbose", action='store_true', default=False, + help="enables time_breakdown and additional logging messages") + parser.add_argument("--num_threads", type=int, default=16, + help="number of threads to use for kernel compilation and post processing") + parser.add_argument("--jobs", type=int, default=1, help="number of generated files") + parser.add_argument("--iters", type=int, default=1000, help="number of generated files") + parser.add_argument("--init_type", type=str, default='randn', + help="Initialization type for input matrices (default uniform rand [0, 1.0)])") + parser.add_argument("--no_warmup", action='store_true', default=False, help="Do not call the warmup kernel") + args = parser.parse_args() + + return args + + +TORCH_HAS_FP8E5B16 = hasattr(torch, 'float8_e5m2fnuz') +TORCH_HAS_FP8E4B8 = hasattr(torch, 'float8_e4m3fnuz') +tl_to_torch_types = { + tl.float16: torch.float16, + tl.bfloat16: torch.bfloat16, + tl.float32: torch.float32, + tl.int8: torch.int8, + tl.int32: torch.int32, +} +if TORCH_HAS_FP8E5B16: + tl_to_torch_types[tl.float8e5b16] = torch.float8_e5m2fnuz +if TORCH_HAS_FP8E4B8: + tl_to_torch_types[tl.float8e4b8] = torch.float8_e4m3fnuz + +name_to_tl_types = { + 'int8': tl.int8, + 'int32': tl.int32, + 'fp16': tl.float16, + 'fp32': tl.float32, + 'bf16': tl.bfloat16, + 'fp8': tl.float8e4b8, + 'bf8': tl.float8e5b16, +} + + +def process_item(item): + M = item['M'] + N = item['N'] + K = item['K'] + col_a = False if item['rowMajorA'] == 'T' else True + col_b = False if item['rowMajorB'] == 'T' else True + del item['M'] + del item['N'] + del item['K'] + del item['rowMajorA'] + del item['rowMajorB'] + return M, N, K, col_a, col_b, item + + +def type_name_to_bytes(ty_name): + if '32' in ty_name: + return 4 + if '16' in ty_name: + return 2 + if '8' in ty_name: + return 1 + else: + print(f"Unrecognized input type name {ty_name}") + sys.exit(1) + + +def format_output(unformatted): + if unformatted < 0.0001: + formatted = "{:.3e}".format(unformatted) + elif unformatted > 1000: + formatted = "{:.1f}".format(unformatted) + else: + formatted = "{:.2f}".format(unformatted) + return formatted + + +def main(): + args = parse_args() + matrix_size_file = args.gemm_size_file + tuning_output_file = args.o + keepTmp = args.keep + run_bench = args.benchmark + jobs = args.jobs + iters = args.iters + skipWarmup = args.no_warmup + num_cus = 304 + + # Get GPU ids + ngpus = args.ngpus + gpu_ids = args.gpu_ids + if ngpus != 0 and gpu_ids: + print("--ngpus and --gpu_ids are mutually exclusive options") + return os.EX_USAGE + if ngpus == 0 and not gpu_ids: + ngpus = 1 + if ngpus != 0: + gpus = range(ngpus) + if gpu_ids: + gpus = gpu_ids + + if run_bench: + gpus = [gpus[0]] + jobs = 1 + + # Get element type + dtype_a = args.dtype_a + dtype_b = args.dtype_b + dtype_c = args.dtype_c + dtype_p = 'fp32' + dtype_lock = 'int32' + if dtype_a not in name_to_tl_types or dtype_b not in name_to_tl_types or dtype_c not in name_to_tl_types: + print(f"Unsupported dtype_a {args.dtype_a} or dtype_b {args.dtype_b} or dtype_c {args.dtype_c}") + print("Supported types: ", list(name_to_tl_types.keys())) + sys.exit(1) + + mnks = [] + # TODO: make it more robust to get user input + init_type = args.init_type + if matrix_size_file == "" or not os.path.isfile(matrix_size_file): + M = args.m + N = args.n + K = args.k + col_a = args.col_a + col_b = args.col_b + mnks = [(M, N, K, col_a, col_b, None)] + else: + with open(matrix_size_file) as file: + matrix_sizes = yaml.safe_load(file) + for item in matrix_sizes: + M, N, K, col_a, col_b, item = process_item(item) + mnks.append((M, N, K, col_a, col_b, item)) + + # Check correctness from given configs + if args.compare_wo_tuning: + for (M, N, K, col_a, col_b, myConfig) in mnks: + test_correctness(M, N, K, num_cus, col_a, col_b, dtype_a, dtype_b, dtype_c, init_type, myConfig, True) + return + + configs_full = get_full_tuning_space() + + start_time = datetime.now() + if run_bench: + print(f"Benchmarking gemm with {dtype_a} inputs") + print("trans M N K TFLOPS us") + else: + print(f"Tuning {len(mnks)} gemm sizes starts at: {start_time}", flush=True) + f_results = open(tuning_output_file, 'w') + + for (M, N, K, col_a, col_b, myConfig) in mnks: + start_local_time = datetime.now() + # Obtain a pruned tuning space according to gemm size + # If running benchmark, use the provided config + pruned_configs = [myConfig] if run_bench else prune_configs(M, N, K, configs_full, type_name_to_bytes(dtype_a), + type_name_to_bytes(dtype_b)) + + row_a_str = 'N' if col_a else 'T' + row_b_str = 'N' if col_b else 'T' + size_str = f'SIZE: {M} {N} {K} {row_a_str}{row_b_str}' + if not run_bench: + print(f"{size_str} nConfigs: {len(pruned_configs)}", end=" ", flush=True) + else: + print(f"{row_a_str}{row_b_str} {M:5d} {N:5d} {K:5d} ", end="") + + # The main tuning funtion for one gemm size + verbose_level = 0 + if args.time_breakdown: + verbose_level = 1 + if args.verbose: + verbose_level = 2 + minTime, bestConfig, compile_time, profile_time, post_time = tune_gemm_config( + M, N, K, num_cus, col_a, col_b, dtype_a, dtype_b, dtype_c, dtype_p, dtype_lock, init_type, pruned_configs, + run_bench, jobs, iters, skipWarmup, num_threads=args.num_threads, gpus=gpus, verbose=verbose_level) + + EVEN_K = True if K % bestConfig.get('BLOCK_SIZE_K') == 0 else False + # post processing the numbers + perf_tflops = lambda us: 2 * M * N * K * 1e-12 / (us * 1e-6) + tri_tflops = perf_tflops(minTime) + formatted_tflops = format_output(tri_tflops) + minTime = format_output(minTime) + if not run_bench: + print(f'TFLOPS: {formatted_tflops} time(us): {minTime}', end=" ", flush=True) + + bestConfig_compact_str, _ = gen_kernel_and_configStr_from_config(M, N, K, num_cus, EVEN_K, bestConfig, None, + None, None, None, None) + if not run_bench: + print(f'best_config: {bestConfig_compact_str}', end=" ", flush=True) + + # write best config to tuning_results.yaml + if run_bench: + print(f"{formatted_tflops} {minTime}") + + sizeDict = {'M': M, 'N': N, 'K': K, 'rowMajorA': row_a_str, 'rowMajorB': row_b_str} + sizeDict.update(bestConfig) + if not run_bench: + f_results.write("- " + str(sizeDict) + " ") + f_results.write(f'# TFLOPS: {formatted_tflops} time(us): {minTime}\n') + + # remove generated files if asked to + if not keepTmp: + for i in range(jobs): + generated_script = generated_kernel_name(M, N, K, i) + os.remove(generated_script) + if not skipWarmup: + os.remove(generated_script + ".failed_configs") + for f in glob.glob(f"results_{i}.*"): + os.remove(f) + + # Check correctness if asked to + if args.compare: + print("correctness: ", end=" ", flush=True) + test_correctness(M, N, K, num_cus, col_a, col_b, dtype_a, dtype_b, dtype_c, init_type, bestConfig, False) + elif not run_bench: + print("", flush=True) + + end_local_time = datetime.now() + if not run_bench: + print( + f">>> Elapsed time: {end_local_time - start_local_time} = {compile_time} (compile) + {profile_time} (profile) + {post_time} (post processing)", + flush=True) + + if not run_bench: + f_results.close() + + end_time = datetime.now() + tuning_time = end_time - start_time + if not run_bench: + print(f"Tuning ends at: {end_time}") + print(f"Total tuning time (h:m:s): {tuning_time}") + + +if __name__ == '__main__': + sys.exit(main()) diff --git a/python/perf-kernels/tools/amdgcn-cfg/README.md b/python/perf-kernels/tools/amdgcn-cfg/README.md new file mode 100644 index 000000000000..bea420ea530c --- /dev/null +++ b/python/perf-kernels/tools/amdgcn-cfg/README.md @@ -0,0 +1,14 @@ +# Control Flow Graph Generator from AMDGCN assembly + +The script reads an assembly file and generates a Control Flow Graph (CFG) for each function in the file. The graph can be saved in `dot`, `svg` and `pdf` formats. The nodes of a graph can be represented with 1) just labels or 2) the corresponding assembly code. The edges of a graph can help to identify cycles and, thus, to provide a better navigation through the code. + + +### Basic usage + +``` +python ./amdgcn-cfg.py -i -o / -f [dot|svg|pdf] +``` + +`dot`-files can be visualize with [this](https://dreampuf.github.io/GraphvizOnline) online tool. You just need to copy and paste the content of a generated `dot`-file. + +By default, the nodes are named with basic block labels. Use `-v` or `--verbose` option to add assembly source code to corresponding nodes. diff --git a/python/perf-kernels/tools/amdgcn-cfg/amdgcn-cfg.py b/python/perf-kernels/tools/amdgcn-cfg/amdgcn-cfg.py new file mode 100644 index 000000000000..ae2f65830766 --- /dev/null +++ b/python/perf-kernels/tools/amdgcn-cfg/amdgcn-cfg.py @@ -0,0 +1,222 @@ +import os +import argparse +import re +from collections import OrderedDict +import graphviz + + +class Options: + + def __init__(self, input_file, output_file, verbose, format): + if not os.path.exists(input_file): + raise RuntimeError('input file is not provided') + + output_dir = os.path.dirname(output_file) + if not os.path.exists(output_dir): + raise RuntimeError('output directory does not exist') + + self.input_file = input_file + self.output_file = output_file + self.verbose = verbose + self.format = format + self.output_dir = output_dir + + +class Block: + + def __init__(self, label, code): + self.label = label + self.code = code + self.edges = [] + + +class Kernel: + + def __init__(self, kernel_name, blocks): + self.name = kernel_name + self.blocks = blocks + self.cfg = None + + +begin_label = 'Begin' +end_label = 'End' + + +def find_kernel(text): + func_name_expr = r'^([^\s^\.]\w.+):' + func_name = None + start = None + for index, line in enumerate(text): + match = re.search(func_name_expr, line) + if match is not None: + func_name = match[1] + start = index + break + if start is None: + return None, None, None + + end = None + for index, line in enumerate(text): + if re.search(r's_endpgm', line) is not None: + end = index + break + + if end is None: + return None, None, None + + return func_name, text[start:end + 1], end + + +def find_label(kernel): + label = None + index = None + for index, line in enumerate(kernel): + match = re.search(r'^\.(\w+):', line) + if match is not None: + label = match[1] + break + return label, index + + +def get_block_list(kernel): + label, index = find_label(kernel) + + blocks = OrderedDict() + if (index > 1): + blocks[begin_label] = Block(begin_label, kernel[:index - 1]) + + while label is not None: + kernel = kernel[index + 1:] + next_label, next_index = find_label(kernel) + if next_label is None: + code = kernel[index:] + else: + code = kernel[:next_index] + blocks[label] = Block(label, code) + + label = next_label + index = next_index + + blocks[end_label] = Block(end_label, []) + + return blocks + + +def find_terminators(code): + terminator_labels = [] + for line in code: + branch = re.search(r'(c)?branch.*\s+\.?(.*)', line) + if branch is not None: + is_condional = True if len(branch.groups()) == 2 else False + label_idx = 2 if is_condional else 1 + terminator_labels.append(branch[label_idx]) + if not is_condional: + return terminator_labels, True + end = re.search(r's_endpgm', line) + if end is not None: + terminator_labels.append(end_label) + return terminator_labels, True + + return terminator_labels, False + + +def add_edges(kernel): + keys = list(kernel.blocks.keys()) + for index, curr_label in enumerate(keys): + if curr_label == end_label: + continue + + code = kernel.blocks[curr_label].code + terminators, is_last_unconditional = find_terminators(code[:-1]) + + if is_last_unconditional: + # unconditional jump in the middle of the block + break + + # handle the last terminator in the current BB + last_terminator, is_unconditional = find_terminators([code[-1]]) + + is_conditional = not is_unconditional + next_block_label = keys[index + 1] + is_next_covered = next_block_label in terminators + + if last_terminator: + terminators.extend(last_terminator) + if is_conditional and not is_next_covered: + next_block_label = keys[index + 1] + terminators.append(next_block_label) + else: + if not is_next_covered: + next_block_label = keys[index + 1] + terminators.append(next_block_label) + + assert (len(terminators)) + kernel.blocks[curr_label].edges = terminators + + +def generate_cfg(kernel, options): + graph = graphviz.Digraph(f'{kernel.name}') + for curr_label in kernel.blocks: + block = kernel.blocks[curr_label] + asm = [line.strip() for line in block.code] + if options.verbose: + label_text = repr('\n'.join([f'{curr_label}', *asm])) + else: + label_text = curr_label + graph.node(curr_label, shape='rect', labeljust='l', margin='0.01', label=label_text) + + for curr_label in kernel.blocks: + block = kernel.blocks[curr_label] + for edge in block.edges: + graph.edge(curr_label, edge) + + return graph + + +def main(options): + asm = [] + with open(options.input_file, 'r') as file: + context = file.readlines() + for line in context: + asm.append(line[:-1]) + + kernels = [] + last_end_index = 0 + while last_end_index is not None: + func_name, kernel_asm, last_end_index = find_kernel(asm) + if kernel_asm is None: + break + + blocks = get_block_list(kernel_asm) + kernel = Kernel(func_name, blocks) + add_edges(kernel) + + cfg = generate_cfg(kernel, options) + kernel.cfg = cfg + kernels.append(kernel) + asm = asm[last_end_index + 1:] + + for index, kernel in enumerate(kernels): + output_file_name = f'{options.output_file}.kernel-{index}' + if options.format == 'dot': + with open(f'{output_file_name}.dot', 'w') as file: + file.write(str(kernel.cfg)) + file.write('\n') + else: + kernel.cfg.render( + filename=f'{output_file_name}', + format=options.format, + ).replace('\\', '/') + + +if __name__ == "__main__": + parser = argparse.ArgumentParser(prog="Generates Control Flow Graph (CFG) from amdgcn assembly file", ) + parser.add_argument("-i", "--input", type=str, default=None, help="input file") + parser.add_argument("-o", "--output", type=str, default=None, help="output file prefix") + parser.add_argument("-v", "--verbose", action='store_true', help='verbose output') + parser.add_argument("-f", "--format", choices=['dot', 'svg', 'pdf'], default="dot", help="output format type") + args = parser.parse_args() + + options = Options(args.input, args.output, args.verbose, args.format) + + main(options) diff --git a/python/perf-kernels/tools/occ.sh b/python/perf-kernels/tools/occ.sh new file mode 100755 index 000000000000..51c8f9095907 --- /dev/null +++ b/python/perf-kernels/tools/occ.sh @@ -0,0 +1,71 @@ +#! /bin/bash + +## $1: input script that contains one kernel + +rm -rf ~/.triton/cache/ + +export MLIR_ENABLE_DUMP=1 +export AMDGCN_ENABLE_DUMP=1 +## Assume CDNA arch +SIMD=4 +LDS_SIZE=65536 +TOTAL_VGPR=512 + +get_occ_per_CU() { + ## $1: vgpr count + vgpr=$1 + occPerEU=$((TOTAL_VGPR/vgpr)) + if [[ $vgpr -gt 256 ]]; then + occPerEU=1 + elif [[ $vgpr -gt 168 ]]; then + occPerEU=2 + elif [[ $vgpr -gt 128 ]]; then + occPerEU=3 + elif [[ $vgpr -gt 96 ]]; then + occPerEU=4 + elif [[ $vgpr -gt 80 ]]; then + occPerEU=5 + elif [[ $vgpr -gt 72 ]]; then + occPerEU=6 + elif [[ $vgpr -gt 64 ]]; then + occPerEU=7 + else + occPerEU=8 + fi + + occPerCU=$((occPerEU*SIMD/num_warps)) + echo $occPerCU +} + +$1 > output.mlir 2>&1 + +LDS_line=$(sed -n '/triton_gpu\.shared\ /p' output.mlir | tail -n 1 | grep -o 'triton_gpu.shared = [0-9]*') +numWarps_line=$(sed -n '/triton_gpu\.num-warps/p' output.mlir | tail -n 1 | grep -o 'triton_gpu.num-warps. = [0-9]*') + +LDS=${LDS_line##*=} +num_warps=${numWarps_line##*=} +echo "LDS: $LDS, num_warps: $num_warps" + +VGPRs=$(sed -n '/vgpr_count/p' output.mlir | tail -n 1 | awk '{print $2}') +SPILLs=$(sed -n '/vgpr_spill/p' output.mlir | tail -n 1 | awk '{print $2}') + +echo "VGPRS: $VGPRs (spill: $SPILLs)" + +occLDSPerCU=$((LDS_SIZE/LDS)) +occVgprPerCU=$(get_occ_per_CU $VGPRs) +occPerCU=$occVgprPerCU +if [ $occLDSPerCU -lt $occVgprPerCU ];then + occPerCU=$occLDSPerCU +fi +occPerEU=$((occPerCU*num_warps/SIMD)) +echo "occupancy: $occPerEU waves/SIMD or $occPerCU workgroups/CU (occLDSPerCU: $occLDSPerCU, occVgprPerCU: $occVgprPerCU)" + +perf=$(tail -n 2 output.mlir) +echo "$perf" + +## remove distracting info from the assembly +sed -i '/local_/! {/\.loc/d}' output.mlir +sed -i '/\.Ltmp.*:/d' output.mlir +sed -i '/AMD clang version/d' output.mlir + +sed -n '/AMDGCN/, $p' output.mlir > output.amdgcn diff --git a/python/perf-kernels/tools/plot-layout/README.md b/python/perf-kernels/tools/plot-layout/README.md new file mode 100644 index 000000000000..40de35bdb3aa --- /dev/null +++ b/python/perf-kernels/tools/plot-layout/README.md @@ -0,0 +1,117 @@ +# Plot script for triton layouts + +This script is used to draw triton layouts in the context of matmul. +Here is the help info from the script. + +```bash +>$ python3 plot_layout.py -h +usage: Draw triton layouts [-h] [-shape SHAPE SHAPE SHAPE] [-plot {blocked,dot,wmma,lds}] [-nonKDim {16,32}] [-sizePerThread SIZEPERTHREAD SIZEPERTHREAD] [-threadsPerWarp THREADSPERWARP THREADSPERWARP] + [-warpsPerCTA WARPSPERCTA WARPSPERCTA] [-order ORDER ORDER] [-kWidth {4,8,16}] [-lds_layout {swizzle,padding,none}] [-lds_access {read,write,none}] [-wave_size {32,64}] [-o O] [-mfmaTrans] [-keep] + +options: + -h, --help show this help message and exit + -shape SHAPE SHAPE SHAPE + Tensor shape in the form of M,N,K + -plot {blocked,dot,wmma,lds} + choose plot mode + -nonKDim {16,32} mfma instruction dim + -sizePerThread SIZEPERTHREAD SIZEPERTHREAD + -threadsPerWarp THREADSPERWARP THREADSPERWARP + -warpsPerCTA WARPSPERCTA WARPSPERCTA + -order ORDER ORDER + -kWidth {4,8,16} number of elements per thread + -lds_layout {swizzle,padding,none} + choose the LDS data layout + -lds_access {read,write,none} + choose LDS access mode + -wave_size {32,64} choose the wmma instruction mode + -o O output pdf file name (without surfix) + -mfmaTrans If set, then use mfma.trans layout + -keep If set, keep the generated .tex file +``` + +## Installation +This script does not require torch or triton to be installed. The only package +it depends on is latex. On Ubuntu, do +```bash +sudo apt install texlive-full +``` + +## Draw blocked layout (`-plot blocked`) + +Examples: +```bash +python3 plot_layout.py -plot blocked -shape 128 128 64 -sizePerThread 1 8 -threadsPerWarp 8 8 -warpsPerCTA 4 1 +python3 plot_layout.py -plot blocked -shape 16 128 64 -sizePerThread 1 8 -threadsPerWarp 16 4 -warpsPerCTA 1 2 +python3 plot_layout.py -plot blocked -shape 32 128 64 -sizePerThread 8 1 -threadsPerWarp 4 16 -warpsPerCTA 1 2 -order 0 1 +``` + +Blocked layouts are used during global load. It is used to describe the layout of the tensor +for pointers and results. +We can provide tensor shape (`-shape M N K`) and blocked layout parameters ( +`-sizePerThread x y`, `-threadsPerWarp x y`, and `-warpsPerCTA x y`). +We can also provide the order of the tensor as `-order x y` to control which dim +is the fastest changing dimension. + +Notes +- All of the gemm dims (M, N, and K) are needed when providing the shape. But only + M and K will be used to plot the layout of the tensor. +- The script does not support the case when threads are loading elements that are + out of the boundary of the tensor dimensions. This means + - For M: sizePerThread[0] * threadsPerWarps[0] * warpsPerCTA[0] <= M + - For K: sizePerThread[1] * threadsPerWarps[1] * warpsPerCTA[1] <= K + + +## Draw mfma operand and result layouts (`-plot dot`) + +Examples: +```bash +python3 plot_layout.py -plot dot -shape 128 128 64 -warpsPerCTA 2 4 -nonKDim 32 -kWidth 4 +python3 plot_layout.py -plot dot -shape 128 128 64 -warpsPerCTA 2 4 -nonKDim 32 -kWidth 8 +python3 plot_layout.py -plot dot -shape 128 128 64 -warpsPerCTA 2 4 -nonKDim 32 -kWidth 8 -mfmaTrans +python3 plot_layout.py -plot dot -shape 128 128 64 -warpsPerCTA 2 4 -nonKDim 16 -kWidth 8 +python3 plot_layout.py -plot dot -shape 128 128 64 -warpsPerCTA 2 4 -nonKDim 16 -kWidth 16 +``` + +This mode draws two graphs: +1. The layout of the whole tile for tile A, B, and C +2. The layout of a single mfma block, operands and results of one or more mfma + instructions that share the same accumulating VGPRs. + This view has thread distributions among tensor elements. + +Knobs +- `-kWidth`: the number of elements that will be loaded into one thread at once +- `-nonKDim`: 16 ot 32, which is used to control the mfma instruction size +- `-mfmaTrans`: if set, the transposed mfma layout will be plotted. + +Notes +- The layout shows the mapping from the threads/wave to the elements in the + original tensor. It does not care if the elements are arranged in LDS, like + swizzling to avoid bank conflicts. +- The script does not allow settings for data type or k dim of the mfma instruction. + This can be controled by the `-kWidth` flag. + - For example, if we want `mfma_32x32x8xf16`, we can set `-nonKDim 32` and `-kWidth 4`. + - If we want `mfma_32x32x16xf8`, we can set `-nonKDim 32` and `-kWidth 8`. + + +## Draw LDS access (`-plot lds`) + +Examples: +```bash +python3 plot_layout.py -plot lds -lds_layout none -lds_access none -shape 128 128 64 -kWidth 8 +``` + +Knobs +- `kWidth` here means the vector size when accessing LDS +- Three options for `-lds_layout`: + - `none`: no swizzling, no padding + - `padding`: padding at every 128B + - `swizzling`: apply the swizzling pattern, which is derived from tensor shape and kWidth. +- Three options for `-lds_access`: + - `none`: do not plot access pattern + - `read`: plot accessed elements during ds_read + - `write`: plot accessed elements during ds_write. Note that this needs some infomation from + global load. Therefore, we need to provide `-sizePerThread` and `-threadsPerWarp`. + +Notes +- This mode is rarely used. If you have any questions, please contact Lixun Zhang directly. diff --git a/python/perf-kernels/tools/plot-layout/plot_layout.py b/python/perf-kernels/tools/plot-layout/plot_layout.py new file mode 100644 index 000000000000..599f92c790e4 --- /dev/null +++ b/python/perf-kernels/tools/plot-layout/plot_layout.py @@ -0,0 +1,290 @@ +import argparse +import sys +import os +import subprocess + + +def draw_preamble_cmd(): + return '''\\documentclass[tikz, border=1mm, dvipsnames]{standalone} +\\usepackage{ifthen} +\\usepackage{tikz} +\\usetikzlibrary{arrows.meta,arrows} +\\usetikzlibrary{intersections} +\\usetikzlibrary{calc, quotes} +\\usetikzlibrary{patterns} +\\usepackage{xparse} + +\\ExplSyntaxOn +\\NewExpandableDocumentCommand{\\bitwiseXor}{mm} + { + \\recuenco_bitwise_xor:nn { #1 } { #2 } + } + +\\cs_new:Nn \\recuenco_bitwise_xor:nn + { + \\int_from_bin:e + { + \\__recuenco_bitwise_xor:ee { \\int_to_bin:n { #1 } } { \\int_to_bin:n { #2 } } + } + } +\\cs_generate_variant:Nn \\int_from_bin:n { e } + +\\cs_new:Nn \\__recuenco_bitwise_xor:nn + { + \\__recuenco_bitwise_xor_binary:ee + { + \\prg_replicate:nn + { + \\int_max:nn { \\tl_count:n { #1 } } { \\tl_count:n { #2 } } - \\tl_count:n { #1 } + } + { 0 } + #1 + } + { + \\prg_replicate:nn + { + \\int_max:nn { \\tl_count:n { #1 } } { \\tl_count:n { #2 } } - \\tl_count:n { #2 } + } + { 0 } + #2 + } + } +\\cs_generate_variant:Nn \\__recuenco_bitwise_xor:nn { ee } + +\\cs_new:Nn \\__recuenco_bitwise_xor_binary:nn + { + \\__recuenco_bitwise_xor_binary:w #1;#2; + } +\\cs_generate_variant:Nn \\__recuenco_bitwise_xor_binary:nn { ee } + +\\cs_new:Npn \\__recuenco_bitwise_xor_binary:w #1#2;#3#4; + { + \\int_abs:n { #1-#3 } + \\tl_if_empty:nF { #2 } { \\__recuenco_bitwise_xor_binary:w #2;#4; } + } + +\\ExplSyntaxOff''' + + +def draw_dot_layout_cmd(M, N, K, mfmaNonKDim, warpsPerCTA, trans, kpack): + return f'''\\begin{{document}} + \\begin{{tikzpicture}} + \\def\\scale{{1}} + \\def\\elem{{0.04}} + \\coordinate (C TL) at (0,0); + \\def\\opColorAL{{magenta}} + \\def\\opColorAR{{cyan}} + \\def\\opColorBL{{Maroon}} + \\def\\opColorBR{{BlueGreen}} + \\drawDot{{{M}}}{{{N}}}{{{K}}}{{{mfmaNonKDim}}}{{{warpsPerCTA[0]}}}{{{warpsPerCTA[1]}}}{{{trans}}}{{{kpack}}} + + \\coordinate (C TL) at ($(C TL)+({N}*\elem+32*\elem, 0)$); + \\def\\mfmaTrans{{{trans}}} + + %% Draw zoomed in view of mfma + \\def\\elem{{.16}} + \\pgfmathsetmacro{{\\gap}}{{\\elem*5}} + \\pgfmathsetmacro{{\\nonTrans}}{{1-\\mfmaTrans}} + \\pgfmathsetmacro{{\\groups}}{{64/{mfmaNonKDim}}} + \\coordinate (C TL) at ($(C TL)+(.5*\\gap+1.2*\\nonTrans*\\gap+\\groups*{kpack}*\\elem, 0)$); + \\drawMFMAInstr{{{mfmaNonKDim}}}{{{kpack}}}{{\\mfmaTrans}} + + \\end{{tikzpicture}} +\\end{{document}}''' + + +def draw_blocked_layout_cmd(M, K, sizePerThread, threadsPerWarp, warpsPerCTA, order): + return f'''\\begin{{document}} + \\begin{{tikzpicture}} + \\def\\scale{{1}} + \\def\\elem{{0.06}} + \\coordinate (TL) at (0,0); + \\drawBlockedTensor{{{M}}}{{{K}}}{{{sizePerThread[0]}}}{{{sizePerThread[1]}}}{{{threadsPerWarp[0]}}}{{{warpsPerCTA[0]}}}{{{warpsPerCTA[1]}}}{{{order[0]}}} + \\end{{tikzpicture}} +\\end{{document}}''' + + +def draw_lds_access_cmd(M, K, kpack, ldsLayout, ldsAccess, sizePerThread, threadsPerWarp): + if ldsLayout == 'swizzle': + hasSwizzle = 1 + elif ldsLayout == 'padding': + hasSwizzle = 2 + else: + hasSwizzle = 0 + + if ldsAccess == 'read': + accessMode = 1 + elif ldsAccess == 'write': + accessMode = 2 + else: + accessMode = 0 + + return f'''\\begin{{document}} + \\begin{{tikzpicture}} + \\def\\scale{{1}} + \\def\\M{{{M}}} + \\def\\K{{{K}}} + \\def\\vec{{{kpack}}} + \\def\\hasSwizzle{{{hasSwizzle}}} + \\def\\accessMode{{{accessMode}}} + + \\def\\sizePerThreadK{{{sizePerThread[1]}}} + \\def\\sizePerThreadM{{{sizePerThread[0]}}} + \\def\\threadsPerWarpK{{{threadsPerWarp[1]}}} + + \\def\\elem{{0.18}} + \\coordinate (TL) at (0,0); + \\drawTensorLayoutGlobalMem + \\coordinate (TL) at ($(TL)+(0, -24*\\elem-10*\\elem)$); + \\drawLDSLayoutTritonSwizzling{{\\hasSwizzle}}{{\\accessMode}} + \\end{{tikzpicture}} +\\end{{document}}''' + + +def draw_wmma_instr_cmd(waveSize): + wmma_mode = 0 if waveSize == 32 else 1 + return f'''\\begin{{document}} + \\begin{{tikzpicture}} + \\def\\scale{{1}} + \\coordinate (C TL) at (0,0); + \\def\\elem{{0.25}} + \\drawWMMAInstr{{{wmma_mode}}}{{1}} + \\end{{tikzpicture}} +\\end{{document}}''' + + +def run_bash_command(commandstring): + proc = subprocess.run(commandstring, shell=True, check=True, executable='/bin/bash', stdout=subprocess.PIPE) + return proc.stdout.splitlines() + + +def parse_args(): + parser = argparse.ArgumentParser( + prog="Draw triton layouts", + allow_abbrev=False, + ) + ## tensor shapes + parser.add_argument("-shape", type=int, nargs=3, default=(32, 128, 64), help='Tensor shape in the form of M,N,K') + parser.add_argument("-plot", type=str, default="blocked", choices=['blocked', 'dot', 'wmma', 'lds'], + help='choose plot mode') + parser.add_argument("-nonKDim", type=int, default=32, choices=[16, 32], help='mfma instruction dim') + ## blocked layout parameters + parser.add_argument("-sizePerThread", type=int, nargs=2, default=(1, 4)) + parser.add_argument("-threadsPerWarp", type=int, nargs=2, default=(16, 4)) + parser.add_argument("-warpsPerCTA", type=int, nargs=2, default=(1, 4)) + parser.add_argument("-order", type=int, nargs=2, default=(1, 0)) + ## LDS access parameters + parser.add_argument("-kWidth", type=int, default=4, choices=[4, 8, 16], help='number of elements per thread') + parser.add_argument("-lds_layout", type=str, default="none", choices=['swizzle', 'padding', 'none'], + help='choose the LDS data layout') + parser.add_argument("-lds_access", type=str, default="none", choices=['read', 'write', 'none'], + help='choose LDS access mode') + ## wmma instruction layout parameter + parser.add_argument("-wave_size", type=int, default=32, choices=[32, 64], help='choose the wmma instruction mode') + + parser.add_argument("-o", type=str, default="myplot", help='output pdf file name (without surfix)') + parser.add_argument("-mfmaTrans", action='store_true', default=False, help='If set, then use mfma.trans layout') + parser.add_argument("-keep", action='store_true', default=False, help='If set, keep the generated .tex file') + + args = parser.parse_args() + + return args + + +def main(): + args = parse_args() + + shape = args.shape + M = shape[0] + N = shape[1] + K = shape[2] + plot_mode = args.plot + mfmaNonKDim = args.nonKDim + kpack = args.kWidth + trans = 1 if args.mfmaTrans else 0 + ofilename = args.o + keepSrc = args.keep + + ldsLayout = args.lds_layout + ldsAccess = args.lds_access + + waveSize = args.wave_size + + sizePerThread = args.sizePerThread + threadsPerWarp = args.threadsPerWarp + warpsPerCTA = args.warpsPerCTA + order = args.order + + CTAShape = [] + if plot_mode == 'blocked': + print(f"Plotting tensor M={M},K={K} with blocked layout:") + print(f"sizePerThread={sizePerThread}", end=" ") + print(f"threadsPerWarp={threadsPerWarp}", end=" ") + print(f"warpsPerCTA={warpsPerCTA}", end=" ") + print(f"order={order}", end=" ") + CTAShape.append(sizePerThread[0] * threadsPerWarp[0] * warpsPerCTA[0]) + CTAShape.append(sizePerThread[1] * threadsPerWarp[1] * warpsPerCTA[1]) + + if plot_mode == 'dot': + mfma_inst_str = "mfma_32x32" if mfmaNonKDim == 32 else "mfma_16x16" + mfma_trans_str = ".trans" if trans else "" + print(f"Plotting dot operation with shapes M={M},N={N},K={K}") + print("MFMA: " + mfma_inst_str + mfma_trans_str + f" kWidth = {kpack}", end=" ") + print(f"warpsPerCTA={warpsPerCTA}", end=" ") + CTAShape.append(mfmaNonKDim * warpsPerCTA[0]) + CTAShape.append(mfmaNonKDim * warpsPerCTA[1]) + + if plot_mode == 'blocked' or plot_mode == 'dot': + print(f"CTAShape={CTAShape}") + assert M != 0 and CTAShape[0] <= M and M % CTAShape[0] == 0, "bad tensor dimension M" + + if plot_mode == 'blocked': + assert K != 0 and CTAShape[1] <= K and K % CTAShape[1] == 0, "bad tensor dimension K" + + if plot_mode == 'dot': + assert N != 0 and CTAShape[1] <= N and N % CTAShape[1] == 0, "bad tensor dimension N" + assert K != 0 and K % (2 * kpack) == 0, "bad tensor dimension K" + + if plot_mode == 'lds': + print(f"Plotting LDS access for tensor M={M},K={K} with vec={kpack}") + if ldsAccess == 'write': + print(f"sizePerThread={sizePerThread}, threadsPerWarp={threadsPerWarp}") + + with open("myplot.tex", 'w') as f_plot: + with open("tikzplot.tex") as file: + tikz_code = file.read() + + preamble_str = draw_preamble_cmd() + + draw_blockedLayout_str = draw_blocked_layout_cmd(M, K, sizePerThread, threadsPerWarp, warpsPerCTA, order) + + draw_dotLayout_str = draw_dot_layout_cmd(M, N, K, mfmaNonKDim, warpsPerCTA, trans, kpack) + + draw_lds_str = draw_lds_access_cmd(M, K, kpack, ldsLayout, ldsAccess, sizePerThread, threadsPerWarp) + + draw_wmma_str = draw_wmma_instr_cmd(waveSize) + + f_plot.write(preamble_str + "\n") + f_plot.write(tikz_code) + if plot_mode == 'blocked': + f_plot.write(draw_blockedLayout_str) + elif plot_mode == 'dot': + f_plot.write(draw_dotLayout_str) + elif plot_mode == 'lds': + f_plot.write(draw_lds_str) + elif plot_mode == 'wmma': + f_plot.write(draw_wmma_str) + + run_bash_command(f"pdflatex -jobname {ofilename} myplot.tex") + print(f"plot saved in {ofilename}.pdf") + + ## Remove au files + os.remove(f"{ofilename}.aux") + os.remove(f"{ofilename}.log") + if not keepSrc: + os.remove("myplot.tex") + run_bash_command("rm -rf ./auto") + + +if __name__ == '__main__': + sys.exit(main()) diff --git a/python/perf-kernels/tools/plot-layout/tikzplot.tex b/python/perf-kernels/tools/plot-layout/tikzplot.tex new file mode 100644 index 000000000000..d8441b042f02 --- /dev/null +++ b/python/perf-kernels/tools/plot-layout/tikzplot.tex @@ -0,0 +1,880 @@ +\newcommand{\drawBlockedWave}[5]{ + %% + %% Draw a wave coverage with blocked layout + %% + %% Wave TL: pre defined top-left coordinate of the wave + %% \elem: pre defined variable + %% + %% #1: sizePerThread[0] --> sizePerThreadM + %% #2: sizePerThread[1] --> sizePerThreadN + %% #3: threadsPerWarp[0] --> threadsPerWarpM + %% #4: threadsPerWarp[1] --> threadsPerWarpN + %% #5: fastest changing dim --> order + + \pgfmathsetmacro{\sizePerThreadM}{#1} + \pgfmathsetmacro{\sizePerThreadN}{#2} + \pgfmathsetmacro{\threadsPerWarpM}{#3} + \pgfmathsetmacro{\threadsPerWarpN}{#4} + \pgfmathsetmacro{\order}{#5} + + \pgfmathsetmacro{\waveSizeM}{\sizePerThreadM*\threadsPerWarpM} + \pgfmathsetmacro{\waveSizeN}{\sizePerThreadN*\threadsPerWarpN} + + \foreach \tid in {0,...,63}{ + \pgfmathsetmacro{\tidM}{int(\tid/\threadsPerWarpN)} + \pgfmathsetmacro{\tidN}{mod(\tid,\threadsPerWarpN)} + \coordinate (Thread TL) at ($(Wave TL)+(\tidN*\sizePerThreadN*\elem, -\tidM*\sizePerThreadM*\elem)$); + \pgfmathsetmacro{\ratio}{\tidM*10} + + \ifthenelse{\tid = 0}{ + \draw [line width = 0.01mm, fill=red] (Thread TL) + rectangle ++(\sizePerThreadN*\elem, -\sizePerThreadM*\elem); + }{ + \draw [line width = 0.01mm, fill=blue!\ratio!white] (Thread TL) + rectangle ++(\sizePerThreadN*\elem, -\sizePerThreadM*\elem); + } + } + \draw (Wave TL) rectangle ++(\waveSizeN*\elem, -\waveSizeM*\elem); +} + +\newcommand{\drawBlockedCTA}[7]{ + %% + %% Draw a CTA coverage with blocked layout + %% + %% CTA TL: pre defined top-left coordinate of the CTA + %% \elem: pre defined variable + %% + %% #1: sizePerThread[0] --> sizePerThreadM + %% #2: sizePerThread[1] --> sizePerThreadN + %% #3: threadsPerWarp[0] --> threadsPerWarpM + %% #4: threadsPerWarp[1] --> threadsPerWarpN + %% #5: warpsPerCTA[0] --> warpsPerCTAM + %% #6: warpsPerCTA[1] --> warpsPerCTAN + %% #7: fastest changing dim --> order + + \pgfmathsetmacro{\sizePerThreadM}{#1} + \pgfmathsetmacro{\sizePerThreadN}{#2} + \pgfmathsetmacro{\threadsPerWarpM}{#3} + \pgfmathsetmacro{\threadsPerWarpN}{#4} + \pgfmathsetmacro{\warpsPerCTAM}{#5} + \pgfmathsetmacro{\warpsPerCTAN}{#6} + \pgfmathsetmacro{\order}{#7} + + \pgfmathsetmacro{\CTASizeM}{\sizePerThreadM*\threadsPerWarpM*\warpsPerCTAM} + \pgfmathsetmacro{\CTASizeN}{\sizePerThreadN*\threadsPerWarpN*\warpsPerCTAN} + \pgfmathsetmacro{\waveSizeM}{\sizePerThreadM*\threadsPerWarpM} + \pgfmathsetmacro{\waveSizeN}{\sizePerThreadN*\threadsPerWarpN} + + \pgfmathsetmacro{\maxWaveId}{\warpsPerCTAM*\warpsPerCTAN-1} + + \coordinate (Wave TL) at (CTA TL); + \drawBlockedWave{\sizePerThreadM}{\sizePerThreadN}{\threadsPerWarpM}{\threadsPerWarpN}{\order} + \foreach \waveId in {0,...,\maxWaveId}{ + \ifthenelse{\order=1} + { + \pgfmathsetmacro{\waveCoordM}{int(\waveId/\warpsPerCTAN)} + \pgfmathsetmacro{\waveCoordN}{mod(\waveId,\warpsPerCTAN)} + \pgfmathsetmacro{\rot}{0} + }{ + \pgfmathsetmacro{\waveCoordM}{mod(\waveId,\warpsPerCTAM)} + \pgfmathsetmacro{\waveCoordN}{int(\waveId/\warpsPerCTAM)} + \pgfmathsetmacro{\rot}{90} + } + + \coordinate (Wave TL) at ($(CTA TL)+(\waveCoordN*\waveSizeN*\elem, -\waveCoordM*\waveSizeM*\elem)$); + \draw [ultra thin] (Wave TL) rectangle ++(\waveSizeN*\elem, -\waveSizeM*\elem) + node [pos=.5, scale=.6*\scale, inner sep=0, fill=white, rotate=\rot] {wave\waveId}; + } + + \draw [thick] (CTA TL) rectangle ++(\CTASizeN*\elem, -\CTASizeM*\elem); +} + +\newcommand{\drawBlockedTensor}[8]{ + %% + %% Draw a tensor with blocked layout of the following parameters + %% sizePerThread[2] + %% threadsPerWarp[2] + %% warpsPerCTA[2] + %% order[2] + %% + %% TL: pre defined top-left coordinate of the tensor + %% \elem: pre defined variable + %% + %% #1: tensorShape[0] --> M + %% #2: tensorShape[1] --> N + %% #3: sizePerThread[0] --> sizePerThreadM + %% #4: sizePerThread[1] --> sizePerThreadN + %% #5: threadsPerWarp[0] --> threadsPerWarpM + %% Note that threadsPerWarp[1] is calculated by 64/threadsPerWarp[0] + %% #6: warpsPerCTA[0] --> warpsPerCTAM + %% #7: warpsPerCTA[1] --> warpsPerCTAN + %% #8: fastest changing dim --> order + + \pgfmathsetmacro{\M}{#1} + \pgfmathsetmacro{\N}{#2} + \pgfmathsetmacro{\sizePerThreadM}{#3} + \pgfmathsetmacro{\sizePerThreadN}{#4} + \pgfmathsetmacro{\threadsPerWarpM}{#5} + \pgfmathsetmacro{\warpsPerCTAM}{#6} + \pgfmathsetmacro{\warpsPerCTAN}{#7} + \pgfmathsetmacro{\order}{#8} + + \pgfmathsetmacro{\threadsPerWarpN}{64/\threadsPerWarpM} + \pgfmathsetmacro{\CTASizeM}{\sizePerThreadM*\threadsPerWarpM*\warpsPerCTAM} + \pgfmathsetmacro{\CTASizeN}{\sizePerThreadN*\threadsPerWarpN*\warpsPerCTAN} + \pgfmathsetmacro{\CTARepM}{\M/\CTASizeM} + \pgfmathsetmacro{\CTARepN}{\N/\CTASizeN} + \pgfmathsetmacro{\maxCTAId}{\CTARepM*\CTARepN-1} + + \foreach \ctaId in {0,...,\maxCTAId}{ + \pgfmathsetmacro{\ctaCoordM}{int(\ctaId/\CTARepN)} + \pgfmathsetmacro{\ctaCoordN}{mod(\ctaId,\CTARepN)} + \coordinate (CTA TL) at ($(TL)+(\ctaCoordN*\CTASizeN*\elem, -\ctaCoordM*\CTASizeM*\elem)$); + \drawBlockedCTA{\sizePerThreadM}{\sizePerThreadN}{\threadsPerWarpM}{\threadsPerWarpN}{\warpsPerCTAM}{\warpsPerCTAN}{\order} + } + + \node [scale=.7*\scale, above, rotate=90] at ($(TL)+(0, -.5*\M*\elem)$) {M=\M}; + \node [scale=.7*\scale, above] at ($(TL)+(.5*\N*\elem, 0)$) {K=\N}; + + \def\zoomR{1.5} + \coordinate (zoomin BL) at ($(TL)+(0, .3)$); + + \foreach \hl in {0,...,\sizePerThreadM}{ + \draw ($(zoomin BL)+(0, \hl*\elem*\zoomR)$) -- ++(\sizePerThreadN*\elem*\zoomR,0); + } + \foreach \vl in {0,...,\sizePerThreadN}{ + \draw ($(zoomin BL)+(\vl*\elem*\zoomR, 0)$) -- ++(0, \sizePerThreadM*\elem*\zoomR); + } + + \node [scale=.6*\scale, left] at ($(zoomin BL)+(0, .5*\sizePerThreadM*\elem*\zoomR)$) {$t_0$}; + \node [scale=.6*\scale, right] at ($(zoomin BL)+(\sizePerThreadN*\elem*\zoomR, .5*\sizePerThreadM*\elem*\zoomR)$) {\sizePerThreadM$\times$\sizePerThreadN}; + + \draw [densely dotted] (TL) -- (zoomin BL); + \draw [densely dotted] ($(TL)+(\sizePerThreadN*\elem, 0)$) -- ($(zoomin BL)+(\sizePerThreadN*\elem*\zoomR, 0)$); + \draw [fill=red] (TL) rectangle ++(\sizePerThreadN*\elem, -\sizePerThreadM*\elem); +} + +\newcommand{\drawBlockMFMALayoutLarge}[3]{ + %% + %% Draw a single block of MFMA_32x32x8xf16 or MFMA_16x16x16xf16 + %% + %% block TL: pre-defined top-left coordinate of the block + %% \elem: pre defined variable + %% + %% #1: 1 for mfma.trans, 0 for normal mfma + %% #2: mfmaNonKDim + %% #3: verbose. 1 means draw tid in each vec; 0 means draw nothing + + \pgfmathsetmacro{\trans}{#1} + \pgfmathsetmacro{\nonTrans}{1-#1} + \pgfmathsetmacro{\nonKDim}{#2} + \pgfmathsetmacro{\maxTID}{\nonKDim-1} + \pgfmathsetmacro{\groups}{64/\nonKDim} + \pgfmathsetmacro{\maxGID}{\groups-1} + \pgfmathsetmacro{\maxIVec}{\nonKDim*\nonKDim/256-1} + \pgfmathsetmacro{\verbose}{#3} + \foreach \iVec in {0,...,\maxIVec} { + \coordinate (wave TL) at ($(block TL)+(\trans*\iVec*\groups*4*\elem, -\nonTrans*\iVec*\groups*4*\elem)$); + \foreach \tg in {0,...,\maxGID}{ + \pgfmathsetmacro{\colID}{\tg+4} + \pgfmathsetmacro{\col}{\Colors[\colID]} + \foreach \tid in {0,...,\maxTID} { + \pgfmathsetmacro{\ratio}{\tid*2.5*\groups+15} + \ifthenelse{\verbose=0}{ + \draw [line width=0.005mm, fill=\col!\ratio!white] + ($(wave TL)+(\nonTrans*\tid*\elem+\tg*\trans*4*\elem, -\trans*\tid*\elem-\tg*\nonTrans*4*\elem)$) + rectangle ++(\nonTrans*\elem+\trans*4*\elem, -\nonTrans*4*\elem-\trans*\elem); + }{ + \pgfmathsetmacro{\drawTid}{int(\tid+\tg*\nonKDim)} + \draw [line width=0.005mm, fill=\col!\ratio!white] + ($(wave TL)+(\nonTrans*\tid*\elem+\tg*\trans*4*\elem, -\trans*\tid*\elem-\tg*\nonTrans*4*\elem)$) + rectangle ++(\nonTrans*\elem+\trans*4*\elem, -\nonTrans*4*\elem-\trans*\elem) + node [pos=.5, scale=.35*\scale, rotate=90*\nonTrans] {t\drawTid}; + } + } + } + } + \draw [thick] (block TL) rectangle ++(\nonKDim*\elem, -\nonKDim*\elem); +} + + +\newcommand{\drawTensorMFMALayout}[6]{ + %% + %% Draw a tensor with mfma layout. + %% + %% C TL: pre defined top-left coordinates of the tensor + %% + %% #1: M + %% #2: N + %% #3: MFMA nonKDim + %% #4: warpsPerCTA[0] + %% #5: warpsPerCTA[1] + %% #6: 1 for mfma.trans, 0 for normal mfma + + \pgfmathsetmacro{\tensorShapeH}{#1} + \pgfmathsetmacro{\tensorShapeW}{#2} + \pgfmathsetmacro{\mfmaNonKDim}{#3} + \pgfmathsetmacro{\warpsPerCTAH}{#4} + \pgfmathsetmacro{\warpsPerCTAW}{#5} + \pgfmathsetmacro{\mfmaTrans}{#6} + + \coordinate (old TL) at (TL); + \coordinate (TL) at (C TL); + + + \pgfmathsetmacro{\CTARepH}{\tensorShapeH/\mfmaNonKDim/\warpsPerCTAH} + \pgfmathsetmacro{\CTARepW}{\tensorShapeW/\mfmaNonKDim/\warpsPerCTAW} + \pgfmathsetmacro{\maxCTAId}{\CTARepH*\CTARepW-1} + \pgfmathsetmacro{\maxWaveId}{\warpsPerCTAH*\warpsPerCTAW-1} + \pgfmathsetmacro{\CTASizeH}{\warpsPerCTAH*\mfmaNonKDim} + \pgfmathsetmacro{\CTASizeW}{\warpsPerCTAW*\mfmaNonKDim} + + + \foreach \ctaId in {0,...,\maxCTAId}{ + \pgfmathsetmacro{\ctaCoordH}{int(\ctaId/\CTARepW)} + \pgfmathsetmacro{\ctaCoordW}{mod(\ctaId,\CTARepW)} + \coordinate (CTA TL) at ($(TL)+(\ctaCoordW*\CTASizeW*\elem, -\ctaCoordH*\CTASizeH*\elem)$); + %% Draw a detailed view of wave0 in each CTA + \coordinate (block TL) at (CTA TL); + \drawBlockMFMALayoutLarge{\mfmaTrans}{\mfmaNonKDim}{0} + + \foreach \waveId in {0,...,\maxWaveId}{ + \pgfmathsetmacro{\waveCoordH}{int(\waveId/\warpsPerCTAW)} + \pgfmathsetmacro{\waveCoordW}{mod(\waveId,\warpsPerCTAW)} + \coordinate (block TL) at ($(CTA TL)+(\waveCoordW*\mfmaNonKDim*\elem, -\waveCoordH*\mfmaNonKDim*\elem)$); + %% Inside the loop, only draw a rectangle + \draw [ultra thin] (block TL) rectangle ++(\mfmaNonKDim*\elem, -\mfmaNonKDim*\elem) + node [scale=.7*\mfmaNonKDim/32*\scale, pos=.5, fill=white, inner sep=0] {wave\waveId}; + } + + %% Draw the outline of each CTA rep + \draw [ultra thick] (CTA TL) rectangle ++(\CTASizeW*\elem, -\CTASizeH*\elem); + } + + \coordinate (TL) at (old TL); +} + +\newcommand{\drawMFMAOperand}[4]{ + %% + %% Draw one mfma operand + %% + %% mfma op TL: pre defined coordinates of the top-left + %% \elem: pre defined variable + %% + %% #1: mfmNonKDim + %% #2: kpack + %% #3: 0 for opA and 1 for opB + %% #4: verbose. 1 means draw tid in each vec; 0 means draw nothing + + \pgfmathsetmacro{\nonKDim}{#1} + \pgfmathsetmacro{\maxGID}{64/\nonKDim-1} + \pgfmathsetmacro{\maxTID}{\nonKDim-1} + \pgfmathsetmacro{\kpack}{#2} + \pgfmathsetmacro{\opIdxA}{#3} + \pgfmathsetmacro{\opIdxB}{1-\opIdxA} + \pgfmathsetmacro{\verbose}{#4} + + \foreach \col/\tg in {0,...,\maxGID}{ + \pgfmathsetmacro{\col}{\Colors[\tg]} + \foreach \tid in {0,...,\maxTID} { + % \pgfmathsetmacro{\ratio}{\tid*2.5+15} + \ifthenelse{\verbose=0}{ + \draw [line width=0.005mm, fill=\col] + ($(mfma op TL)+(\tg*\kpack*\elem*\opIdxB+\tid*\elem*\opIdxA, -\tid*\elem*\opIdxB-\tg*\kpack*\elem*\opIdxA)$) + rectangle ++(\kpack*\elem*\opIdxB + \elem*\opIdxA, -\elem*\opIdxB-\kpack*\elem*\opIdxA); + }{ + \pgfmathsetmacro{\drawTid}{int(\tid+\tg*\nonKDim)} + \draw [line width=0.005mm, fill=\col] + ($(mfma op TL)+(\tg*\kpack*\elem*\opIdxB+\tid*\elem*\opIdxA, -\tid*\elem*\opIdxB-\tg*\kpack*\elem*\opIdxA)$) + rectangle ++(\kpack*\elem*\opIdxB + \elem*\opIdxA, -\elem*\opIdxB-\kpack*\elem*\opIdxA) + node [pos=.5, scale=.35*\scale, rotate=90*\opIdxA] {t\drawTid}; + } + } + } +} + +\newcommand{\drawWaveOperand}[4]{ + %% + %% Draw the part of the tensor that is one operand of the wave + %% + %% Op TL: pre defined coordinates of the top-left of the operand + %% \elem: pre defined variable + %% + %% #1: K + %% #2: mfmNonKDim + %% #3: kpack + %% #4: 0 for opA and 1 for opB + + \pgfmathsetmacro{\K}{#1} + \pgfmathsetmacro{\nonKDim}{#2} + \pgfmathsetmacro{\groups}{64/\nonKDim} + \pgfmathsetmacro{\kpack}{#3} + \pgfmathsetmacro{\opIdx}{#4} + \pgfmathsetmacro{\opIdxOther}{1-\opIdx} + + \coordinate (TL) at (Op TL); + + \pgfmathsetmacro{\numKRep}{\K/\kpack/\groups} + \pgfmathsetmacro{\maxKRepId}{\numKRep-1} + + \foreach \repId in {0,...,\maxKRepId}{ + \coordinate (mfma op TL) at ($(TL)+(\repId*\groups*\kpack*\elem*\opIdxOther, -\repId*\groups*\kpack*\elem*\opIdx)$); + \drawMFMAOperand{\nonKDim}{\kpack}{\opIdx}{0} + \draw [thick] (mfma op TL) rectangle + ++(\groups*\kpack*\elem*\opIdxOther+\nonKDim*\opIdx*\elem, -\nonKDim*\opIdxOther*\elem-\groups*\kpack*\elem*\opIdx); + } +} + +\newcommand{\drawDotOperands}[7]{ + %% + %% Draw operand tensors of dot + %% + %% A TL and B TL: pre defined top-left coordinates of A and B tensor + %% \elem: pre defined variable + %% + %% #1: M + %% #2: N + %% #3: K + %% #4: MFMA nonKDim + %% #5: warpsPerCTA[0] + %% #6: warpsPerCTA[1] + %% #7: kpack + + \pgfmathsetmacro{\M}{#1} + \pgfmathsetmacro{\N}{#2} + \pgfmathsetmacro{\K}{#3} + \pgfmathsetmacro{\mfmaNonKDim}{#4} + \pgfmathsetmacro{\warpsPerCTAM}{#5} + \pgfmathsetmacro{\warpsPerCTAN}{#6} + \pgfmathsetmacro{\kpack}{#7} + + %% operand A + \pgfmathsetmacro{\CTARepM}{\M/\warpsPerCTAM/\mfmaNonKDim} + \pgfmathsetmacro{\maxCTAIdM}{\CTARepM-1} + \pgfmathsetmacro{\maxWaveId}{\warpsPerCTAM-1} + \foreach \ctaId in {0,...,\maxCTAIdM}{ + \coordinate (CTA TL) at ($(A TL)+(0, -\ctaId*\warpsPerCTAM*\mfmaNonKDim*\elem)$); + \foreach \waveId in {0,...,\maxWaveId}{ + \coordinate (wave TL) at ($(CTA TL)+(0, -\waveId*\mfmaNonKDim*\elem)$); + \draw [ultra thin] (wave TL) rectangle ++(\K*\elem, -\mfmaNonKDim*\elem); + } + %% Only draw the detailed view of the first wave in CTA + \coordinate (Op TL) at (CTA TL); + \drawWaveOperand{\K}{\mfmaNonKDim}{\kpack}{0} + + %% Draw the outline of each CTA rep + \draw [ultra thick] (CTA TL) rectangle ++(\K*\elem, -\warpsPerCTAM*\mfmaNonKDim*\elem); + } + \draw [ultra thin] (A TL) rectangle ++(\K*\elem, -\M*\elem); + + + %% operand B + \pgfmathsetmacro{\CTARepN}{\N/\warpsPerCTAN/\mfmaNonKDim} + \pgfmathsetmacro{\maxCTAIdN}{\CTARepN-1} + \pgfmathsetmacro{\maxWaveId}{\warpsPerCTAN-1} + \foreach \ctaId in {0,...,\maxCTAIdN}{ + \coordinate (CTA TL) at ($(B TL)+(\ctaId*\warpsPerCTAN*\mfmaNonKDim*\elem, 0)$); + \foreach \waveId in {0,...,\maxWaveId}{ + \coordinate (wave TL) at ($(CTA TL)+(\waveId*\mfmaNonKDim*\elem ,0)$); + \draw [ultra thin] (wave TL) rectangle ++(\mfmaNonKDim*\elem, -\K*\elem); + } + %% Only draw the detailed view of the first wave in CTA + \coordinate (Op TL) at (CTA TL); + \drawWaveOperand{\K}{\mfmaNonKDim}{\kpack}{1} + + %% Draw the outline of each CTA rep + \draw [ultra thick] (CTA TL) rectangle ++(\warpsPerCTAN*\mfmaNonKDim*\elem, -\K*\elem); + } + \draw [ultra thin] (B TL) rectangle ++(\N*\elem, -\K*\elem); +} + + +\newcommand{\drawDot}[8]{ + %% + %% Draw C = dot A, B + %% + %% C TL: pre defined top-left coordinates of the result tensor + %% \elem: pre defined variable + %% + %% #1: M + %% #2: N + %% #3: K + %% #4: MFMA nonKDim + %% #5: warpsPerCTA[0] + %% #6: warpsPerCTA[1] + %% #7: 1 for mfma.trans, 0 for normal mfma + %% #8: kpack + + \pgfmathsetmacro{\M}{#1} + \pgfmathsetmacro{\N}{#2} + \pgfmathsetmacro{\K}{#3} + \pgfmathsetmacro{\mfmaNonKDim}{#4} + \pgfmathsetmacro{\groups}{64/\mfmaNonKDim} + \pgfmathsetmacro{\warpsPerCTAM}{#5} + \pgfmathsetmacro{\warpsPerCTAN}{#6} + \pgfmathsetmacro{\mfmaTrans}{#7} + \pgfmathsetmacro{\kpack}{#8} + \pgfmathsetmacro{\kdim}{int(\groups*\kpack)} + + \pgfmathsetmacro{\gap}{\elem*20} + \coordinate (A TL) at ($(C TL)+(-\gap-\K*\elem, 0)$); + \coordinate (B TL) at ($(C TL)+(0, \gap+\K*\elem)$); + + \drawDotOperands{\M}{\N}{\K}{\mfmaNonKDim}{\warpsPerCTAM}{\warpsPerCTAN}{\kpack} + + \drawTensorMFMALayout{\M}{\N}{\mfmaNonKDim}{\warpsPerCTAM}{\warpsPerCTAN}{\mfmaTrans} + + %% Draw labels + \node [scale=\scale, above] at ($(A TL)+(.5*\K*\elem, 0)$) {K=\K}; + \node [scale=\scale, above, rotate=90] at ($(A TL)+(0, -.5*\M*\elem)$) {M=\M}; + + \node [scale=\scale, above, rotate=90] at ($(B TL)+(0, -.5*\K*\elem)$) {K=\K}; + \node [scale=\scale, above] at ($(B TL)+(.5*\N*\elem, 0)$) {N=\N}; + + \node [scale=\scale, above left] at (A TL) {A}; + \node [scale=\scale, above left] at (B TL) {B}; + \node [scale=\scale, above left] at (C TL) {C}; + + %% label nonKDim + \node [scale=.8*\scale, left] at ($(A TL)+(0, -.5*\mfmaNonKDim*\elem)$) {\mfmaNonKDim}; + \node [scale=.8*\scale, above] at ($(B TL)+(.5*\mfmaNonKDim*\elem, 0)$) {\mfmaNonKDim}; + %% label kpack + \node [scale=.8*\scale, above] at ($(A TL)+(0.5*\groups*\kpack*\elem, 0)$) {\kdim}; + \node [scale=.8*\scale, left] at ($(B TL)+(0, -0.5*\groups\kpack*\elem)$) {\kdim}; +} + +\newcommand{\Colors}{{ + "red", + "YellowGreen", + "blue", + "Maroon", + "orange", + "cyan", + "magenta", + "brown", + "teal", + "purple", + "gray", + "Green", + "BlueGreen", + "violet", + "olive", + "darkgray", + }} + +\newcommand{\drawTensorLayoutGlobalMem}{ + %% + %% Draw tensor layout in global memory without any swizzling + %% + %% TL: pre defined top-left coordinates of the tensor in global memory + %% \elem: per defined variable + %% \Colors: a pre defined array of 16 colors + %% + %% The following arguments are also expected to be pre defined + %% #1: M + %% #2: K + %% #3: vec: number of elements in a group + + \pgfmathsetmacro{\numVecK}{\K/\vec} + \pgfmathsetmacro{\maxVecId}{16*\numVecK-1} + \pgfmathsetmacro{\drawM}{20} + + %% Draw the tensor, but only draw 32 rows + \draw (TL) rectangle ++(\K*\elem, -\drawM*\elem); + %% Draw detailed vec view of the tensor + \foreach \vecId in {0,...,\maxVecId}{ + + \pgfmathsetmacro{\vecCoordM}{int(\vecId/\numVecK)} + \pgfmathsetmacro{\vecCoordK}{mod(\vecId,\numVecK)} + \coordinate (vec TL) at ($(TL)+(\vecCoordK*\vec*\elem, -\vecCoordM*\elem)$); + + \pgfmathsetmacro{\colorIdxK}{int(mod(\vecCoordK,16))} + \pgfmathsetmacro{\colorIdxM}{mod(\vecCoordM,16)} + \pgfmathsetmacro{\vecColor}{\Colors[\colorIdxK]} + \pgfmathsetmacro{\ratio}{100-floor(\vecCoordK/16)*40} + + \draw [ultra thin, fill=\vecColor!\ratio!white] (vec TL) rectangle ++(\vec*\elem, -\elem) + node [pos=.5, scale=.6*\scale, white] {m\vecCoordM}; + + } + %% M and K dim + \node [scale=\scale, rotate=90, above] at ($(TL)+(0, -.5*\drawM*\elem-8*\elem)$) {M=\M}; + \node [scale=.8*\scale, left] at ($(TL)+(0, -.5*16*\elem)$) {16}; + \node [scale=\scale, above] at ($(TL)+(.5*\K*\elem, 0)$) {K=\K}; + %% label for vecSize + \def\vecR{1.5} + \coordinate (vec TL) at ($(TL)+(-.25*\vec*\elem, 3*\elem*\vecR)$); + \pgfmathsetmacro{\maxVec}{\vec-1} + \foreach \vecId in {0,...,\maxVec}{ + \draw ($(vec TL)+(\vecId*\elem*\vecR, 0)$) rectangle ++(\elem*\vecR, -\elem*\vecR); + } + \draw [densely dotted] (TL) -- ($(vec TL)+(0, -\elem*\vecR)$); + \draw [densely dotted] ($(TL)+(\vec*\elem, 0)$) -- ($(vec TL)+(\vec*\elem*\vecR, -\elem*\vecR)$); + \node [scale=.8*\scale, above] at ($(vec TL)+(.5*\vec*\elem*\vecR, 0)$) {vec=\vec}; +} + + + +\newcommand{\drawLDSLayoutTritonSwizzling}[2]{ + %% + %% Draw tensor layout in LDS with swizzling + %% + %% TL: pre defined top-left coordinates of the tensor in global memory + %% \elem: per defined variable + %% \Colors: a pre defined array of 16 colors + %% + %% The following three arguments are expected to be pre defined + %% #1: M + %% #2: K + %% #3: vec: number of elements in a group + %% + %% #1: hasSwizzle, 0 means no swizzling and no padding, + %% 1 means optimal swizzling + %% 2 means padding + %% #2: access mode, 0 means draw nothing, 1 means ds_read, 2 means ds_write + %% For ds_write access, the following variables are assumed to be pre defined + %% \sizePerThreadK + %% \sizePerThreadM + %% \threadsPerWarpK + + \pgfmathsetmacro{\hasSwizzle}{#1} + \pgfmathsetmacro{\accessMode}{#2} + \pgfmathsetmacro{\numVecK}{\K/\vec} + + %% Assuming fp16 data type + \pgfmathsetmacro{\LDSK}{64} + \pgfmathsetmacro{\numLDSVec}{\LDSK/\vec} + \pgfmathsetmacro{\swizzleK}{max(\LDSK, \K)} + \pgfmathsetmacro{\LDSM}{int(\M/\LDSK*\K)} + + \ifthenelse{\accessMode = 2}{ + %% \accessMode == 2, draw 8 rows + \pgfmathsetmacro{\maxVecId}{8*\numVecK-1} + \pgfmathsetmacro{\drawM}{8*\K/\LDSK+4} + }{ + %% \accessMode == 0 or 1, draw 16 rows + \pgfmathsetmacro{\maxVecId}{16*\numVecK-1} + \pgfmathsetmacro{\drawM}{16*\K/\LDSK+4} + } + + %% Parameters used for swizzling + \pgfmathsetmacro{\numVecSwizzleK}{\swizzleK/\vec} + %% perPhase = ceil(LDSK / K) + %% The number of the rows of the tensor that can share the same swizzling pattern + \pgfmathsetmacro{\perPhase}{ceil(\LDSK/\K)} + %% maxPhase: the total number of different swizzling patterns + \ifthenelse{\hasSwizzle=0}{ + %% When swizzling is disabled + \pgfmathsetmacro{\maxPhase}{1} + }{ + %% When vec is small enough, we want 16/perPhase different swizzling patterns + %% When vec is large, we can only have 64 / \vec different swizzling pattern at most + \pgfmathsetmacro{\maxPhase}{min(16/\perPhase,64/\vec)} + } + + %% Draw the LDS + \draw (TL) rectangle ++(\LDSK*\elem, -\drawM*\elem); + + %% Draw detailed vec view of LDS + \foreach \vecId in {0,...,\maxVecId}{ + \pgfmathsetmacro{\vecCoordM}{int(\vecId/\numVecK)} + \pgfmathsetmacro{\vecCoordK}{int(mod(\vecId,\numVecK))} + \pgfmathsetmacro{\rawPhase}{floor(\vecId/\numVecSwizzleK)} + %% vec color + \pgfmathsetmacro{\colorIdxK}{int(mod(\vecCoordK,16))} + \pgfmathsetmacro{\colorIdxM}{mod(\vecCoordM,16)} + \pgfmathsetmacro{\ratio}{100-floor(\vecCoordK/16)*40} + \pgfmathsetmacro{\vecColor}{\Colors[\colorIdxK]} + + %% old vec coordinates + \coordinate (vec TL) at ($(TL)+(\vecCoordK*\vec*\elem, -\vecCoordM*\elem)$); + + %% new vec coordinates in LDS by swizzling + %% The following two conditions correspond to the relation between \LDSK and \K + \ifthenelse{\LDSK < \K}{ + \pgfmathsetmacro{\vecLDSM}{\vecCoordM*\K/\LDSK+floor(\vecCoordK*\vec/\LDSK)} + \pgfmathsetmacro{\vecLDSK}{int(mod(\vecCoordK, \LDSK/\vec))} + }{ + \pgfmathsetmacro{\vecLDSM}{floor(\vecCoordM/\perPhase)} + \pgfmathsetmacro{\vecLDSK}{int(\vecCoordK+mod(\vecCoordM,\perPhase)*\numVecK)} + } + %% + \pgfmathsetmacro{\phase}{int(mod(\rawPhase, \maxPhase))} + %% Compute the swizzled col id + \pgfmathsetmacro{\vecLDSKSwizzled}{\bitwiseXor{\vecLDSK}{\phase}} + + %% new vec coordinates in LDS by padding + \pgfmathsetmacro{\numPads}{floor(\vecId/\numLDSVec)} + \pgfmathsetmacro{\bankId}{\vec/2*\vecId+\numPads} + \pgfmathsetmacro{\vecPadM}{int(\bankId/32)} + \pgfmathsetmacro{\vecPadK}{int(mod(\bankId,32))} + + \ifthenelse{\hasSwizzle = 2}{ + %% vec coordinates by padding + \coordinate (new vec TL) at ($(TL)+(\vecPadK*2*\elem, -\vecPadM*\elem)$); + \pgfmathsetmacro{\tailBankId}{int(\vecPadK+\vec/2-1)} + }{ + %% vec coordinates by swizzling + \coordinate (new vec TL) at ($(TL)+(\vecLDSKSwizzled*\vec*\elem, -\vecLDSM*\elem)$); + \pgfmathsetmacro{\tailBankId}{0} + } + + \ifthenelse{\hasSwizzle = 2 \AND \tailBankId > 31}{ + \pgfmathsetmacro{\nextBanks}{\tailBankId-31} + \pgfmathsetmacro{\leftBanks}{\vec/2 - \nextBanks} + \draw [ultra thin, fill=\vecColor!\ratio!white] (new vec TL) rectangle ++(\leftBanks*2*\elem, -\elem) + node [pos=.5, scale=.6*\scale, white] {m\vecCoordM}; + \draw [ultra thin, fill=\vecColor!\ratio!white] ($(TL)+(0, -\vecPadM*\elem-\elem)$) + rectangle ++(\nextBanks*2*\elem, -\elem) node [pos=.5, scale=.6*\scale, white] {m\vecCoordM}; + }{ + \draw [ultra thin, fill=\vecColor!\ratio!white] (new vec TL) rectangle ++(\vec*\elem, -\elem) + node [pos=.5, scale=.6*\scale, white] {m\vecCoordM}; + } + + %% ds_read + %% Highlight the elements the first 16 threads access in the first cycle + %% This is used to visualize bank conflicts + \ifthenelse{\accessMode = 1}{ + \ifthenelse{\vecCoordK = 0}{ + \draw [fill=white] (new vec TL) rectangle ++(\elem, -\elem); + \draw (new vec TL) -- ++(\elem, -\elem); + \draw ($(new vec TL)+(0, -\elem)$) -- ++(\elem, \elem); + }{} + }{} + + %% Draw ds_write pattern + \ifthenelse{\accessMode = 2}{ + %% First compute the coverage of the first 16 threads + \pgfmathsetmacro{\covK}{min(16, \threadsPerWarpK)*\sizePerThreadK/\vec} + \pgfmathsetmacro{\covM}{ceil(16/\threadsPerWarpK)*\sizePerThreadM} + %% Check conditions for the first 16 threads + \pgfmathsetmacro{\vecInThread}{int(mod(\vecCoordK, \sizePerThreadK/\vec))} + \ifthenelse{\vecInThread=0}{ + \ifthenelse{\vecCoordK<\covK \AND \vecCoordM<\covM}{ + \draw [fill=white] (new vec TL) rectangle ++(\elem, -\elem); + \draw (new vec TL) -- ++(\elem, -\elem); + \draw ($(new vec TL)+(0, -\elem)$) -- ++(\elem, \elem); + }{} + }{} + }{} + + %% Label the phase of each line if swizzling is used + \ifthenelse{\hasSwizzle = 2}{}{ + \pgfmathsetmacro{\lastVecId}{int(64/\vec)-1} + \ifthenelse{\vecLDSKSwizzled = \lastVecId}{ + \draw [ultra thin] ($(new vec TL)+(\vec*\elem, -.5*\elem)$) -- ++(\elem, 0) + node [scale=.6*\scale, right] {\phase}; + }{} + } + } + + %% Draw boundary of 32 banks + %% Assume fp16 data type + \foreach \bank in {0,...,31}{ + \draw [ultra thin, gray] ($(TL)+(\bank*2*\elem, 0)$) -- ++(0, 2*\elem) + node [scale=.6*\scale, right, black] {\bank}; + } + \draw [ultra thin, gray] ($(TL)+(32*2*\elem, 0)$) -- ++(0, 2*\elem); + \node [scale=.6*\scale, left, black] at ($(TL)+(0, 2*\elem)$) {bank id}; + + \node [scale=\scale, above] at ($(TL)+(.5*\LDSK*\elem, 3*\elem)$) {LDS 32 banks}; + \node [scale=\scale, rotate=90, above] at ($(TL)+(0, -.5*\drawM*\elem)$) {LDSM=\LDSM}; + + %% label phase if swizzling is used + \ifthenelse{\hasSwizzle = 2}{}{ + \node [scale=.6*\scale, above right] at($(TL)+(32*2*\elem, 0)$) {phase}; + } +} + +\newcommand{\drawMFMAInstr}[3]{ + %% + %% Draw layout of mfma instructions with tid labeled + %% + %% C TL: pre defined top-left coordinates of the output matrix + %% \elem: pre defined variable + %% + %% #1: mfmaNonKDim + %% #2: kpack + %% #3: mfmaTrans + \pgfmathsetmacro{\mfmaNonKDim}{#1} + \pgfmathsetmacro{\groups}{64/\mfmaNonKDim} + \pgfmathsetmacro{\kpack}{#2} + \pgfmathsetmacro{\mfmaTrans}{#3} + \pgfmathsetmacro{\nonTrans}{1-#3} + + \pgfmathsetmacro{\gap}{\elem*5} + \coordinate (mfma opA TL) at ($(C TL)+(-.5*\gap-1.2*\nonTrans*\gap-\groups*\kpack*\elem, 0)$); + \coordinate (mfma op TL) at (mfma opA TL); + \drawMFMAOperand{\mfmaNonKDim}{\kpack}{0}{1} + \coordinate (mfma op TL) at ($(C TL)+(0, 1.5*\gap+.5*\mfmaTrans*\gap+\groups*\kpack*\elem)$); + \drawMFMAOperand{\mfmaNonKDim}{\kpack}{1}{1} + + \coordinate (block TL) at (C TL); + \drawBlockMFMALayoutLarge{\mfmaTrans}{\mfmaNonKDim}{1} + + %% Draw labels + \def\vecR{1.5} + \coordinate (vec TL) at ($(mfma opA TL)+(-.25*\kpack*\elem, 3*\elem*\vecR)$); + \pgfmathsetmacro{\maxVec}{\kpack-1} + \foreach \vecId in {0,...,\maxVec}{ + \draw ($(vec TL)+(\vecId*\elem*\vecR, 0)$) rectangle ++(\elem*\vecR, -\elem*\vecR); + } + \draw [densely dotted] (mfma opA TL) -- ($(vec TL)+(0, -\elem*\vecR)$); + \draw [densely dotted] ($(mfma opA TL)+(\kpack*\elem, 0)$) -- ($(vec TL)+(\kpack*\elem*\vecR, -\elem*\vecR)$); + \node [scale=.8*\scale, above] at ($(vec TL)+(.5*\kpack*\elem*\vecR, 0)$) {vec=\kpack}; + + \coordinate (vec TL) at ($(mfma op TL)+(-3*\elem*\vecR, .25*\kpack*\elem)$); + \foreach \vecId in {0,...,\maxVec}{ + \draw ($(vec TL)+(0, -\vecId*\elem*\vecR)$) rectangle ++(\elem*\vecR, -\elem*\vecR); + } + \draw [densely dotted] (mfma op TL) -- ($(vec TL)+(\elem*\vecR,0)$); + \draw [densely dotted] ($(mfma op TL)+(0, -\kpack*\elem)$) -- ($(vec TL)+(\elem*\vecR, -\kpack*\elem*\vecR)$); + \node [scale=.8*\scale, above, rotate=90] at ($(vec TL)+(0, -.5*\kpack*\elem*\vecR)$) {vec=\kpack}; + + \node [scale=\scale, below] at ($(block TL)+(.5*\mfmaNonKDim*\elem,-\mfmaNonKDim*\elem)$) {outC}; + \ifthenelse{\mfmaTrans=0}{ + \node [scale=\scale, below] at ($(mfma opA TL)+(\kpack*\elem, -\mfmaNonKDim*\elem)$) {opA}; + \node [scale=\scale, above] at (mfma op TL) {opB}; + \coordinate (vec TL) at ($(block TL)+(-3*\elem-\elem*\vecR, .25*4*\elem)$); + \foreach \vecId in {0,1,2,3}{ + \draw ($(vec TL)+(0, -\vecId*\elem*\vecR)$) rectangle ++(\elem*\vecR, -\elem*\vecR); + } + \draw [densely dotted] (block TL) -- ++(-3*\elem, .25*4*\elem); + \draw [densely dotted] ($(block TL)+(0, -4*\elem)$) -- ++(-3*\elem, -.25*4*\elem); + \node [scale=.8*\scale, above, rotate=90] at ($(vec TL)+(0, -.5*4*\elem*\vecR)$) {vec=4}; + \node [scale=.8*\scale, above, align=center] at ($(block TL)+(.5*\mfmaNonKDim*\elem, 0)$) {mfmaLayout\\trans=False}; + }{ + \node [scale=\scale, below] at ($(mfma opA TL)+(\kpack*\elem, -\mfmaNonKDim*\elem)$) {opB}; + \node [scale=\scale, above] at (mfma op TL) {opA}; + \coordinate (vec TL) at ($(block TL)+(-.25*4*\elem, 3*\elem+\elem*\vecR)$); + \foreach \vecId in {0,1,2,3}{ + \draw ($(vec TL)+(\vecId*\elem*\vecR, 0)$) rectangle ++(\elem*\vecR, -\elem*\vecR); + } + \draw [densely dotted] (block TL) -- ++(-.25*4*\elem, 3*\elem); + \draw [densely dotted] ($(block TL)+(4*\elem, 0)$) -- ++(.25*4*\elem, 3*\elem); + \node [scale=.8*\scale, above] at ($(vec TL)+(.5*4*\elem*\vecR, 0)$) {vec=4}; + \node [scale=.8*\scale, above, align=center] at ($(block TL)+(16*\elem, 0)$) {mfmaLayout\\trans=True}; + } +} + +\newcommand{\drawWMMAOperand}[3]{ + %% + %% Draw the layout of one operand of WMMA instruction + %% + %% #1: opIdx. 0 for opA, 1 for opB + %% #2: verbose. 1 means draw tid in each vec; 0 means draw nothing + %% #3: mode. 0 for w32, 1 for w64 + %% + %% wmma op TL: pre defined top-left coordinates of the operand matrix + + \pgfmathsetmacro{\isOpB}{#1} + \pgfmathsetmacro{\isOpA}{1-\isOpB} + \pgfmathsetmacro{\verbose}{#2} + \pgfmathsetmacro{\isWLarge}{#3} + + \foreach \row in {0,...,15}{ + \pgfmathsetmacro{\ratio}{\row*5+15} + \coordinate (vec TL) at ($(wmma op TL)+(\row*\isOpB*\elem, -\row*\elem*\isOpA)$); + \ifthenelse{\isWLarge=1}{ + \pgfmathsetmacro{\tidone}{int(\row+16)} + \pgfmathsetmacro{\tidtwo}{int(\row+32)} + \pgfmathsetmacro{\tidthree}{int(\row+48)} + \draw [line width=0.005mm, fill=brown!\ratio!white] (vec TL) + rectangle ++(16*\elem*\isOpA+\elem*\isOpB, -\elem*\isOpA-16*\elem*\isOpB) + node [scale=0.4*\scale, pos=.5, rotate=90*\isOpB] {t\row, t\tidone, t\tidtwo, t\tidthree}; + }{ + \pgfmathsetmacro{\tidone}{int(\row+16)} + \draw [line width=0.005mm, fill=brown!\ratio!white] (vec TL) + rectangle ++(16*\elem*\isOpA+\elem*\isOpB, -\elem*\isOpA-16*\elem*\isOpB) + node [scale=0.4*\scale, pos=.5, rotate=90*\isOpB] {t\row, t\tidone}; + } + } +} + +\newcommand{\drawWMMAResult}[2]{ + %% + %% Draw layout of WMMA result tensor + %% + %% #1: verbose. 1 means draw tid in each vec; 0 means draw nothing + %% #2: mode. 0 for w32, 1 for w64 + + \pgfmathsetmacro{\verbose}{#1} + \pgfmathsetmacro{\isWLarge}{#2} + + \pgfmathsetmacro{\numElem}{256} + \pgfmathsetmacro{\maxElemId}{\numElem-1} + + \foreach \elemId in {0,...,\maxElemId}{ + %% figure out the rowID + \pgfmathsetmacro{\rowId}{floor(\elemId/16)} + %% figure out the colID + \pgfmathsetmacro{\colId}{mod(\elemId,16)} + %% figure out the tid and color + \ifthenelse{\isWLarge=1}{ + \pgfmathsetmacro{\tid}{int(mod(\elemId,64))} + \pgfmathsetmacro{\laneId}{mod(\elemId,64)} + }{ + \pgfmathsetmacro{\tid}{int(mod(\elemId,32))} + \pgfmathsetmacro{\laneId}{mod(\elemId,32)} + } + %% figure out the color + \pgfmathsetmacro{\colorId}{floor(\laneId/16)} + \pgfmathsetmacro{\vecColor}{\Colors[\colorId]} + %% Coordinate + \coordinate (vec TL) at ($(C TL)+(\colId*\elem, -\rowId*\elem)$); + \draw [line width=0.005mm, fill=\vecColor!60!white] (vec TL) rectangle ++(\elem, -\elem) + node [scale=.4*\scale, pos=.5] {t\tid}; + } + + +} + +\newcommand{\drawWMMAInstr}[2]{ + %% + %% Draw wmma instruction layouts 16x16x16 + %% + %% #1: mode. 0 for w32, 1 for w64 + %% #2: verbose. 1 means draw tid in each vec; 0 means draw nothing + %% + %% C TL: pre defined top-left coordinates of output matrix + %% \elem: pre defined element size + + + \pgfmathsetmacro{\isWLarge}{#1} + \pgfmathsetmacro{\verbose}{#2} + + \pgfmathsetmacro{\gap}{\elem*2} + \coordinate (wmma op TL) at ($(C TL)+(-\gap-16*\elem, 0)$); + \coordinate (wmma opA TL) at (wmma op TL); + \drawWMMAOperand{0}{\verbose}{\isWLarge} + \coordinate (wmma op TL) at ($(C TL)+(0, \gap+16*\elem)$); + \drawWMMAOperand{1}{\verbose}{\isWLarge} + + \drawWMMAResult{1}{\isWLarge} + + %% labels + \pgfmathsetmacro{\gap}{\elem} + \node [above left, scale=\scale] at (wmma opA TL) {A}; + \node [above left, scale=\scale] at (wmma op TL) {B}; + \node [above right, scale=\scale] at ($(C TL)+(16*\elem, 0)$) {C}; + + %% A k dim + \node [scale=.8*\scale] (k dim A) at ($(wmma opA TL)+(8*\elem,\gap)$) {16}; + \draw [->, >=stealth] (k dim A.west) -- ($(wmma opA TL)+(0, \gap)$); + \draw [->, >=stealth] (k dim A.east) -- ($(wmma opA TL)+(16*\elem, \gap)$); + + %% B K dim + \node [scale=.8*\scale, rotate=90] (k dim B) at ($(wmma op TL)+(-\gap, -8*\elem)$) {16}; + \draw [->, >=stealth] (k dim B.east) -- ($(wmma op TL)+(-\gap, 0)$); + \draw [->, >=stealth] (k dim B.west) -- ($(wmma op TL)+(-\gap, -16*\elem)$); + + %% C M dim + \node [scale=.8*\scale] (m dim) at ($(C TL)+(8*\elem,-16*\elem-\gap)$) {16}; + \draw [->, >=stealth] (m dim.west) -- ($(C TL)+(0, -16*\elem-\gap)$); + \draw [->, >=stealth] (m dim.east) -- ($(C TL)+(16*\elem, -16*\elem-\gap)$); + + %% C N dim + \node [scale=.8*\scale, rotate=-90] (n dim) at ($(C TL)+(16*\elem+\gap, -8*\elem)$) {16}; + \draw [->, >=stealth] (n dim.west) -- ($(C TL)+(16*\elem+\gap, 0)$); + \draw [->, >=stealth] (n dim.east) -- ($(C TL)+(16*\elem+\gap, -16*\elem)$); +} diff --git a/python/perf-kernels/tools/tune_gemm/README.md b/python/perf-kernels/tools/tune_gemm/README.md new file mode 100644 index 000000000000..c22382143544 --- /dev/null +++ b/python/perf-kernels/tools/tune_gemm/README.md @@ -0,0 +1,348 @@ +# GEMM tuning script (current v3.4) + +## matmul kernel + +The matmul kernel implementation can be found as [matmul_kernel.py](https://github.com/ROCm/triton/blob/main_perf/python/perf-kernels/tune_gemm/matmul_kernel.py), which includes the following features: +- XCD-based pid remapping +- grouping order of workgroup id, which is controlled by `GROUP_SIZE_M`, that +implements L2 cache optimization introduced in the [tutorial](https://triton-lang.org/main/getting-started/tutorials/03-matrix-multiplication.html#l2-cache-optimizations). +- split-k algorithm, which is controlled by `SPLIT_K`. +- Bias along M dim, which is controlled by `BIAS` and `bias_ptr`. +- Masked load along K dim inside the loop, which is controlled by `EVEN_K`. +This means `BLOCK_SIZE_K` does not need to divide K dim. + +### Differences between the tutorial + +Unlike the [matmul tutorial](https://github.com/triton-lang/triton/blob/main/python/tutorials/03-matrix-multiplication.py) (referred as the tutorial), +the matmul kernel used in the tuning script (referred as the kernel) does not +guard load along M and N dim +([this](https://github.com/triton-lang/triton/blob/main/python/tutorials/03-matrix-multiplication.py#L282-L283) shows how this is done in the tutorial). +When `BLOCK_SIZE_M` or `BLOCK_SIZE_N` does not divide M or N, the kernel will +load out-of-bound data. +In most cases this is fine, since the kernel does masked store at the end. +However, this may lead to GPU memory access fault in some cases, especially +when the tensor is large. +We will fix this issue in the future. + + +## Tuning script usage + +### Tuning mode + +The tuning script can take one or more gemm sizes and run tuning for them. +The input gemm sizes are prepared in a yaml file. Here is an example yaml file: +```yaml +- {'M': 4864, 'N': 4096, 'K': 8256, 'rowMajorA': 'T', 'rowMajorB': 'N'} +- {'M': 512, 'N': 512, 'K': 512, 'rowMajorA': 'T', 'rowMajorB': 'N'} +``` + +The tuning script works as follows +```python +./tune_gemm.py --gemm_size_file input.yaml [options] +``` +The following `options` are supported in the tuning mode + +- Input data types: + - `-dtype_a dtype`, `-dtype_b dtype`, and `-dtype_c dtype`: input and output element type. + - Supported `dtype`: fp16 (default), bf16, fp8, bf8, int8, int32, fp32 +- Parallel compilation of kernels: + - `--num_threads n` controls that n threads will + be used in the compilation stage. The default value is 32. + - `--no_warmup` can be used to skip the compilation stage. Thus kernels will be + compiled during the profiling stage. This increases tuning time. But it's + required for some old torch version, in which some function used in the warmup + kernel launch is not supported. +- Parallel profiling of kernels: The tuning space is first divided into a number +of tasks, which is controlled by `--jobs n`. And all the tasks can be profiled in +parallel on a number of GPUs in the system. There are two ways to specify which +GPU(s) we want to use for profiling. Note that these flags cannot be use together. +By default, only one task is generated and profiled on GPU0. + - `--ngpus n`: GPU 0,1,.., n-1 will be used. + - `--gpu_ids ids`: `ids` are comma separated gpu ids and GPUs in `ids` will be used. +- General tuning control flags + - `--init_type INIT_TYPE` defines how input data are initialized. `INIT_TYPE` can be + - hpl: uniform distribution between -.5 and .5 + - trig_float: the distribution of elements in the flattened tensor follow + the `sin` function. + - zeros: initialize all data as 0, i.e. `torch.zeros` + - randn (default): normal distribution, i.e. `torch.randn` + - `--rotating_tensor SIZE`: provide the size of memory used for rotating tensor. + The default is 0, meaning rotating tensor is not used. + - `--icahe_flush`: If true, the script will generate a kernel to flush i-cache. + The default is False. + - `--bias_vector`: If true, a bias vector along the M dim is applied. + The default is False. +- Correctness check + - `--compare` will check the correctness of the best config for each gemm size. + - `--compare_wo_tuning` will check the correctness of the config provided in + the yaml file. If this is set, user needs to provide all the parameters in + the input yaml file. Example can be found in the benchmark mode section. +- Logistics + - `--keep` can be used to keep the files generated during the tuning process. + Be default, intermediate files are removed at the end. + - `--time_breakdown`: If set, the script will print out elapsed time during + each stage of the tuning in real-time. The default is False. + - `--verbose` will enable more logging message than `--time_breakdown`, such + as output from rocprofv2 + - `--o OUTPUT` can be used to control the output filename to store the tuning + result. The default filename is `tuning_results_branchName@gitCommit_timeStamp.yaml`. + Therefore, each time the user runs the tuning script, a different output file + will be generated. +- Hacks + - `--hack_triton_compiler`: If set, the triton source code will be modified + to provide a static backend target so that the compiler will not query + GPU information. This makes sure that during the compilation stage, no + hip runtime kernels are launched. + Note that this is a very hacky option, because + - It modifies the triton compiler directly, which is located from + `pip show triton`. + - It does string match and replace to modify the code. + - It does not restore the code when the tuning session terminates. + +Here are some example usages of running the script for tuning: + +Tune some gemm sizes with f16 input +```python +./tune_gemm.py --gemm_size_file input.yaml --ngpus 8 --jobs 32 --o output.yaml +``` +It's recommended to use as many GPUs as possible and set `--jobs` to +a value that is 4 to 6 times the number of GPUs. + +If you are only allowed to use a subset of the GPUs, you can +```python +./tune_gemm.py --gemm_size_file input.yaml --gpu_ids 0,1,3,4 --jobs 32 --o output.yaml +``` +This runs the profiling on GPU 0,1,3,4. + +For bf8 input +```python +./tune_gemm.py --gemm_size_file input.yaml --ngpus 8 --jobs 32 -dtype_a bf8 -dtype_b bf8 +``` + +Check correctness of the tuned configs +```python +./tune_gemm.py --gemm_size_file output.yaml --compare_wo_tuning +``` + + +### Benchmark mode + +In benchmark mode, the script will run a single given config multiple times to +collect performance data. The benchmark mode works as +The tuning script works as follows +```python +./tune_gemm.py --gemm_size_file input.yaml [options] --benchmark +``` +The supported `options` are as followings +- `-dtype_a dtype`, `-dtype_b dtype`, and `-dtype_c dtype`: same as tuning mode. +- `--iters n` controls the number of iterations to run the kernel. +The default value is 1000. +- `--icache_flush`: same as tuning mode +- `--rotating_tensor SIZE`: same as tuning mode + + +## Tuning script implementation overview + +The general idea of the tuning script can be summarized as +- Compile all the kernels in the tuning space in parallel. +- Divide the tuning space into tasks and invoke `rocprof` once per +task. This will save invocation overhead of the profiler. +- Profile tasks in parallel on multiple GPUs. + +For detailed implementation, please refer to the changelog of each version. + +### Dependency graph + +The following graph depicts the dependency between Python modules: +```mermaid +graph TD; + one_config.py --> tune_gemm.py + tune_gemm.py --> matmul_kernel.py + tune_gemm.py --> utils/file_generator.py + tune_gemm.py --> utils/utils.py + utils/file_generator.py --> utils/utils.py + utils/file_generator.py -.-> icache_flush.py +``` + +`utils/file_generator.py` doesn't import `icache_flush.py` but it generates kernels that can import +`icache_flush.py`. + + +# Changelog + +## GEMM tuning script v1 + +Shucai (@scxiao) implemented the first version of gemm tuning script: https://github.com/ROCmSoftwarePlatform/triton/pull/309 + +## GEMM tuning script v2 + +This version is based on v1 and @alefimov-amd's thread pool https://github.com/ROCmSoftwarePlatform/triton/pull/310 + +### Main features +- `rocprof` is used to measure the time for kernels in the full tuning space +- Each kernel is executed 10 times and the execution time of the last instance is used +- All kernels are compiled in parallel +- Two modes for correctness checking + - During tuning, check correctness with the best perf_config for the current gemm size + - Without tuning, check correctness based on the tuning results, which includes best perf_config for each gemm size +- The process takes about 30 - 40 minutes for the full tuning space with ~15000 configs +- Limitations + - For now, only support fp16 as inputs. It should be trivial to extend to other types, but may require some work for mixed inputs + +### Overview of implementations + +Workflow of the tuning process +1. Generate the full tuning space. For now the `range`s for each tuning parameter are hard-coded +2. Prune the tuning space according to the current GEMM size and some rules + - BLOCK_SIZE must be equal or larger than the mfma instruction size. + - SPLIT_K * BLOCK_SIZE_K must divide K. Therefore, we do not need EVEN_K in the kernel. + - When split-k is not needed, i.e. both M and N are large, it must be 1 + - GROUP_M * BLOCK_SIZE_M must be smaller than M. Otherwise, GROUP_M must be 1 + - When BLOCK_SIZE_K = 128, neither BLOCK_SIZE_M or BLOCK_SIZE_N can be 128. Otherwise too much LDS will be required. **Needs further investigation** + - Skip BLOCK_SIZE_M or BLOCK_SIZE_N if they are over 2 times larger than M or N. +3. Open a file `generated_kernel{M}-{N}-{K}-{gpuid}.py` and write the following into the file + 1. For each config in the pruned space, generate a kernel with name `matmul_kernel_{configStr}`, where `configStr` contains the gemm size and the tuning parameters. + 2. Generate `matmul` function for each config in a similar way + 3. Generate `try_config` functions for each `matmul` function. + 4. Generate `test_gemm`, which does + 1. Add all `try_config` functions in the thread_pool by `thread_pool.apply_async(try_config)`. This is used to compile all kernels in parallel. + 2. Call each `matmul` function in a for loop of 10 iterations + 5. Generate `main` function +4. Run the generated script with 16 workers. This will compile all kernels in parallel. +5. Invoke `rocprof` on the generated script +6. Post process `results.csv` by extract the execution time of the last instance of each kernel. Pick the best one, write to file, and return. + +## GEMM Tuning Script v3 + +### API changes + +- Input and output data types can be provided as `-dtype_a`, `-dtype_b`, and `-dtype_c`. +The provided types must be one of ['fp32', 'fp16', 'bf16', 'fp8', 'bf8', 'int8']. +- Row/col major-ness of operand a and b can be provided as `-col_a` and `-col_b`. +If set, it means the corresponding operand is column major. +The major-ness is considered as problem input. +So they should be included in the input yaml file. However, in the yaml file, user should +set `rowMajowA` and `rowMajorB` as shown in the example below. +- `--benchmark` is used to control if the perf config in the input yaml file is used as the tuning space. +- `--jobs` is used to control the number of .py files for generated kernels. +Note that this can be different from `ngpus`. This usually means multiple kernel files +will be profiled on each GPU. +This is necessary to keep each file "small" in terms of execution time. + +### Implementation changes +- `gen_input` is used to generate matmul inputs. +- Time measurement + - In benchmark mode, the kernel is executed 1000 times. + - In tuning mode, each kernel is executed 200 times. We cannot afford to larger runs since rocprof hangs if the session takes too long. + - In both tuning and benchmark mode, kernel time is measured as the average execution time of the last 100 instances. +- Added error recovery. This helps when rocprof crashes in multi-processing mode. + + + +## GEMM Tuning Script v3.1 + +### API changes + +- Added `matrix_instr_nonkdim` into the tuning space. Now we can tune mfma instruction size. + + +## GEMM Tuning Script v3.2 + +### API changes + +- Added `--rotating_tensor ` to use rotating memory blocks in each iteration, size in MB. Default is 0MB. +- Added `--icache_flush` to flush icache in each iteration. +Note, icache flush needs the module `python-hip`, which can be installed as: +`python3 -m pip install -i https://test.pypi.org/simple hip-python~=$rocm_version` +Rotating tensor and icache flush are to make perf numbers are closer to that in real applications. +- Added `--bias_vector` to support kernel execution with bias (bias vector is of the same size as the number of rows of the output matrix, +so each element of the bias vector is added to all elements of the corresponding row of the output matrix.) + + +## GEMM Tuning Script v3.3 + +### API changes + +no API changes + +### Implementation changes + +- We use a dedicated file (named `get_filename_myKernels()`) to keep all the kernels +in the tuning space. +- Inside the for loop of tuning, each iteration tunes one gemm size + 1. Update kernel stage: Different gemm size may need different configs. We keep track + of the current tuning space. And if the current gemm size needs some configs that is + not included in the current tuning space, we expand the tuning space with the newly + added configs. + - This means if two gemm sizes share some configs, these configs will be compiled + once. This will greatly reduce batch tuning time. + 2. Compilation stage: + - We generate a single compilation driver file, named compile_driver.py (this is + obtained from `get_filename_compile_driver`) which contains the wrapper functions + of all the configs in the **pruned** tuning space for this gemm size. + - All the kernels will be compiled by 32 threads by default. Compiling all the + kernels in a single file in parallel is faster than splitting them into multiple + files. This can greatly reduce the compile time of the tuning process. + - Note that we no longer generate matmul_kernel in this file. Kernels are imported + from myKernels.py. + 3. Profile stage + - We generate one task file per job, named `profile_driver_MxNxK_{job_id}.py` + (this is obtained from `get_filename_profile_driver`). The only difference is + that we no longer generate matmul_kernel in this file. Kernels are imported + from myKernels.py. +- `configStr` does not contain gemm size anymore. This allows the same matmul_{configStr} +kernel to be reused by different gemm sizes. +- `configStr` does not contain `_bias` if bias is provided. This is because we do not +expect to compare the same kernel w/ and w/o bias. Therefore, we treat bias in the same +way as gemm sizes. +- Add support for `EVEN_K` in the matmul kernel. Now the kernel support `BLOCK_SIZE_K` +that cannot divide `K`. +- Tuning result file is open and closed inside the tuning loop, enabling timely flush +of the tuning results. +- Now we use `rocprofv2` to measure kernel time. +- We can use `--hack_triton_compile` to avoid all GPU activities during the compilation +stage. This is achieved by modifying the triton frontend compiler in the following +places: + - Return True from the `is_active()` function in the hip hackend [driver](https://github.com/triton-lang/triton/blob/fd691c67ac20958a67693358186d877790f5f48f/third_party/amd/backend/driver.py#L433) + - Return statically constructed GPUTarget from the `get_current_target()` + function in the hip backend [driver](https://github.com/triton-lang/triton/blob/fd691c67ac20958a67693358186d877790f5f48f/third_party/amd/backend/driver.py#L437) + - Return False from the `is_active()` function in the cuda hackend [driver](https://github.com/triton-lang/triton/blob/fd691c67ac20958a67693358186d877790f5f48f/third_party/nvidia/backend/driver.py#L383) + - Statically set `device` and `stream` in the [jit.py](https://github.com/triton-lang/triton/blob/fd691c67ac20958a67693358186d877790f5f48f/python/triton/runtime/jit.py#L588-L589) + + +# GEMM Tuning Script v3.4 + +## API changes + +No API changes + +## Implementation changes + +- Now the matmul_kernel supports XCD-based pid remapping. Details with experiments +will be added later. +- Switched back to rocprofv1. Check [ticket#228](https://github.com/ROCm/triton-internal/issues/228) for more details. +- Improved the post-procesing logic to filter out the "spikes" in the profiling results. +- Reduced the number of iterations in both tuning and benchmark mode (120 and 200). + + +# One config running script + +`one_config.py` is a script that runs one given matmul config. +It is an interface to `tune_gemm.py` functionality and could be used for triton debugging. + +## Usage + +This script supports two methods to specify configuration parameters. + +Variant 1: Separate command line attributes. + +```bash +python one_config.py -m 256 -n 256 -k 256 --block_m 64 --block_n 64 --block_k 64 --group_m 1 --split_k 2 --num_warps 2 --num_stages 0 --waves_per_eu 0 --matrix_instr_nonkdim 16 --kpack 2 +``` + +Variant 2: one-line config description. +This is how configs are printed by `tune_gemm.py` script + +```bash +python one_config.py --config_str M16_N8_K128_BM64_BN64_BK64_GM1_SK2_nW2_nS0_EU0_kP2_mfma16 +``` diff --git a/python/perf-kernels/tools/tune_gemm/icache_flush.py b/python/perf-kernels/tools/tune_gemm/icache_flush.py new file mode 100644 index 000000000000..6b9c0359c381 --- /dev/null +++ b/python/perf-kernels/tools/tune_gemm/icache_flush.py @@ -0,0 +1,82 @@ +# the hip module can be installed as +# `python3 -m pip install -i https://test.pypi.org/simple hip-python~=$rocm_version` +# more information about hip-python is at: https://github.com/ROCm/hip-python +from hip import hip, hiprtc + + +def hip_check(call_result): + err = call_result[0] + result = call_result[1:] + if len(result) == 1: + result = result[0] + + if isinstance(err, hip.hipError_t) and err != hip.hipError_t.hipSuccess: + raise RuntimeError(str(err)) + elif (isinstance(err, hiprtc.hiprtcResult) and err != hiprtc.hiprtcResult.HIPRTC_SUCCESS): + raise RuntimeError(str(err)) + + return result + + +# S_ICACHE_INV Invalidate entire first level instruction cache. +# There must be 16 separate S_NOP instructions or a jump/branch instruction +# after this instruction to ensure the internal instruction buffers are also +# invalidated. +def gen_kernel(): + source = b"""\ + extern "C" __global__ void icache_flush_kernel() { + asm __volatile__("s_icache_inv"); + asm __volatile__("s_nop 0"); + asm __volatile__("s_nop 0"); + asm __volatile__("s_nop 0"); + asm __volatile__("s_nop 0"); + asm __volatile__("s_nop 0"); + asm __volatile__("s_nop 0"); + asm __volatile__("s_nop 0"); + asm __volatile__("s_nop 0"); + asm __volatile__("s_nop 0"); + asm __volatile__("s_nop 0"); + asm __volatile__("s_nop 0"); + asm __volatile__("s_nop 0"); + asm __volatile__("s_nop 0"); + asm __volatile__("s_nop 0"); + asm __volatile__("s_nop 0"); + asm __volatile__("s_nop 0"); + } + """ + + # print(f"source = {source}") + prog = hip_check(hiprtc.hiprtcCreateProgram(source, b"icache_flush_kernel", 0, [], [])) + progs = hip.hipDeviceProp_t() + hip_check(hip.hipGetDeviceProperties(progs, 0)) + arch = progs.gcnArchName + cflags = [b"--offload-arch=" + arch] + err, = hiprtc.hiprtcCompileProgram(prog, len(cflags), cflags) + if err != hiprtc.hiprtcResult.HIPRTC_SUCCESS: + log_size = hip_check(hiprtc.hiprtcGetProgramLogSize(prog)) + log = bytearray(log_size) + hip_check(hiprtc.hiprtcGetProgramLog(prog, log)) + print(f"log = {log.decode()}, err = {err}") + raise RuntimeError(log.decode()) + + code_size = hip_check(hiprtc.hiprtcGetCodeSize(prog)) + code = bytearray(code_size) + hip_check(hiprtc.hiprtcGetCode(prog, code)) + module = hip_check(hip.hipModuleLoadData(code)) + kernel = hip_check(hip.hipModuleGetFunction(module, b"icache_flush_kernel")) + + return kernel + + +kernel = gen_kernel() +progs = hip.hipDeviceProp_t() +hip_check(hip.hipGetDeviceProperties(progs, 0)) +cu_num = progs.multiProcessorCount + + +def icache_flush(): + block = hip.dim3(x=64) + grid = hip.dim3(cu_num * 60) + + hip_check( + hip.hipModuleLaunchKernel(kernel, *grid, *block, sharedMemBytes=0, stream=None, kernelParams=None, extra=())) diff --git a/python/perf-kernels/tools/tune_gemm/matmul_kernel.py b/python/perf-kernels/tools/tune_gemm/matmul_kernel.py new file mode 100644 index 000000000000..1d9902bc2de6 --- /dev/null +++ b/python/perf-kernels/tools/tune_gemm/matmul_kernel.py @@ -0,0 +1,69 @@ +import triton +import triton.language as tl + + +@triton.jit +def matmul_kernel(a_ptr, b_ptr, c_ptr, bias_ptr, M, N, K, stride_am, stride_ak, stride_bk, stride_bn, stride_cm, + stride_cn, stride_bias, BLOCK_SIZE_M: tl.constexpr, BLOCK_SIZE_N: tl.constexpr, + BLOCK_SIZE_K: tl.constexpr, SPLIT_K: tl.constexpr, GROUP_SIZE_M: tl.constexpr, BIAS: tl.constexpr, + EVEN_K: tl.constexpr, GRID_MN: tl.constexpr, NUM_XCDS: tl.constexpr): + pid = tl.program_id(axis=0) + pid_z = tl.program_id(1) + num_pid_m = tl.cdiv(M, BLOCK_SIZE_M) + num_pid_n = tl.cdiv(N, BLOCK_SIZE_N) + + if NUM_XCDS != 1: + ## pid remapping on xcds + # Number of pids per XCD in the new arrangement + pids_per_xcd = GRID_MN // NUM_XCDS + # Compute current XCD and local pid within the XCD + xcd = pid % NUM_XCDS + local_pid = pid // NUM_XCDS + # Calculate new pid based on the new grouping + pid = xcd * pids_per_xcd + local_pid + + if GROUP_SIZE_M == 1: + pid_m = pid // num_pid_n + pid_n = pid % num_pid_n + else: + num_pid_in_group = GROUP_SIZE_M * num_pid_n + group_id = pid // num_pid_in_group + first_pid_m = group_id * GROUP_SIZE_M + group_size_m = min(num_pid_m - first_pid_m, GROUP_SIZE_M) + pid_m = first_pid_m + ((pid % num_pid_in_group) % group_size_m) + pid_n = (pid % num_pid_in_group) // group_size_m + + if SPLIT_K == 1: + offs_k = tl.arange(0, BLOCK_SIZE_K) + else: + offs_k = pid_z * BLOCK_SIZE_K + tl.arange(0, BLOCK_SIZE_K) + offs_am = (pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M)) + offs_bn = (pid_n * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N)) + a_ptrs = a_ptr + offs_am[:, None] * stride_am + offs_k[None, :] * stride_ak + b_ptrs = b_ptr + offs_k[:, None] * stride_bk + offs_bn[None, :] * stride_bn + if BIAS: + bias_ptrs = bias_ptr + offs_am * stride_bias + bias = tl.load(bias_ptrs, mask=offs_am < M, other=0.0) + acc_dtype = tl.float32 if a_ptr.type.element_ty != tl.int8 else tl.int32 + accumulator = tl.zeros((BLOCK_SIZE_M, BLOCK_SIZE_N), dtype=acc_dtype) + for k in range(0, tl.cdiv(K, BLOCK_SIZE_K * SPLIT_K)): + if EVEN_K: + a = tl.load(a_ptrs) + b = tl.load(b_ptrs) + else: + a = tl.load(a_ptrs, mask=offs_k[None, :] < K - k * BLOCK_SIZE_K, other=0.0) + b = tl.load(b_ptrs, mask=offs_k[:, None] < K - k * BLOCK_SIZE_K, other=0.0) + accumulator += tl.dot(a, b) + a_ptrs += BLOCK_SIZE_K * SPLIT_K * stride_ak + b_ptrs += BLOCK_SIZE_K * SPLIT_K * stride_bk + c = accumulator.to(c_ptr.type.element_ty) + if BIAS: + c += bias[:, None] + offs_cm = pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M) + offs_cn = pid_n * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N) + c_ptrs = c_ptr + stride_cm * offs_cm[:, None] + stride_cn * offs_cn[None, :] + c_mask = (offs_cm[:, None] < M) & (offs_cn[None, :] < N) + if SPLIT_K == 1: + tl.store(c_ptrs, c, mask=c_mask) + else: + tl.atomic_add(c_ptrs, c, mask=c_mask) diff --git a/python/perf-kernels/tools/tune_gemm/one_config.py b/python/perf-kernels/tools/tune_gemm/one_config.py new file mode 100644 index 000000000000..5354a270f493 --- /dev/null +++ b/python/perf-kernels/tools/tune_gemm/one_config.py @@ -0,0 +1,102 @@ +""" +Script for running one Matrix Multiplication kernel config at a time +""" + +import argparse +import re +import sys +import tune_gemm + + +def parse_args(): + parser = argparse.ArgumentParser( + prog="check corectness of particular config for tuning gemm script", + allow_abbrev=False, + ) + + parser.add_argument("-m", type=int, default=0) + parser.add_argument("-n", type=int, default=0) + parser.add_argument("-k", type=int, default=0) + parser.add_argument("-col_a", action='store_true', default=False, help='whether matrix a is column major') + parser.add_argument("-col_b", action='store_true', default=False, help='whether matrix b is column major') + parser.add_argument("-dtype_a", type=str, default='fp16', help="matrix a element data type") + parser.add_argument("-dtype_b", type=str, default='fp16', help="matrix b element data type") + parser.add_argument("-dtype_c", type=str, default='fp16', help="output element data type") + parser.add_argument("--init_type", type=str, default='randn', + help="Initialization type for input matrices (default uniform rand [0, 1.0)])") + parser.add_argument("--bias_vector", action='store_true', default=False, help="apply bias vector") + parser.add_argument("--block_m", type=int, default=0) + parser.add_argument("--block_n", type=int, default=0) + parser.add_argument("--block_k", type=int, default=0) + parser.add_argument("--group_m", type=int, default=0) + parser.add_argument("--split_k", type=int, default=0) + parser.add_argument("--num_warps", type=int, default=0) + parser.add_argument("--num_stages", type=int, default=0) + parser.add_argument("--waves_per_eu", type=int, default=0) + parser.add_argument("--matrix_instr_nonkdim", type=int, default=0) + parser.add_argument("--kpack", type=int, default=0) + parser.add_argument( + "--config_str", type=str, default="", help= + "can take from tune_gemm.py script output, looks like M16_N8_K128_BM64_BN64_BK64_GM1_SK2_nW2_nS0_EU0_kP2_mfma16" + ) + args = parser.parse_args() + + return args + + +def parse_config(cfg_str): + values = cfg_str.split("_") + # yapf: disable + config_name = { + "M": "M", + "N": "N", + "K": "K", + "BM": "BLOCK_SIZE_M", + "BN": "BLOCK_SIZE_N", + "BK": "BLOCK_SIZE_K", + "GM": "GROUP_SIZE_M", + "SK": "SPLIT_K", + "nW": "num_warps", + "nS": "num_stages", + "EU": "waves_per_eu", + "kP": "kpack", + "mfma": "matrix_instr_nonkdim", + } + # yapf: enable + config = {} + for val in values: + match = re.search("([a-zA-Z]*)([0-9]*)", val) + if match: + cfg_field_name = config_name[match.group(1)] + config[cfg_field_name] = int(match.group(2)) + return config + + +def main(): + args = parse_args() + if args.config_str: + config = parse_config(args.config_str) + else: + # yapf: disable + config = { + "M": args.m, + "N": args.n, + "K": args.k, + "BLOCK_SIZE_M": args.block_m, + "BLOCK_SIZE_N": args.block_n, + "BLOCK_SIZE_K": args.block_k, + "GROUP_SIZE_M": args.group_m, + "SPLIT_K": args.split_k, + "num_warps": args.num_warps, + "num_stages": args.num_stages, + "waves_per_eu": args.waves_per_eu, + "kpack": args.kpack, + "matrix_instr_nonkdim": args.matrix_instr_nonkdim, + } + # yapf: enable + tune_gemm.test_correctness(config["M"], config["N"], config["K"], args.col_a, args.col_b, args.dtype_a, + args.dtype_b, args.dtype_c, args.init_type, config, args.bias_vector, verbose=True) + + +if __name__ == "__main__": + sys.exit(main()) diff --git a/python/perf-kernels/tools/tune_gemm/tune_gemm.py b/python/perf-kernels/tools/tune_gemm/tune_gemm.py new file mode 100755 index 000000000000..291096b3d7af --- /dev/null +++ b/python/perf-kernels/tools/tune_gemm/tune_gemm.py @@ -0,0 +1,711 @@ +#!/usr/bin/env python3 + +import argparse +import sys +import yaml +import os +import glob + +import torch +import triton +import triton.language as tl + +from matmul_kernel import matmul_kernel + +from datetime import datetime +import multiprocessing +import pandas as pd + +from utils.file_generator import ( + gen_configStr, + generate_compile_driver, + generate_matmul_kernels, + generate_profile_tasks, + read_config, +) +from utils.utils import ( + get_default_tuning_result_filename, + get_filename_compile_driver, + get_filename_myKernels, + get_filename_profile_driver, + name_to_tl_types, + patch_triton_compiler, + run_bash_command, + run_bash_command_wrapper, + tl_to_torch_types, + TORCH_HAS_FP8E4B8, + TORCH_HAS_FP8E5B16, +) + + +def is_hip_available(): + try: + __import__("hip") + except ImportError: + return False + else: + return True + + +def get_full_tuning_space(): + configs = [] + + block_mn_range = [16, 32, 64, 128, 256] + block_k_range = [16, 32, 64, 128, 256] + split_k_range = [1, 2, 4, 5, 6, 8, 10, 12, 16, 18, 24] + num_warps_range = [1, 2, 4, 8] + group_m_range = [1, 2, 4, 8, 16, 32] + # For now we see better perf with num_stages=0 for all gemm configs we care + # But keep this explicit so that we do not forget we may need to set it to + # other values in the future + num_stage_range = [0] + waves_per_eu_range = [0] + matrix_instr_nonkdim_range = [16, 32] + kpack_range = [1, 2] + + for block_m in block_mn_range: + for block_n in block_mn_range: + for block_k in block_k_range: + for num_warps in num_warps_range: + for group_m in group_m_range: + for split_k in split_k_range: + for num_stages in num_stage_range: + for waves_per_eu in waves_per_eu_range: + for matrix_instr_nonkdim in matrix_instr_nonkdim_range: + for kpack in kpack_range: + configs.append({ + 'BLOCK_SIZE_M': block_m, 'BLOCK_SIZE_N': block_n, 'BLOCK_SIZE_K': + block_k, 'GROUP_SIZE_M': group_m, 'SPLIT_K': split_k, 'num_warps': + num_warps, 'num_stages': num_stages, 'waves_per_eu': waves_per_eu, + 'matrix_instr_nonkdim': matrix_instr_nonkdim, 'kpack': kpack + }) + + return configs + + +def get_default_config(): + full_configs = get_full_tuning_space() + return full_configs[0] + + +def prune_configs(M, N, K, configs, elemBytes_a, elemBytes_b): + pruned_configs = [] + + if M < 32 or N < 32: + mfma = 16 + else: + mfma = 32 + + # TODO (zhanglx): figure out the boundary between large and small gemms + large_gemm = False + if M >= 2048 and N >= 2048: + large_gemm = True + + for config in configs: + BLOCK_SIZE_M = config.get("BLOCK_SIZE_M") + BLOCK_SIZE_N = config.get("BLOCK_SIZE_N") + BLOCK_SIZE_K = config.get("BLOCK_SIZE_K") + num_warps = config.get("num_warps") + num_stages = config.get("num_stages") + matrix_instr_nonkdim = config.get("matrix_instr_nonkdim") + if matrix_instr_nonkdim > mfma: + continue + if mfma == 4 and BLOCK_SIZE_K < 64: + continue + # some layouts could not work properly in case + # number elemens per thread is less 1 + if BLOCK_SIZE_M * BLOCK_SIZE_N < 64: + continue + SPLIT_K = config.get("SPLIT_K") + GROUP_M = config.get("GROUP_SIZE_M") + if BLOCK_SIZE_M < matrix_instr_nonkdim or BLOCK_SIZE_N < matrix_instr_nonkdim: + continue + if M <= matrix_instr_nonkdim and BLOCK_SIZE_M != matrix_instr_nonkdim: + continue + if N <= matrix_instr_nonkdim and BLOCK_SIZE_N != matrix_instr_nonkdim: + continue + # Skip BLOCK_SIZE that is too large compare to M/N + # unless BLOCK_SIZE is already small enough + if BLOCK_SIZE_M > M * 2 and BLOCK_SIZE_M != 16: + continue + if BLOCK_SIZE_N > N * 2 and BLOCK_SIZE_N != 16: + continue + # skip large split_k when not necessary + if SPLIT_K != 1 and not need_split_k(M, N, K): + continue + # skip split_k that leads to EVEN_K = false + leap = SPLIT_K * BLOCK_SIZE_K + modv = K % leap + if modv != 0 and SPLIT_K != 1: + continue + # skip large GROUP_M + if GROUP_M * BLOCK_SIZE_M > M and GROUP_M != 1: + continue + # out of shared memory resource + # TODO (zhanglx): This does not consider the LDS usage in the epilogue + LDS = BLOCK_SIZE_K * BLOCK_SIZE_M * elemBytes_a + BLOCK_SIZE_K * BLOCK_SIZE_N * elemBytes_b + LDS = LDS if not num_stages else LDS * num_stages + if LDS > 65536: + continue + # Skip small block sizes and num_warps for large gemm + # For fp16 and f8, we want to only use BLOCK_SIZE >= 64 + if large_gemm: + if BLOCK_SIZE_M < 64 or BLOCK_SIZE_N < 64: + continue + if BLOCK_SIZE_K < 64: + continue + if num_warps < 4: + continue + # check if tiling is integer multiple of GEMM size because we have no boundary check + if M % BLOCK_SIZE_M != 0 or N % BLOCK_SIZE_N != 0: + continue + + pruned_configs.append(config) + + return pruned_configs + + +def need_split_k(SIZE_M, SIZE_N, SIZE_K): + return (SIZE_M < 64 or SIZE_N < 64) and SIZE_K > 1024 + + +def extract_kernel_time(M, N, K, config, df): + configStr = gen_configStr(config) + df = df[df['KernelName'].str.contains(configStr)] + + first_value = df['DurationNs'].iloc[0] + filtered_data = df['DurationNs'][df['DurationNs'] <= first_value] + new_meanTime = filtered_data.tail(100).mean() + + return config, new_meanTime + + +def profile_batch_kernels(M, N, K, gpuid, gpus, jobs, verbose): + ngpus = len(gpus) + gpuIdx = gpus.index(gpuid) + if gpuIdx + 1 > jobs: + return + os.environ['ROCR_VISIBLE_DEVICES'] = str(gpuid) + jobId = gpuIdx + while jobId < jobs: + kernel_name = get_filename_profile_driver(M, N, K, jobId) + if verbose: + print(f"profiling {kernel_name} on GPU {gpuid}") + run_bash_command_wrapper( + f"rocprof --stats -o results_{jobId}.csv python {get_filename_profile_driver(M, N, K, jobId)}", + capture=(verbose < 2)) + jobId += ngpus + + +def tune_gemm_config(M, N, K, col_a, col_b, dtype_a, dtype_b, dtype_c, init_type, configs, run_bench, jobs, iters, + skipWarmup, verbose=0, num_threads=32, gpus=[0], rotating_buffer_size=256, bias_size=0, + icache_flush=False): + + # precompile the kernels in parallel + start_time = datetime.now() + if not skipWarmup: + # Generate kernel out of all configs + fname = generate_compile_driver(M, N, K, col_a, col_b, dtype_a, dtype_b, dtype_c, init_type, configs, + rotating_buffer_size, bias_size) + + run_bash_command(f"python {fname} -n {num_threads}", capture=(verbose < 2)) + compile_end = datetime.now() + compile_time = compile_end - start_time + if verbose: + print(f"compile time: {compile_time}", flush=True) + + # Generate kernels out of all configs + generate_profile_tasks(M, N, K, col_a, col_b, dtype_a, dtype_b, dtype_c, init_type, configs, jobs, iters, run_bench, + rotating_buffer_size, bias_size, icache_flush) + + # profile generated kernels + running = [ + multiprocessing.Process(target=profile_batch_kernels, args=(M, N, K, gpu_id, gpus, jobs, verbose)) + for gpu_id in gpus + ] + for p in running: + p.start() + for p in running: + p.join() + + profile_end = datetime.now() + profile_time = profile_end - compile_end + if verbose: + print(f"profile time: {profile_time}", flush=True) + + # post process results.csv to get the best config and minTime + # TODO: process the file in parallel + minTime = 1024 * 1024 * 1024 + thread_pool = multiprocessing.Pool(processes=num_threads) + tasks = [] + idx = 0 + df_prof = [pd.read_csv(f"results_{i}.csv") for i in range(jobs)] + for config in configs: + file_idx = idx % jobs + tasks += [thread_pool.apply_async(extract_kernel_time, args=(M, N, K, config, df_prof[file_idx]))] + idx += 1 + thread_pool.close() + thread_pool.join() + + for task in tasks: + config, myTime = task.get() + if myTime: + min_us = myTime / 1000 + if min_us < minTime: + minTime = min_us + bestConfig = config + else: + min_us = -1 + print(f"invalid config(post processing): SIZE {M} {N} {K}: {config}", flush=True) + post_end = datetime.now() + post_time = post_end - profile_end + if verbose: + print(f"post procesing time: {post_time}", flush=True) + return minTime, bestConfig, compile_time, profile_time, post_time + + +def gen_input(M, N, ty_name, needTrans, seed, init_type, device='cuda'): + d_type = name_to_tl_types[ty_name] + torch.manual_seed(seed) + torch.cuda.manual_seed(seed) + + @triton.jit + def copy_kernel(input_ptr, output_ptr, n_elements, BLOCK_SIZE: tl.constexpr): + offsets = tl.program_id(axis=0) * BLOCK_SIZE + tl.arange(0, BLOCK_SIZE) + mask = offsets < n_elements + input = tl.load(input_ptr + offsets, mask=mask) + output = input + tl.store(output_ptr + offsets, output, mask=mask) + + def init_by_size_and_type(size, dtype, init_type): + if init_type == 'hpl': + return torch.empty(size, device='cuda', dtype=dtype).uniform_(-0.5, 0.5) + # This init type has element[i] in row[j] equal to sin(i+j*N) + elif init_type == 'trig_float': + M, N = size + return torch.reshape(torch.arange(0, M * N), (M, N)).sin().to(dtype=dtype, device='cuda') + elif init_type == 'zeros': + return torch.zeros(size, dtype=dtype, device='cuda') + elif init_type == "randn": + temp = torch.randn(size, dtype=dtype, device='cuda') + return temp + else: + raise ValueError("Bad matrix initialization type.") + + raw_data = init_by_size_and_type((N, M) if needTrans else (M, N), torch.float32, init_type) + if needTrans: + raw_data = raw_data.T + if (d_type == tl.float8e4b8 and TORCH_HAS_FP8E4B8) or \ + (d_type == tl.float8e5b16 and TORCH_HAS_FP8E5B16) or not d_type.is_fp8(): + input = raw_data.to(tl_to_torch_types[d_type]) + input_f16 = input.to(torch.float16) + else: + f8_tensor = raw_data.to(torch.int8) + # keep only two bits of exponent to avoid overflow + f8_tensor = f8_tensor & 0b00111111 + input = triton.reinterpret(f8_tensor, d_type) + input_f16 = torch.empty_like(f8_tensor, dtype=torch.float16) + grid = lambda meta: (triton.cdiv(n_elements, meta['BLOCK_SIZE']), ) + n_elements = raw_data.numel() + copy_kernel[grid](input, input_f16, n_elements, BLOCK_SIZE=1024) + + return input, input_f16 + + +# generate inputs/outputs according to rotating tensor size +def gen_rotating_tensors(M, N, K, dtype_a, need_Trans_a, dtype_b, need_Trans_b, dtype_c, seed, init_type, + rotating_buffer_size, bias_size, device='cuda'): + a_size = M * K * type_name_to_bytes(dtype_a) + b_size = K * N * type_name_to_bytes(dtype_b) + c_size = M * N * type_name_to_bytes(dtype_c) + bias_size = bias_size * type_name_to_bytes(dtype_c) + + total_size = a_size + b_size + c_size + bias_size + block_count = rotating_buffer_size * 1024 * 1024 // total_size + block_count = max(1, block_count) + + # generate input and outputs + a = [] + b = [] + c = [] + bias = [] + for i in range(block_count): + in_a, in_a_fp16 = gen_input(M, K, dtype_a, need_Trans_a, 1, init_type, device='cuda') + a.append(in_a) + in_b, in_b_fp16 = gen_input(K, N, dtype_b, need_Trans_b, 2, init_type, device='cuda') + b.append(in_b) + out_c = torch.zeros((M, N), dtype=tl_to_torch_types[name_to_tl_types[dtype_c]], device='cuda') + c.append(out_c) + if bias_size > 0: + bs, bs_fp16 = gen_input(M, 1, dtype_b, need_Trans_b, 2, init_type, device='cuda') + bias.append(bs.squeeze(dim=1)) + + in_outs = {"rotating_num": block_count, "input_a": a, "input_b": b, "output_c": c, "bias": bias} + + return in_outs + + +def matmul(a, b, c, bias, block_m, block_n, block_k, group_m, split_k, num_warps, num_stages, waves_per_eu, + mfmaInstrSize, kpack, use_bias): + # Check constraints. + assert a.shape[1] == b.shape[0], "Incompatible dimensions" + #assert a.is_contiguous(), "Matrix A must be contiguous" + #assert b.is_contiguous(), "Matrix B must be contiguous" + M, K = a.shape + K, N = b.shape + # 1D launch kernel where each block gets its own program. + + grid = triton.cdiv(M, block_m) * triton.cdiv(N, block_n), split_k + stride_bias = bias.stride(0) if use_bias else 0 + EVEN_K = K % block_k == 0 + num_xcds = 1 if split_k > 1 else 8 + matmul_kernel[grid](a, b, c, bias, M, N, K, a.stride(0), a.stride(1), b.stride(0), b.stride(1), c.stride(0), + c.stride(1), stride_bias=stride_bias, BLOCK_SIZE_M=block_m, BLOCK_SIZE_N=block_n, + BLOCK_SIZE_K=block_k, GROUP_SIZE_M=group_m, SPLIT_K=split_k, num_warps=num_warps, + num_stages=num_stages, waves_per_eu=waves_per_eu, matrix_instr_nonkdim=mfmaInstrSize, + kpack=kpack, BIAS=use_bias, EVEN_K=EVEN_K, GRID_MN=grid[0], NUM_XCDS=num_xcds) + return c + + +def test_correctness(M, N, K, col_a, col_b, dtype_a, dtype_b, dtype_c, init_type, config, bias_vector, verbose): + block_m, block_n, block_k, group_m, split_k, num_warps, num_stages, waves_per_eu, mfmaInstrSize, kpack = read_config( + config) + use_bias = bias_vector + torch.manual_seed(0) + #a = torch.randn((M, K), device='cuda', dtype=datatype) + #b = torch.randn((K, N), device='cuda', dtype=datatype) + a, a_fp16 = gen_input(M, K, dtype_a, col_a, 1, init_type, device='cuda') + b, b_fp16 = gen_input(K, N, dtype_b, col_b, 2, init_type, device='cuda') + bias = None + if use_bias: + bias, bias_fp16 = gen_input(M, 1, dtype_b, col_b, 2, init_type, device='cuda') + bias = bias.squeeze(dim=1) + bias_fp16 = bias.squeeze(dim=1) + # Allocates output. + c = torch.zeros((M, N), device=a.device, dtype=tl_to_torch_types[name_to_tl_types[dtype_c]]) + triton_output = matmul(a, b, c, bias, block_m, block_n, block_k, group_m, split_k, num_warps, num_stages, + waves_per_eu, mfmaInstrSize, kpack, use_bias) + torch_output = torch.matmul(a_fp16, b_fp16) + if use_bias: + torch_output += bias_fp16[:, None] + rtol = 0 if torch.version.hip is None else 1e-2 + atol = 1e-3 if split_k == 1 else 4e-2 + row_a_str = 'N' if col_a else 'T' + row_b_str = 'N' if col_b else 'T' + size_str = '' + if verbose: + size_str = f'SIZE M: {M}, N: {N}, K: {K}, trans: {row_a_str}{row_b_str}' + if torch.allclose(triton_output.to(torch.float16), torch_output, atol=atol, rtol=rtol): + print(f'{size_str} Correct✅') + else: + print(f"triton_output={triton_output}") + print(f"torch_output={torch_output}") + print(f'{size_str} Incorrect❌') + + +def parse_args(): + parser = argparse.ArgumentParser( + prog="tune a specific gemm size", + allow_abbrev=False, + ) + + parser.add_argument("-m", type=int, default=0) + parser.add_argument("-n", type=int, default=0) + parser.add_argument("-k", type=int, default=0) + parser.add_argument("-col_a", action='store_true', default=False, help='whether matrix a is column major') + parser.add_argument("-col_b", action='store_true', default=False, help='whether matrix b is column major') + parser.add_argument("-dtype_a", type=str, default='fp16', help="matrix a element data type") + parser.add_argument("-dtype_b", type=str, default='fp16', help="matrix b element data type") + parser.add_argument("-dtype_c", type=str, default='fp16', help="output element data type") + parser.add_argument("--ngpus", type=int, default=0, help='number of GPUs used in the profiling step') + parser.add_argument("--gpu_ids", type=lambda s: [int(id) for id in s.split(',')], default=[], + help='list of gpu ids to use for tuning') + parser.add_argument("--gemm_size_file", type=str, default="", help='yaml file to indicate matrix size') + parser.add_argument("--o", type=str, default='', help='yaml file to store tuning results') + parser.add_argument("--keep", action='store_true', default=False, help='keep generated files') + parser.add_argument("--compare", action='store_true', default=False, help="Whether check result correctness") + parser.add_argument("--compare_wo_tuning", action='store_true', default=False, + help="Whether check result correctness without tuning.") + parser.add_argument("--benchmark", action='store_true', default=False, help="Benchmark the given config") + parser.add_argument("--time_breakdown", action='store_true', default=False, + help="Show detailed time breakdown of each step during the tuning") + parser.add_argument("--verbose", action='store_true', default=False, + help="enables time_breakdown and additional logging messages") + parser.add_argument("--num_threads", type=int, default=32, + help="number of threads to use for kernel compilation and post processing") + parser.add_argument("--jobs", type=int, default=1, help="number of tasks during the profiling process") + parser.add_argument("--iters", type=int, default=200, help="number of iterations used in --benchmark mode") + parser.add_argument("--init_type", type=str, default='randn', choices=['randn', 'hpl', 'trig_float', 'zeros'], + help="Input tensor initialization (default normal distribution)") + parser.add_argument( + "--rotating_tensor", type=int, default=0, help="total size (MB) of all tensors (a, b, c, bias)." + " The default value is 0 (no rotating tensor)." + " When set, it needs to be larger than the L1, L2, MALL size)") + parser.add_argument("--bias_vector", action='store_true', default=False, help="apply bias vector") + parser.add_argument("--icache_flush", action='store_true', default=False, + help="apply icache flush in tuning performance") + parser.add_argument("--no_warmup", action='store_true', default=False, + help="Whether we want to skip the compilation stage") + parser.add_argument("--hack_triton_compiler", action='store_true', default=False, + help="Modify the triton source to avoid backend query") + args = parser.parse_args() + if not args.o: + if args.benchmark: + args.o = "benchmarking_results.csv" + else: + args.o = get_default_tuning_result_filename() + + return args + + +def process_item(item): + M = item['M'] + N = item['N'] + K = item['K'] + col_a = False if item['rowMajorA'] == 'T' else True + col_b = False if item['rowMajorB'] == 'T' else True + del item['M'] + del item['N'] + del item['K'] + del item['rowMajorA'] + del item['rowMajorB'] + return M, N, K, col_a, col_b, item + + +def type_name_to_bytes(ty_name): + if '32' in ty_name: + return 4 + if '16' in ty_name: + return 2 + if '8' in ty_name: + return 1 + else: + print(f"Unrecognized input type name {ty_name}") + sys.exit(1) + + +def format_output(unformatted): + if unformatted < 0.0001: + formatted = "{:.3e}".format(unformatted) + elif unformatted > 1000: + formatted = "{:.1f}".format(unformatted) + else: + formatted = "{:.2f}".format(unformatted) + return formatted + + +def get_rocm_version(): + torch_hip_version = torch.version.hip + vers = torch_hip_version.split('.') + ret_ver = '$rocm_version' + if len(vers) >= 2: + ret_ver = vers[0] + '.' + vers[1] + return ret_ver + + +def main(): + args = parse_args() + matrix_size_file = args.gemm_size_file + output_file = args.o + keepTmp = args.keep + run_bench = args.benchmark + jobs = args.jobs + iters = args.iters + skipWarmup = args.no_warmup + hack_triton = args.hack_triton_compiler + + # Get GPU ids + ngpus = args.ngpus + gpu_ids = args.gpu_ids + if ngpus != 0 and gpu_ids: + print("--ngpus and --gpu_ids are mutually exclusive options") + return os.EX_USAGE + if ngpus == 0 and not gpu_ids: + ngpus = 1 + if ngpus != 0: + gpus = range(ngpus) + if gpu_ids: + gpus = gpu_ids + + if run_bench: + gpus = [gpus[0]] + jobs = 1 + + # Get element type + dtype_a = args.dtype_a + dtype_b = args.dtype_b + dtype_c = args.dtype_c + if dtype_a not in name_to_tl_types or dtype_b not in name_to_tl_types or dtype_c not in name_to_tl_types: + print(f"Unsupported dtype_a {args.dtype_a} or dtype_b {args.dtype_b} or dtype_c {args.dtype_c}") + print("Supported types: ", list(name_to_tl_types.keys())) + sys.exit(1) + rotating_buffer_size = args.rotating_tensor + bias_vector = args.bias_vector + icache_flush = args.icache_flush + if icache_flush: + if not is_hip_available(): + print("************************************************************************************************") + print(" `icache-flush` is disabled for this run.") + print(" `icache-flush` needs python-hip module, which is unavailable.") + print(" python-hip module can be installed as:") + print(f" `python3 -m pip install -i https://test.pypi.org/simple hip-python~={get_rocm_version()}`") + print("************************************************************************************************") + icache_flush = False + + mnks = [] + # TODO: make it more robust to get user input + init_type = args.init_type + if matrix_size_file == "" or not os.path.isfile(matrix_size_file): + M = args.m + N = args.n + K = args.k + col_a = args.col_a + col_b = args.col_b + mnks = [(M, N, K, col_a, col_b, None)] + else: + with open(matrix_size_file) as file: + matrix_sizes = yaml.safe_load(file) + for item in matrix_sizes: + M, N, K, col_a, col_b, item = process_item(item) + mnks.append((M, N, K, col_a, col_b, item)) + + # Check correctness from given configs + if args.compare_wo_tuning: + for (M, N, K, col_a, col_b, myConfig) in mnks: + if myConfig is None: + raise Exception("kernel config is None, need to provide a tuning config") + test_correctness(M, N, K, col_a, col_b, dtype_a, dtype_b, dtype_c, init_type, myConfig, bias_vector, True) + return + + configs_full = get_full_tuning_space() + + start_time = datetime.now() + # Append to the output file so that we can save all results into one file + f_results = open(output_file, 'a') + if run_bench: + print(f"Benchmarking gemm with {dtype_a} inputs") + print("trans M N K TFLOPS us") + f_results.write("trans,M,N,K,TFLOPS,us\n") + else: + print(f"Tuning {len(mnks)} gemm sizes starts at: {start_time}", flush=True) + + f_results.close() + + ## Before tuning starts, clear cache and previously generated kernel files + run_bash_command("rm -rf ~/.triton/cache") + run_bash_command(f"rm -rf {get_filename_myKernels()}") + + ## Modify triton compiler + ## Hacky !!! + if hack_triton: + patch_triton_compiler() + + configs = [] + + ## Big for loop of tuning + ## Each iteration performs tuning for one gemm size + for (M, N, K, col_a, col_b, myConfig) in mnks: + + f_results = open(output_file, 'a') + + start_local_time = datetime.now() + # Obtain a pruned tuning space according to gemm size + # If running benchmark, use the provided config + pruned_configs = [myConfig] if run_bench else prune_configs(M, N, K, configs_full, type_name_to_bytes(dtype_a), + type_name_to_bytes(dtype_b)) + + ## Only append new configs from the current gemm size + delta_configs = [config for config in pruned_configs if config not in configs] + configs += delta_configs + + ## Append new configs into the tuning space + generate_matmul_kernels(delta_configs) + + row_a_str = 'N' if col_a else 'T' + row_b_str = 'N' if col_b else 'T' + size_str = f'SIZE: {M} {N} {K} {row_a_str}{row_b_str}' + if not run_bench: + print(f"{size_str} nConfigs: {len(pruned_configs)}", end=" ", flush=True) + else: + print(f"{row_a_str}{row_b_str} {M:5d} {N:5d} {K:5d} ", end="") + f_results.write(f"{row_a_str}{row_b_str},{M},{N},{K},") + + # The main tuning funtion for one gemm size + verbose_level = 0 + if args.time_breakdown: + verbose_level = 1 + if args.verbose: + verbose_level = 2 + # we consider bias size as M for now. + bias_size = M if bias_vector else 0 + minTime, bestConfig, compile_time, profile_time, post_time = tune_gemm_config( + M, N, K, col_a, col_b, dtype_a, dtype_b, dtype_c, init_type, pruned_configs, run_bench, jobs, iters, + skipWarmup, num_threads=args.num_threads, gpus=gpus, verbose=verbose_level, + rotating_buffer_size=rotating_buffer_size, bias_size=bias_size, icache_flush=icache_flush) + + # post processing the numbers + perf_tflops = lambda us: 2 * M * N * K * 1e-12 / (us * 1e-6) + tri_tflops = perf_tflops(minTime) + formatted_tflops = format_output(tri_tflops) + minTime = format_output(minTime) + if not run_bench: + print(f'TFLOPS: {formatted_tflops} time(us): {minTime}', end=" ", flush=True) + + bestConfig_compact_str = gen_configStr(bestConfig) + if not run_bench: + print(f'best_config: {bestConfig_compact_str}', end=" ", flush=True) + + # write best config to tuning_results.yaml + if run_bench: + print(f"{formatted_tflops} {minTime}") + f_results.write(f"{formatted_tflops},{minTime}\n") + + sizeDict = {'M': M, 'N': N, 'K': K, 'rowMajorA': row_a_str, 'rowMajorB': row_b_str} + sizeDict.update(bestConfig) + if not run_bench: + f_results.write("- " + str(sizeDict) + " ") + f_results.write(f'# TFLOPS: {formatted_tflops} time(us): {minTime}\n') + + # remove generated files if asked to + if not keepTmp: + if not skipWarmup: + os.remove(get_filename_compile_driver()) + try: + os.remove(get_filename_compile_driver() + ".failed_configs") + except OSError: + pass + for i in range(jobs): + generated_script = get_filename_profile_driver(M, N, K, i) + os.remove(generated_script) + for f in glob.glob(f"results_{i}.*"): + os.remove(f) + + # Check correctness if asked to + if args.compare: + print("correctness: ", end=" ", flush=True) + test_correctness(M, N, K, col_a, col_b, dtype_a, dtype_b, dtype_c, init_type, bestConfig, bias_vector, + False) + elif not run_bench: + print("", flush=True) + + end_local_time = datetime.now() + if not run_bench: + print( + f">>> Elapsed time: {end_local_time - start_local_time} = {compile_time} (compile) + {profile_time} (profile) + {post_time} (post processing)", + flush=True) + + f_results.close() + ## End big loop for tuning + + end_time = datetime.now() + tuning_time = end_time - start_time + if not run_bench: + print(f"Tuning ends at: {end_time}") + print(f"Total tuning time (h:m:s): {tuning_time}") + + if hack_triton: + print("Triton compiler is hacked, don't forget to git restore the changes :)") + + +if __name__ == '__main__': + sys.exit(main()) diff --git a/python/perf-kernels/tools/tune_gemm/utils/file_generator.py b/python/perf-kernels/tools/tune_gemm/utils/file_generator.py new file mode 100644 index 000000000000..d92079dab9a0 --- /dev/null +++ b/python/perf-kernels/tools/tune_gemm/utils/file_generator.py @@ -0,0 +1,363 @@ +import os + +from .utils import ( + get_filename_compile_driver, + get_filename_myKernels, + get_filename_profile_driver, + get_filename_without_extension, + name_to_tl_types, + tl_to_torch_types, +) + + +def read_config(config): + block_m = config.get('BLOCK_SIZE_M') + block_n = config.get('BLOCK_SIZE_N') + block_k = config.get('BLOCK_SIZE_K') + group_m = config.get('GROUP_SIZE_M') + split_k = config.get('SPLIT_K') + num_warps = config.get('num_warps') + num_stages = config.get('num_stages') + waves_per_eu = config.get('waves_per_eu') + mfma_instr_size = config.get('matrix_instr_nonkdim') + kpack = config.get('kpack') + return block_m, block_n, block_k, group_m, split_k, num_warps, num_stages, waves_per_eu, mfma_instr_size, kpack + + +def gen_configStr(config): + block_m, block_n, block_k, group_m, split_k, num_warps, num_stages, waves_per_eu, mfmaInstrSize, kpack = read_config( + config) + + ## {M}_{N}_{K} is removed since the same kernel can be used for differen gemm sizes + configStr = f"BM{block_m}_BN{block_n}_BK{block_k}_GM{group_m}_SK{split_k}_nW{num_warps}_nS{num_stages}_EU{waves_per_eu}_kP{kpack}_mfma{mfmaInstrSize}" + + return configStr + + +def generate_matmul_kernels(configs): + """ + Generate kernels based on configs and append them to get_filename_myKernels() + + Use the matmul_kernel template (../matmul_kernel.py) and append config to the + kernel name. E.g. matmul_kernel_BM256_BN256_BK64_GM1_SK1_nW1_nS0_EU0_kP2_mfma16() + """ + + if len(configs) == 0: + return + + f_kernel = open(get_filename_myKernels(), 'a') + + # write imports + import_str = """import triton +import triton.language as tl""" + f_kernel.write(import_str) + + with open(os.path.dirname(os.path.abspath(__file__)) + "/../matmul_kernel.py") as file: + matmul_kernel_code = file.read() + + for config in configs: + configStr = gen_configStr(config) + # Copy the matmul_kernel with name replaced + matmul_kernel_config = matmul_kernel_code.replace("matmul_kernel", f"matmul_kernel_{configStr}") + matmul_kernel_config = matmul_kernel_config.replace("import triton.language as tl", "") + matmul_kernel_config = matmul_kernel_config.replace("import triton", "") + f_kernel.write(matmul_kernel_config) + + f_kernel.close() + + +## construct the configStr and generate the wrapper function matmul_{configStr}() +## If `warmup` is set, the generated kernel will be **compiled** +def gen_kernel_and_configStr_from_config(config, EVEN_K, dtype_a, dtype_b, dtype_c, bias_size, warmup): + block_m, block_n, block_k, group_m, split_k, num_warps, num_stages, waves_per_eu, mfmaInstrSize, kpack = read_config( + config) + + configStr = gen_configStr(config) + + use_bias = bias_size > 0 + + ## Let's enable xcd-based pid remapping only when split-K is NOT used + ## Also #xcd is fixed to 8. If we are tuning for MI308, please change it to 4 + num_xcds = 1 if split_k > 1 else 8 + + if warmup: + torch_dtype_a = 'fp16' + torch_dtype_b = 'fp16' + torch_dtype_c = 'fp16' + if dtype_a: + torch_dtype_a = tl_to_torch_types[name_to_tl_types[dtype_a]] + if dtype_b: + torch_dtype_b = tl_to_torch_types[name_to_tl_types[dtype_b]] + if dtype_c: + torch_dtype_c = tl_to_torch_types[name_to_tl_types[dtype_c]] + + matmul_def_str = f""" +def matmul_{configStr}(M, N, K, am, ak, bk, bn, cm, cn, biasn): + grid_mn = triton.cdiv(M, {block_m}) * triton.cdiv(N, {block_n}) + matmul_kernel_{configStr}.warmup( + {torch_dtype_a}, {torch_dtype_b}, {torch_dtype_c}, {torch_dtype_c}, + M, N, K, + am, ak, bk, bn, cm, cn, biasn, + BLOCK_SIZE_M = {block_m}, + BLOCK_SIZE_N = {block_n}, + BLOCK_SIZE_K = {block_k}, + GROUP_SIZE_M = {group_m}, + SPLIT_K = {split_k}, + num_warps = {num_warps}, + num_stages = {num_stages}, + waves_per_eu = {waves_per_eu}, + matrix_instr_nonkdim = {mfmaInstrSize}, + kpack = {kpack}, + BIAS = {use_bias}, + EVEN_K = {EVEN_K}, + GRID_MN = grid_mn, + NUM_XCDS = {num_xcds}, + grid=(1,), + ) + return None + +def try_compile_config_{configStr}(M, N, K, am, ak, bk, bn, cm, cn, biasn): + try: + matmul_{configStr}(M, N, K, am, ak, bk, bn, cm, cn, biasn) + return True + except Exception as e: + print(f'invalid config(compilation): {configStr}: ', e, flush=True) + return False +""" + else: + matmul_def_str = f""" +def matmul_{configStr}(a, b, c, bias, M, N, K, am, ak, bk, bn, cm, cn, biasn): + grid = triton.cdiv(M, {block_m}) * triton.cdiv(N, {block_n}), {split_k} + matmul_kernel_{configStr}[grid]( + a, b, c, bias, + M, N, K, + am, ak, bk, bn, cm, cn, biasn, + BLOCK_SIZE_M = {block_m}, + BLOCK_SIZE_N = {block_n}, + BLOCK_SIZE_K = {block_k}, + GROUP_SIZE_M = {group_m}, + SPLIT_K = {split_k}, + num_warps = {num_warps}, + num_stages = {num_stages}, + waves_per_eu = {waves_per_eu}, + matrix_instr_nonkdim = {mfmaInstrSize}, + kpack = {kpack}, + BIAS = {use_bias}, + EVEN_K = {EVEN_K}, + GRID_MN = grid[0], + NUM_XCDS = {num_xcds} + ) + return c +""" + return configStr, matmul_def_str + + +def generate_compile_driver(M, N, K, col_a, col_b, dtype_a, dtype_b, dtype_c, init_type, configs, rotating_buffer_size, + bias_size): + """ + Generate a single file that contains all kernels in the tuning space. + This file is used to **compile** the kernels in parallel + """ + + filename = get_filename_compile_driver() + f_kernel = open(filename, 'w') + + # write imports + import_str = f"""import torch +import triton +import triton.language as tl +import argparse +import sys +import multiprocessing +from tune_gemm import gen_rotating_tensors +from {get_filename_without_extension(get_filename_myKernels())} import * +""" + + f_kernel.write(import_str + "\n") + + for config in configs: + EVEN_K = True if K % config.get('BLOCK_SIZE_K') == 0 else False + configStr, matmul_def_str = gen_kernel_and_configStr_from_config(config, EVEN_K, dtype_a, dtype_b, dtype_c, + bias_size, True) + # Copy the matmul_kernel with name replaced + f_kernel.write(matmul_def_str + "\n") + + # write compile_kernels + # pre string + stride_a_str = "1, M" if col_a else "M, 1" + stride_b_str = "1, N" if col_b else "N, 1" + stride_c_str = "N, 1" + compile_kernels_pre_str = f"""def compile_kernels(M, N, K, rotating_buffer_size, bias_size, num_threads): + thread_pool = multiprocessing.Pool(processes=num_threads) + + assert bias_size == M or bias_size == 0 + + stride_bias = 1 if bias_size > 0 else 0 + stride_am, stride_ak = {stride_a_str} + stride_bk, stride_bn = {stride_b_str} + stride_cm, stride_cn = {stride_c_str} + task_args = (M, N, K, + stride_am, stride_ak, + stride_bk, stride_bn, + stride_cm, stride_cn, stride_bias) + + results = [] + config_names = [] +""" + f_kernel.write(compile_kernels_pre_str + "\n") + + # warm up call of all matmul functions in parallel + for config in configs: + configStr = gen_configStr(config) + task_str = f" results += [thread_pool.apply_async(try_compile_config_{configStr}, args=task_args)]\n" + \ + f" config_names += ['{configStr}']\n" + f_kernel.write(task_str) + + threadpool_str = """ + failed_configs = [] + for i in range(len(results)): + results[i].wait() + res = results[i].get() + if not res: + failed_configs += [config_names[i]] + thread_pool.close() + thread_pool.join() + if failed_configs: + with open("{filename}.failed_configs", "w") as f: + for cfg in failed_configs: + f.write(cfg + "\\n") +""".format(filename=filename) + f_kernel.write(threadpool_str) + + # def main and call compile_kernels + def_main_str = f""" +def main(): + parser = argparse.ArgumentParser( + prog="tune a specific gemm size", + allow_abbrev=False,) + parser.add_argument("-n", type=int, default=32, help='number of threads') + parser.add_argument("-rotating_tensor", type=int, default={rotating_buffer_size}, help='size of rotating buffer (MB), default: {rotating_buffer_size}') + args = parser.parse_args() + numThreads = args.n + rotating_buffer_size = args.rotating_tensor + """ + compile_kernels_call_str = f'compile_kernels({M}, {N}, {K}, rotating_buffer_size, {bias_size}, numThreads)' + + f_kernel.write(def_main_str) + f_kernel.write(compile_kernels_call_str + "\n\n") + f_kernel.write("""if __name__ == '__main__': + sys.exit(main())""") + f_kernel.close() + + return filename + + +def generate_profile_tasks(M, N, K, col_a, col_b, dtype_a, dtype_b, dtype_c, init_type, configs, jobs, iters, run_bench, + rotating_buffer_size, bias_size, icache_flush): + """ + Open {len(jobs)} files + generated_kernelM-N-K-0.py, generated_kernelM-N-K-1.py, ..., generated_kernelM-N-K-{njobs-1}.py + and generate + 1. matmul kernels of all configs + 2. wrapper function matmul to invoke all the generated kernels + 3. test_gemm to invoke matmul in a loop of {iters} iterations + """ + + filenames = [] + for i in range(jobs): + filenames.append(get_filename_profile_driver(M, N, K, i)) + f_kernel = [open(path, 'w') for path in filenames] + + # write imports + import_str = f"""import torch +import triton +import triton.language as tl +import argparse +import sys +import multiprocessing +from tune_gemm import gen_rotating_tensors +from {get_filename_without_extension(get_filename_myKernels())} import * +""" + if icache_flush: + import_str += """ +from icache_flush import icache_flush +""" + for fi in range(jobs): + f_kernel[fi].write(import_str + "\n") + + idx = 0 + for config in configs: + file_idx = idx % jobs + EVEN_K = True if K % config.get('BLOCK_SIZE_K') == 0 else False + configStr, matmul_def_str = gen_kernel_and_configStr_from_config(config, EVEN_K, dtype_a, dtype_b, dtype_c, + bias_size, False) + # Copy the matmul_kernel with name replaced + f_kernel[file_idx].write(matmul_def_str + "\n") + idx += 1 + + # write test_gemm + # pre string + test_gemm_pre_str = f"""def test_gemm(M, N, K, rotating_buffer_size, bias_size): + tensors = gen_rotating_tensors(M, N, K, '{dtype_a}', {col_a}, '{dtype_b}', {col_b}, '{dtype_c}', + 1, '{init_type}', rotating_buffer_size, bias_size, device='cuda') + + a = tensors['input_a'][0] + b = tensors['input_b'][0] + c = tensors['output_c'][0] + assert bias_size == M or bias_size == 0 + + stride_bias = tensors['bias'][0].stride(0) if bias_size > 0 else 0 + + try: + with open("{get_filename_compile_driver()}.failed_configs", "r") as f: + failed_configs = [cfg.strip() for cfg in f.readlines()] + except Exception: + failed_configs = [] +""" + for fi in range(jobs): + f_kernel[fi].write(test_gemm_pre_str + "\n") + + # call all matmul_xxx functions + idx = 0 + runs = iters if run_bench else 120 + for config in configs: + configStr = gen_configStr(config) + matmul_call_str = f""" + if '{configStr}' not in failed_configs: + rotating_num = tensors['rotating_num'] + for i in range({runs}): + a = tensors['input_a'][i % rotating_num] + b = tensors['input_b'][i % rotating_num] + c = tensors['output_c'][i % rotating_num] + bias = tensors['bias'][i % rotating_num] if bias_size > 0 else None + bias_stride = bias.stride(0) if bias_size > 0 else 0""" + if icache_flush: + matmul_call_str += """ + icache_flush()""" + matmul_call_str += f""" + d = matmul_{configStr}(a, b, c, bias, M, N, K, a.stride(0), a.stride(1), b.stride(0), b.stride(1), c.stride(0), c.stride(1), bias_stride)""" + f_kernel[idx % jobs].write(matmul_call_str + "\n") + idx += 1 + # post string + for fi in range(jobs): + f_kernel[fi].write(" return d\n") + + # def main and call test_gemm + def_main_str = f""" +def main(): + parser = argparse.ArgumentParser( + prog="tune a specific gemm size", + allow_abbrev=False,) + parser.add_argument("-n", type=int, default=1, help='number of threads') + parser.add_argument("-rotating_tensor", type=int, default={rotating_buffer_size}, help='size of rotating buffer (MB), default: {rotating_buffer_size}') + args = parser.parse_args() + numThreads = args.n + rotating_buffer_size = args.rotating_tensor + """ + test_gemm_call_str = f'test_gemm({M}, {N}, {K}, rotating_buffer_size, {bias_size})' + for fi in range(jobs): + f_kernel[fi].write(def_main_str) + f_kernel[fi].write(test_gemm_call_str + "\n\n") + f_kernel[fi].write("""if __name__ == '__main__': + sys.exit(main())""") + f_kernel[fi].close() diff --git a/python/perf-kernels/tools/tune_gemm/utils/utils.py b/python/perf-kernels/tools/tune_gemm/utils/utils.py new file mode 100644 index 000000000000..bcebf9a3ff8d --- /dev/null +++ b/python/perf-kernels/tools/tune_gemm/utils/utils.py @@ -0,0 +1,110 @@ +import torch +import triton +import triton.language as tl + +import os +import subprocess +from datetime import datetime + +TORCH_HAS_FP8E5B16 = hasattr(torch, 'float8_e5m2fnuz') +TORCH_HAS_FP8E4B8 = hasattr(torch, 'float8_e4m3fnuz') +tl_to_torch_types = { + tl.float16: torch.float16, + tl.bfloat16: torch.bfloat16, + tl.float32: torch.float32, + tl.int8: torch.int8, + tl.int32: torch.int32, +} +if TORCH_HAS_FP8E5B16: + tl_to_torch_types[tl.float8e5b16] = torch.float8_e5m2fnuz +if TORCH_HAS_FP8E4B8: + tl_to_torch_types[tl.float8e4b8] = torch.float8_e4m3fnuz + +name_to_tl_types = { + 'int8': tl.int8, + 'int32': tl.int32, + 'fp16': tl.float16, + 'fp32': tl.float32, + 'bf16': tl.bfloat16, + 'fp8': tl.float8e4b8, + 'bf8': tl.float8e5b16, +} + + +def run_bash_command_wrapper(commandstring, capture=True): + try: + run_bash_command(commandstring, capture) + except subprocess.CalledProcessError: + if not capture: + print(f"running {commandstring} one more time") + run_bash_command(commandstring, capture) + + +def run_bash_command(commandstring, capture=True): + if capture: + proc = subprocess.run(commandstring, shell=True, check=True, executable='/bin/bash', stdout=subprocess.PIPE) + return proc.stdout.splitlines() + proc = subprocess.run(commandstring, shell=True, check=True, executable='/bin/bash') + return None + + +def get_filename_myKernels(): + path = os.path.dirname(os.path.abspath(__file__)) + return f"{path}/../myKernels.py" + + +def get_filename_without_extension(file_path): + base_name = os.path.basename(file_path) + file_name, _ = os.path.splitext(base_name) + return file_name + + +def get_filename_compile_driver(): + path = os.path.dirname(os.path.abspath(__file__)) + return f"{path}/../compile_driver.py" + + +def get_filename_profile_driver(M, N, K, job_id): + path = os.path.dirname(os.path.abspath(__file__)) + return f"{path}/../profile_driver_{M}x{N}x{K}_{job_id}.py" + + +def get_default_tuning_result_filename(): + git_branch_name = run_bash_command("git rev-parse --abbrev-ref HEAD") + git_branch_name = git_branch_name[0].decode() + # handle branch name of "xxx/xxx" format + git_branch_name = git_branch_name.replace('/', '_') + git_commit_hash = run_bash_command("git rev-parse --short HEAD") + git_commit_hash = git_commit_hash[0].decode() + + dt_string = datetime.now().strftime("%m-%d-%Y-%H:%M:%S") + + path = os.path.dirname(os.path.abspath(__file__)) + defaultName = f"{path}/../tuning_results_{git_branch_name}@{git_commit_hash}_{dt_string}.yaml" + return defaultName + + +def patch_triton_compiler(): + device = triton.runtime.driver.active.get_current_device() + stream = triton.runtime.driver.active.get_current_stream(device) + target = triton.runtime.driver.active.get_current_target() + + triton_location_str = run_bash_command("pip show triton | grep Editable") + if not triton_location_str: + print("triton source not found from pip show triton") + + triton_dir = triton_location_str[0].split()[-1].decode('utf-8') + + jit_filename = os.path.join(triton_dir, "triton/runtime", "jit.py") + + run_bash_command(f"sed -i 's/driver.active.get_current_device()/{device}/g' {jit_filename}") + run_bash_command(f"sed -i 's/driver.active.get_current_stream(device)/{stream}/g' {jit_filename}") + + hip_driver_filename = os.path.join(triton_dir, "../third_party/amd/backend/", "driver.py") + cuda_driver_filename = os.path.join(triton_dir, "../third_party/nvidia/backend/", "driver.py") + + run_bash_command(f"sed -i 's/import torch/return True/g' {hip_driver_filename}") + run_bash_command( + f"sed -i 's/device = self.get_current_device()/return GPUTarget(\"hip\", \"{target.arch}\", 64)/g' {hip_driver_filename}" + ) + run_bash_command(f"sed -i 's/import torch/return False/g' {cuda_driver_filename}")