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Release the static code to PyPi +# For more information see: https://help.github.com/en/actions/language-and-framework-guides/using-python-with-github-actions#publishing-to-package-registries + +name: Build wheels and deploy + +on: + create: + tags: + - v* + +jobs: + + setup_release: + name: Create Release + runs-on: ubuntu-latest + steps: + - name: Get the tag version + id: extract_branch + run: echo ::set-output name=branch::${GITHUB_REF#refs/tags/} + shell: bash + + - name: Create Release + id: create_release + uses: actions/create-release@v1 + env: + GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} + with: + tag_name: ${{ steps.extract_branch.outputs.branch }} + release_name: ${{ steps.extract_branch.outputs.branch }} + + build_wheels: + name: Build Wheel + needs: setup_release + runs-on: ${{ matrix.os }} + + strategy: + fail-fast: false + matrix: + # Using ubuntu-20.04 instead of 22.04 for more compatibility (glibc). Ideally we'd use the + # manylinux docker image, but I haven't figured out how to install CUDA on manylinux. + os: [ubuntu-20.04] + python-version: ['3.8', '3.9', '3.10', '3.11', '3.12'] + torch-version: ['2.0.1', '2.1.2', '2.2.2', '2.3.1', '2.4.0.dev20240512'] + cuda-version: ['11.8.0', '12.2.2'] + # We need separate wheels that either uses C++11 ABI (-D_GLIBCXX_USE_CXX11_ABI) or not. + # Pytorch wheels currently don't use it, but nvcr images have Pytorch compiled with C++11 ABI. + # Without this we get import error (undefined symbol: _ZN3c105ErrorC2ENS_14SourceLocationESs) + # when building without C++11 ABI and using it on nvcr images. + cxx11_abi: ['FALSE', 'TRUE'] + exclude: + # see https://github.com/pytorch/pytorch/blob/main/RELEASE.md#release-compatibility-matrix + # Pytorch < 2.2 does not support Python 3.12 + - torch-version: '2.0.1' + python-version: '3.12' + - torch-version: '2.1.2' + python-version: '3.12' + # Pytorch <= 2.0 only supports CUDA <= 11.8 + - torch-version: '2.0.1' + cuda-version: '12.2.2' + + steps: + - name: Checkout + uses: actions/checkout@v3 + + - name: Set up Python + uses: actions/setup-python@v4 + with: + python-version: ${{ matrix.python-version }} + + - name: Set CUDA and PyTorch versions + run: | + echo "MATRIX_CUDA_VERSION=$(echo ${{ matrix.cuda-version }} | awk -F \. {'print $1 $2'})" >> $GITHUB_ENV + echo "MATRIX_TORCH_VERSION=$(echo ${{ matrix.torch-version }} | awk -F \. {'print $1 "." $2'})" >> $GITHUB_ENV + + - name: Free up disk space + if: ${{ runner.os == 'Linux' }} + # https://github.com/easimon/maximize-build-space/blob/master/action.yml + # https://github.com/easimon/maximize-build-space/tree/test-report + run: | + sudo rm -rf /usr/share/dotnet + sudo rm -rf /opt/ghc + sudo rm -rf /opt/hostedtoolcache/CodeQL + + - name: Set up swap space + if: runner.os == 'Linux' + uses: pierotofy/set-swap-space@v1.0 + with: + swap-size-gb: 10 + + - name: Install CUDA ${{ matrix.cuda-version }} + if: ${{ matrix.cuda-version != 'cpu' }} + uses: Jimver/cuda-toolkit@v0.2.14 + id: cuda-toolkit + with: + cuda: ${{ matrix.cuda-version }} + linux-local-args: '["--toolkit"]' + # default method is "local", and we're hitting some error with caching for CUDA 11.8 and 12.1 + # method: ${{ (matrix.cuda-version == '11.8.0' || matrix.cuda-version == '12.1.0') && 'network' || 'local' }} + method: 'network' + # We need the cuda libraries (e.g. cuSparse, cuSolver) for compiling PyTorch extensions, + # not just nvcc + # sub-packages: '["nvcc"]' + + - name: Install PyTorch ${{ matrix.torch-version }}+cu${{ matrix.cuda-version }} + run: | + pip install --upgrade pip + # If we don't install before installing Pytorch, we get error for torch 2.0.1 + # ERROR: Could not find a version that satisfies the requirement setuptools>=40.8.0 (from versions: none) + pip install lit + # For some reason torch 2.2.0 on python 3.12 errors saying no setuptools + pip install setuptools + # We want to figure out the CUDA version to download pytorch + # e.g. we can have system CUDA version being 11.7 but if torch==1.12 then we need to download the wheel from cu116 + # see https://github.com/pytorch/pytorch/blob/main/RELEASE.md#release-compatibility-matrix + # This code is ugly, maybe there's a better way to do this. + export TORCH_CUDA_VERSION=$(python -c "from os import environ as env; \ + minv = {'2.0': 117, '2.1': 118, '2.2': 118, '2.3': 118, '2.4': 118}[env['MATRIX_TORCH_VERSION']]; \ + maxv = {'2.0': 118, '2.1': 121, '2.2': 121, '2.3': 121, '2.4': 121}[env['MATRIX_TORCH_VERSION']]; \ + print(max(min(int(env['MATRIX_CUDA_VERSION']), maxv), minv))" \ + ) + if [[ ${{ matrix.torch-version }} == *"dev"* ]]; then + pip install --no-cache-dir --pre torch==${{ matrix.torch-version }} --index-url https://download.pytorch.org/whl/nightly/cu${TORCH_CUDA_VERSION} + else + pip install --no-cache-dir torch==${{ matrix.torch-version }} --index-url https://download.pytorch.org/whl/cu${TORCH_CUDA_VERSION} + fi + nvcc --version + python --version + python -c "import torch; print('PyTorch:', torch.__version__)" + python -c "import torch; print('CUDA:', torch.version.cuda)" + python -c "from torch.utils import cpp_extension; print (cpp_extension.CUDA_HOME)" + shell: + bash + + - name: Build wheel + run: | + # We want setuptools >= 49.6.0 otherwise we can't compile the extension if system CUDA version is 11.7 and pytorch cuda version is 11.6 + # https://github.com/pytorch/pytorch/blob/664058fa83f1d8eede5d66418abff6e20bd76ca8/torch/utils/cpp_extension.py#L810 + # However this still fails so I'm using a newer version of setuptools + pip install setuptools==68.0.0 + pip install ninja packaging wheel + export PATH=/usr/local/nvidia/bin:/usr/local/nvidia/lib64:$PATH + export LD_LIBRARY_PATH=/usr/local/nvidia/lib64:/usr/local/cuda/lib64:$LD_LIBRARY_PATH + # Limit MAX_JOBS otherwise the github runner goes OOM + # CUDA 11.8 can compile with 2 jobs, but CUDA 12.2 goes OOM + MAX_JOBS=$([ "$MATRIX_CUDA_VERSION" == "122" ] && echo 1 || echo 2) FLASH_ATTENTION_FORCE_BUILD="TRUE" FLASH_ATTENTION_FORCE_CXX11_ABI=${{ matrix.cxx11_abi}} python setup.py bdist_wheel --dist-dir=dist + tmpname=cu${MATRIX_CUDA_VERSION}torch${MATRIX_TORCH_VERSION}cxx11abi${{ matrix.cxx11_abi }} + wheel_name=$(ls dist/*whl | xargs -n 1 basename | sed "s/-/+$tmpname-/2") + ls dist/*whl |xargs -I {} mv {} dist/${wheel_name} + echo "wheel_name=${wheel_name}" >> $GITHUB_ENV + + - name: Log Built Wheels + run: | + ls dist + + - name: Get the tag version + id: extract_branch + run: echo ::set-output name=branch::${GITHUB_REF#refs/tags/} + + - name: Get Release with tag + id: get_current_release + uses: joutvhu/get-release@v1 + with: + tag_name: ${{ steps.extract_branch.outputs.branch }} + env: + GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} + + - name: Upload Release Asset + id: upload_release_asset + uses: actions/upload-release-asset@v1 + env: + GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} + with: + upload_url: ${{ steps.get_current_release.outputs.upload_url }} + asset_path: ./dist/${{env.wheel_name}} + asset_name: ${{env.wheel_name}} + asset_content_type: application/* + + publish_package: + name: Publish package + needs: [build_wheels] + + runs-on: ubuntu-latest + + steps: + - uses: actions/checkout@v3 + + - uses: actions/setup-python@v4 + with: + python-version: '3.10' + + - name: Install dependencies + run: | + pip install ninja packaging setuptools wheel twine + # We don't want to download anything CUDA-related here + pip install torch --index-url https://download.pytorch.org/whl/cpu + + - name: Build core package + env: + FLASH_ATTENTION_SKIP_CUDA_BUILD: "TRUE" + run: | + python setup.py sdist --dist-dir=dist + + - name: Deploy + env: + TWINE_USERNAME: "__token__" + TWINE_PASSWORD: ${{ secrets.PYPI_API_TOKEN }} + run: | + python -m twine upload dist/* diff --git a/flash-attention/assets/flash2_a100_fwd_bwd_benchmark.png b/flash-attention/assets/flash2_a100_fwd_bwd_benchmark.png new file 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Tri Dao. + +import pickle +import math +import torch +import torch.nn as nn +import torch.nn.functional as F + +from einops import rearrange, repeat +from flash_attn.layers.rotary import apply_rotary_emb + +from flash_attn.utils.benchmark import benchmark_all, benchmark_forward, benchmark_backward +from flash_attn.utils.benchmark import benchmark_fwd_bwd, benchmark_combined + +from flash_attn import flash_attn_qkvpacked_func, flash_attn_func + +try: + import xformers.ops as xops +except ImportError: + xops = None + + +def generate_cos_sin(seqlen, rotary_dim, device, dtype): + assert rotary_dim % 2 == 0 + angle = torch.rand(seqlen * 2, rotary_dim // 2, device=device) * 2 * math.pi + cos = torch.cos(angle).to(dtype=dtype) + sin = torch.sin(angle).to(dtype=dtype) + return cos, sin + + +def flash_rotary(q, k, v, cos, sin, causal=False): + # corrected by @tridao comments + q = apply_rotary_emb( + q, cos, sin, seqlen_offsets=0, interleaved=False, inplace=True + ) + k = apply_rotary_emb( + k, cos, sin, seqlen_offsets=0, interleaved=False, inplace=True + ) + + return flash_attn_func(q, k, v, causal=causal) + + +def attn_bias_from_alibi_slopes( + slopes, seqlen_q, seqlen_k, query_padding_mask=None, key_padding_mask=None, causal=False +): + batch, nheads = slopes.shape + device = slopes.device + slopes = rearrange(slopes, "b h -> b h 1 1") + if causal: + return torch.arange(-seqlen_k + 1, 1, device=device, dtype=torch.float32) * slopes + else: + row_idx = rearrange(torch.arange(seqlen_q, device=device, dtype=torch.long), "s -> s 1") + col_idx = torch.arange(seqlen_k, device=device, dtype=torch.long) + sk = ( + seqlen_k + if key_padding_mask is None + else rearrange(key_padding_mask.sum(-1), "b -> b 1 1 1") + ) + sq = ( + seqlen_q + if query_padding_mask is None + else rearrange(query_padding_mask.sum(-1), "b -> b 1 1 1") + ) + relative_pos = torch.abs(row_idx + sk - sq - col_idx) + return -slopes * relative_pos.to(dtype=slopes.dtype) + + +def flops(batch, seqlen, headdim, nheads, causal, mode="fwd"): + assert mode in ["fwd", "bwd", "fwd_bwd"] + f = 4 * batch * seqlen**2 * nheads * headdim // (2 if causal else 1) + return f if mode == "fwd" else (2.5 * f if mode == "bwd" else 3.5 * f) + + +def efficiency(flop, time): + return (flop / time / 10**12) if not math.isnan(time) else 0.0 + + +def attention_pytorch(q, k, v, dropout_p=0.0, causal=True, attn_bias=None): + """ + Arguments: + q, k, v: (batch_size, seqlen, nheads, head_dim) + dropout_p: float + attn_bias: (batch_size, nheads, seqlen, seqlen) or (1, nheads, seqlen, seqlen) + Output: + output: (batch_size, seqlen, nheads, head_dim) + """ + batch_size, seqlen, nheads, d = q.shape + q = rearrange(q, 'b t h d -> (b h) t d') + k = rearrange(k, 'b s h d -> (b h) d s') + softmax_scale = 1.0 / math.sqrt(d) + # Preallocate attn_weights for `baddbmm` + if attn_bias is not None: + scores = rearrange(attn_bias, 'b h t s -> (b h) t s') + else: + scores = torch.empty(batch_size * nheads, seqlen, seqlen, dtype=q.dtype, device=q.device) + scores = rearrange(torch.baddbmm(scores, q, k, beta=1.0, alpha=softmax_scale), + '(b h) t s -> b h t s', h=nheads) + if causal: + # "triu_tril_cuda_template" not implemented for 'BFloat16' + # So we have to construct the mask in float + causal_mask = torch.triu(torch.full((seqlen, seqlen), -10000.0, device=scores.device), 1) + # TD [2022-09-30]: Adding is faster than masked_fill_ (idk why, just better kernel I guess) + scores = scores + causal_mask.to(dtype=scores.dtype) + attention = torch.softmax(scores, dim=-1) + attention_drop = F.dropout(attention, dropout_p) + output = torch.einsum('bhts,bshd->bthd', attention_drop , v) + return output.to(dtype=q.dtype) + + +def time_fwd_bwd(func, *args, **kwargs): + time_f, time_b = benchmark_fwd_bwd(func, *args, **kwargs) + return time_f[1].mean, time_b[1].mean + + +repeats = 30 +device = 'cuda' +dtype = torch.float16 + +bs_seqlen_vals = [(32, 512), (16, 1024), (8, 2048), (4, 4096), (2, 8192), (1, 16384)] +causal_vals = [False, True] +headdim_vals = [64, 128] +dim = 2048 +dropout_p = 0.0 + +methods = (["fa2_alibi", "torch"] + + (["xformers"] if xops is not None else []) + + ["sdpa"] + + ["fa2_baseline"] + + ["fa2_rotary"]) + +time_f = {} +time_b = {} +time_f_b = {} +speed_f = {} +speed_b = {} +speed_f_b = {} +for causal in causal_vals: + for headdim in headdim_vals: + for batch_size, seqlen in bs_seqlen_vals: + config = (causal, headdim, batch_size, seqlen) + nheads = dim // headdim + q, k, v = [torch.randn(batch_size, seqlen, nheads, headdim, device=device, dtype=dtype, + requires_grad=True) for _ in range(3)] + # alibi_slopes = torch.rand(batch_size, nheads, device=device, dtype=torch.float32) * 0.3 + alibi_slopes = torch.rand(1, nheads, device=device, dtype=torch.float32) * 0.3 + attn_bias = attn_bias_from_alibi_slopes(alibi_slopes, seqlen, seqlen, causal=causal).to(dtype) + attn_bias = repeat(attn_bias, "1 ... -> b ...", b=batch_size) + f, b = time_fwd_bwd( + flash_attn_func, + q, k, v, + dropout_p, + causal=causal, + # alibi_slopes=alibi_slopes, + alibi_slopes=None, + repeats=repeats, + verbose=False + ) + time_f[config, "fa2_baseline"] = f + time_b[config, "fa2_baseline"] = b + + q = q.detach().requires_grad_(True) + k = k.detach().requires_grad_(True) + v = v.detach().requires_grad_(True) + f, b = time_fwd_bwd( + flash_attn_func, + q, k, v, + dropout_p, + causal=causal, + alibi_slopes=rearrange(alibi_slopes, "1 h -> h"), + # alibi_slopes=None, + repeats=repeats, + verbose=False + ) + time_f[config, "fa2_alibi"] = f + time_b[config, "fa2_alibi"] = b + + try: + q = q.detach().requires_grad_(True) + k = k.detach().requires_grad_(True) + v = v.detach().requires_grad_(True) + f, b = time_fwd_bwd( + attention_pytorch, + q, k, v, + dropout_p, + causal=causal, + attn_bias=attn_bias, + repeats=repeats, + verbose=False + ) + except: # Skip if OOM + f, b = float('nan'), float('nan') + time_f[config, "torch"] = f + time_b[config, "torch"] = b + + # F.sdpa doesn't currently (torch 2.1) dispatch to flash-attn but just to be safe + with torch.backends.cuda.sdp_kernel(enable_flash=False): + q_pt = q.detach().requires_grad_(True).transpose(1, 2) + k_pt = k.detach().requires_grad_(True).transpose(1, 2) + v_pt = v.detach().requires_grad_(True).transpose(1, 2) + f, b = time_fwd_bwd( + F.scaled_dot_product_attention, + q_pt, k_pt, v_pt, + attn_mask=attn_bias, + dropout_p=dropout_p, + is_causal=causal, + repeats=repeats, + verbose=False + ) + time_f[config, "sdpa"] = f + time_b[config, "sdpa"] = b + + if xops is not None: + q = q.detach().requires_grad_(True) + k = k.detach().requires_grad_(True) + v = v.detach().requires_grad_(True) + if causal: + attn_bias_xops = xops.LowerTriangularMask().add_bias(attn_bias.expand(-1, -1, seqlen, -1).to(dtype=q.dtype)) + # NotImplementedError: No operator found for `memory_efficient_attention_backward` with inputs: + # `flshattB@v2.3.6` is not supported because: + # attn_bias type is + # `cutlassB` is not supported because: + # attn_bias type is + attn_bias_xops = attn_bias_xops.materialize((batch_size, nheads, seqlen, seqlen), dtype=q.dtype, device=device) + else: + attn_bias_xops = attn_bias.to(dtype=q.dtype) + f, b = time_fwd_bwd( + xops.memory_efficient_attention, + q, k, v, + attn_bias_xops, + dropout_p, + repeats=repeats, + verbose=False + ) + time_f[config, "xformers"] = f + time_b[config, "xformers"] = b + + q = q.detach().requires_grad_(True) + k = k.detach().requires_grad_(True) + v = v.detach().requires_grad_(True) + cos, sin = generate_cos_sin(seqlen, headdim, device, dtype) + f, b = time_fwd_bwd( + flash_rotary, + q, k, v, + cos, sin, + causal, + repeats=repeats, + verbose=False + ) + time_f[config, "fa2_rotary"] = f + time_b[config, "fa2_rotary"] = b + + print(f"### causal={causal}, headdim={headdim}, batch_size={batch_size}, seqlen={seqlen} ###") + csv_output = "" + csv_output += f"{causal},{headdim},{batch_size},{seqlen}," + for method in methods: + time_f_b[config, method] = time_f[config, method] + time_b[config, method] + speed_f[config, method] = efficiency( + flops(batch_size, seqlen, headdim, nheads, causal, mode="fwd"), + time_f[config, method] + ) + speed_b[config, method] = efficiency( + flops(batch_size, seqlen, headdim, nheads, causal, mode="bwd"), + time_b[config, method] + ) + speed_f_b[config, method] = efficiency( + flops(batch_size, seqlen, headdim, nheads, causal, mode="fwd_bwd"), + time_f_b[config, method] + ) + print( + f"{method} fwd: {speed_f[config, method]:.2f} TFLOPs/s, " + f"bwd: {speed_b[config, method]:.2f} TFLOPs/s, " + f"fwd + bwd: {speed_f_b[config, method]:.2f} TFLOPs/s" + ) + csv_output += f"{speed_f[config, method]:.2f},{speed_b[config, method]:.2f},{speed_f_b[config, method]:.2f}," + print(csv_output) diff --git a/flash-attention/benchmarks/benchmark_causal.py b/flash-attention/benchmarks/benchmark_causal.py new file mode 100644 index 0000000000000000000000000000000000000000..6c4797c83e0cc5c100d991a9d847f1e9b4351002 --- /dev/null +++ b/flash-attention/benchmarks/benchmark_causal.py @@ -0,0 +1,225 @@ +from functools import partial +import math +import torch +import torch.nn as nn +import torch.nn.functional as F + +from einops import rearrange, repeat + +# from flash_attn.utils.benchmark import benchmark_forward, benchmark_backward, benchmark_combined, benchmark_all, benchmark_fwd_bwd, pytorch_profiler +from flash_attn.utils.benchmark import benchmark_forward, benchmark_backward, benchmark_combined, benchmark_all, benchmark_fwd_bwd, pytorch_profiler +from flash_attn.flash_attn_interface import flash_attn_varlen_qkvpacked_func +# # from flash_attn.triton.fused_attention import attention as attention +# from flash_attn.flash_attn_triton import flash_attn_qkvpacked_func +# from flash_attn.flash_attn_triton_og import attention as attention_og + +# from triton.ops.flash_attention import attention as attention_triton + +from flash_attn import flash_attn_qkvpacked_func, flash_attn_kvpacked_func + +try: + from flash_attn.fused_softmax import scaled_upper_triang_masked_softmax +except ImportError: + scaled_upper_triang_masked_softmax = None + + +def attention_pytorch(qkv, dropout_p=0.0, causal=True): + """ + Arguments: + qkv: (batch_size, seqlen, 3, nheads, head_dim) + dropout_p: float + Output: + output: (batch_size, seqlen, nheads, head_dim) + """ + batch_size, seqlen, _, nheads, d = qkv.shape + q, k, v = qkv.unbind(dim=2) + q = rearrange(q, 'b t h d -> (b h) t d') + k = rearrange(k, 'b s h d -> (b h) d s') + softmax_scale = 1.0 / math.sqrt(d) + # Preallocate attn_weights for `baddbmm` + scores = torch.empty(batch_size * nheads, seqlen, seqlen, dtype=qkv.dtype, device=qkv.device) + scores = rearrange(torch.baddbmm(scores, q, k, beta=0, alpha=softmax_scale), + '(b h) t s -> b h t s', h=nheads) + if causal: + # "triu_tril_cuda_template" not implemented for 'BFloat16' + # So we have to construct the mask in float + causal_mask = torch.triu(torch.full((seqlen, seqlen), -10000.0, device=scores.device), 1) + # TD [2022-09-30]: Adding is faster than masked_fill_ (idk why, just better kernel I guess) + scores = scores + causal_mask.to(dtype=scores.dtype) + attention = torch.softmax(scores, dim=-1) + attention_drop = F.dropout(attention, dropout_p) + output = torch.einsum('bhts,bshd->bthd', attention_drop , v) + return output.to(dtype=qkv.dtype) + + +def attention_megatron(qkv): + """ + Arguments: + qkv: (batch_size, seqlen, 3, nheads, head_dim) + Output: + output: (batch_size, seqlen, nheads, head_dim) + """ + batch_size, seqlen, _, nheads, d = qkv.shape + q, k, v = qkv.unbind(dim=2) + q = rearrange(q, 'b t h d -> (b h) t d') + k = rearrange(k, 'b s h d -> (b h) d s') + softmax_scale = 1.0 / math.sqrt(d) + # Preallocate attn_weights for `baddbmm` + scores = torch.empty(batch_size * nheads, seqlen, seqlen, dtype=qkv.dtype, device=qkv.device) + scores = rearrange(torch.baddbmm(scores, q, k, beta=0, alpha=softmax_scale), + '(b h) t s -> b h t s', h=nheads) + attention = scaled_upper_triang_masked_softmax(scores, None, scale=1.0) + output = torch.einsum('bhts,bshd->bthd', attention, v) + return output.to(dtype=qkv.dtype) + + +torch.manual_seed(0) +repeats = 30 +batch_size = 8 +seqlen = 2048 +nheads = 12 +headdim = 128 +# nheads = 24 +# headdim = 64 +# batch_size = 64 +# seqlen = 512 +# nheads = 8 +# headdim = 128 +dropout_p = 0.0 +causal = True +dtype = torch.float16 +device = 'cuda' + +qkv = torch.randn(batch_size, seqlen, 3, nheads, headdim, device=device, dtype=dtype, + requires_grad=True) +cu_seqlens = torch.arange(0, (batch_size + 1) * seqlen, step=seqlen, dtype=torch.int32, + device=qkv.device) + +qkv_unpad = rearrange(qkv, 'b s ... -> (b s) ...').detach().requires_grad_(True) +# benchmark_all(flash_attn_varlen_qkvpacked_func, qkv_unpad, +# cu_seqlens, seqlen, dropout_p, causal=causal, repeats=repeats, desc='FlashAttention') +# pytorch_profiler(flash_attn_varlen_qkvpacked_func, qkv_unpad, +# cu_seqlens, seqlen, dropout_p, causal=causal, backward=True) +benchmark_forward(flash_attn_qkvpacked_func, qkv, dropout_p, causal=causal, repeats=repeats, desc='Fav2') +pytorch_profiler(flash_attn_qkvpacked_func, qkv, dropout_p, causal=causal, backward=False) + +# for dropout_p in [0.1, 0.0]: +# for causal in [False, True]: +# print(f"### {dropout_p = }, {causal = } ###") +# pytorch_profiler(fav2_qkvpacked_func, qkv, dropout_p, causal=causal, backward=True) + + +# nheads_k = 2 +# q = torch.randn(batch_size, seqlen, nheads, headdim, device=device, dtype=dtype, requires_grad=True) +# kv = torch.randn(batch_size, seqlen, 2, nheads_k, headdim, device=device, dtype=dtype, +# requires_grad=True) +# if fav2_kvpacked_func is not None: +# benchmark_all(fav2_kvpacked_func, q, kv, dropout_p, causal=causal, repeats=repeats, desc='Fav2') +# pytorch_profiler(fav2_kvpacked_func, q, kv, dropout_p, causal=causal, backward=True) + +# dropout_p = 0.0 +# causal = False +# benchmark_all(attention_pytorch, qkv, dropout_p, causal=causal, +# repeats=repeats, desc='PyTorch Attention') + +# benchmark_all(flash_attn_qkvpacked_func, qkv, None, causal, repeats=repeats, desc='FlashAttention Triton') +# pytorch_profiler(flash_attn_qkvpacked_func, qkv, None, causal, backward=True) + +# q, k, v = [torch.randn(batch_size, nheads, seqlen, headdim, device=device, dtype=dtype, +# requires_grad=True) for _ in range(3)] +# benchmark_all(attention_og, q, k, v, 1.0, repeats=repeats, desc='FlashAttention Triton OG') +# # pytorch_profiler(attention, q, k, v, 1.0, backward=True) + +# if scaled_upper_triang_masked_softmax is not None: +# benchmark_all(attention_megatron, qkv, repeats=repeats, desc='Megatron Attention') + +# from src.ops.fftconv import fftconv_func + +# dim = nheads * headdim +# u = torch.randn(batch_size, dim, seqlen, device=device, dtype=dtype, requires_grad=True) +# k = torch.randn(dim, seqlen, device=device, requires_grad=True) +# D = torch.randn(dim, device=device, requires_grad=True) +# benchmark_all(fftconv_func, u, k, D, repeats=repeats, desc='FFTConv') +# pytorch_profiler(fftconv_func, u, k, D, backward=True) +# pytorch_profiler(torch.fft.rfft, u.float()) + +flops = 4 * batch_size * seqlen ** 2 * nheads * headdim +ideal_a100_time = flops / 312 / 1e9 +print(f"Ideal A100 fwd time: {ideal_a100_time:.3f}ms, bwd time: {ideal_a100_time * 2.5:.3f}ms") +exit(0) + + +def time_fwd_bwd(func, *args, **kwargs): + time_f, time_b = benchmark_fwd_bwd(func, *args, **kwargs) + return time_f[1].mean, time_b[1].mean + +bs_seqlen_vals = [(32, 512), (16, 1024), (8, 2048), (4, 4096), (2, 8192), (1, 16384)] +causal_vals = [False, True] +headdim_vals = [64, 128] +dim = 2048 +dropout_p = 0.0 + +time_f = {} +time_b = {} +for causal in causal_vals: + for headdim in headdim_vals: + for batch_size, seqlen in bs_seqlen_vals: + nheads = dim // headdim + qkv = torch.randn(batch_size, seqlen, 3, nheads, headdim, device=device, dtype=dtype, + requires_grad=True) + cu_seqlens = torch.arange(0, (batch_size + 1) * seqlen, step=seqlen, dtype=torch.int32, + device=qkv.device) + qkv_unpad = rearrange(qkv, 'b s ... -> (b s) ...').detach().requires_grad_(True) + f, b = time_fwd_bwd( + flash_attn_varlen_qkvpacked_func, qkv_unpad, cu_seqlens, seqlen, dropout_p, + causal=causal, repeats=repeats, verbose=False + ) + time_f[(causal, headdim, batch_size, seqlen), "Flash"] = f + time_b[(causal, headdim, batch_size, seqlen), "Flash"] = b + + qkv = qkv.detach().requires_grad_(True) + f, b = time_fwd_bwd( + fav2_qkvpacked_func, qkv, dropout_p, causal=causal, repeats=repeats, verbose=False + ) + time_f[(causal, headdim, batch_size, seqlen), "Flash2"] = f + time_b[(causal, headdim, batch_size, seqlen), "Flash2"] = b + + # q, k, v = [torch.randn(batch_size, nheads, seqlen, headdim, device=device, dtype=dtype, + # requires_grad=True) for _ in range(3)] + # # Try both values of sequence_parallel and pick the faster one + # f, b = time_fwd_bwd( + # attention_triton, q, k, v, causal, headdim**(-0.5), + # False, repeats=repeats, verbose=False + # ) + # _, b0 = time_fwd_bwd( + # attention_triton, q, k, v, causal, headdim**(-0.5), + # True, repeats=repeats, verbose=False + # ) + # time_f[(causal, headdim, batch_size, seqlen), "Triton"] = f + # time_b[(causal, headdim, batch_size, seqlen), "Triton"] = min(b, b0) + + if seqlen <= 8 * 1024: + qkv = qkv.detach().requires_grad_(True) + f, b = time_fwd_bwd( + attention_pytorch, qkv, dropout_p, causal=causal, repeats=repeats, verbose=False + ) + else: + f, b = float('nan'), float('nan') + time_f[(causal, headdim, batch_size, seqlen), "Pytorch"] = f + time_b[(causal, headdim, batch_size, seqlen), "Pytorch"] = b + + # q, k, v = [torch.randn(batch_size, seqlen, nheads, headdim, device=device, dtype=dtype, + # requires_grad=True) for _ in range(3)] + # import xformers.ops as xops + # f, b = time_fwd_bwd( + # xops.memory_efficient_attention, q, k, v, + # attn_bias=xops.LowerTriangularMask() if causal else None, + # op=(xops.fmha.cutlass.FwOp, xops.fmha.cutlass.BwOp) + # ) + # time_f[(causal, headdim, batch_size, seqlen), "xformers"] = f + # time_b[(causal, headdim, batch_size, seqlen), "xformers"] = b + + +import pickle +with open('flash2_attn_time_h100.plk', 'wb') as fp: + pickle.dump((time_f, time_b), fp, protocol=pickle.HIGHEST_PROTOCOL) diff --git a/flash-attention/benchmarks/benchmark_flash_attention.py b/flash-attention/benchmarks/benchmark_flash_attention.py new file mode 100644 index 0000000000000000000000000000000000000000..341ae4b213901d8b5406143bf6c8abb50bc422bd --- /dev/null +++ b/flash-attention/benchmarks/benchmark_flash_attention.py @@ -0,0 +1,180 @@ +# Install the newest triton version with +# pip install "git+https://github.com/openai/triton.git#egg=triton&subdirectory=python" +import pickle +import math +import torch +import torch.nn as nn +import torch.nn.functional as F + +from einops import rearrange, repeat + +from flash_attn.utils.benchmark import benchmark_all, benchmark_forward, benchmark_backward +from flash_attn.utils.benchmark import benchmark_fwd_bwd, benchmark_combined + +from flash_attn import flash_attn_qkvpacked_func + +try: + from triton.ops.flash_attention import attention as attention_triton +except ImportError: + attention_triton = None + +try: + import xformers.ops as xops +except ImportError: + xops = None + + +def flops(batch, seqlen, headdim, nheads, causal, mode="fwd"): + assert mode in ["fwd", "bwd", "fwd_bwd"] + f = 4 * batch * seqlen**2 * nheads * headdim // (2 if causal else 1) + return f if mode == "fwd" else (2.5 * f if mode == "bwd" else 3.5 * f) + +def efficiency(flop, time): + return (flop / time / 10**12) if not math.isnan(time) else 0.0 + + +def attention_pytorch(qkv, dropout_p=0.0, causal=True): + """ + Arguments: + qkv: (batch_size, seqlen, 3, nheads, head_dim) + dropout_p: float + Output: + output: (batch_size, seqlen, nheads, head_dim) + """ + batch_size, seqlen, _, nheads, d = qkv.shape + q, k, v = qkv.unbind(dim=2) + q = rearrange(q, 'b t h d -> (b h) t d') + k = rearrange(k, 'b s h d -> (b h) d s') + softmax_scale = 1.0 / math.sqrt(d) + # Preallocate attn_weights for `baddbmm` + scores = torch.empty(batch_size * nheads, seqlen, seqlen, dtype=qkv.dtype, device=qkv.device) + scores = rearrange(torch.baddbmm(scores, q, k, beta=0, alpha=softmax_scale), + '(b h) t s -> b h t s', h=nheads) + if causal: + # "triu_tril_cuda_template" not implemented for 'BFloat16' + # So we have to construct the mask in float + causal_mask = torch.triu(torch.full((seqlen, seqlen), -10000.0, device=scores.device), 1) + # TD [2022-09-30]: Adding is faster than masked_fill_ (idk why, just better kernel I guess) + scores = scores + causal_mask.to(dtype=scores.dtype) + attention = torch.softmax(scores, dim=-1) + attention_drop = F.dropout(attention, dropout_p) + output = torch.einsum('bhts,bshd->bthd', attention_drop , v) + return output.to(dtype=qkv.dtype) + + +def time_fwd_bwd(func, *args, **kwargs): + time_f, time_b = benchmark_fwd_bwd(func, *args, **kwargs) + return time_f[1].mean, time_b[1].mean + + +repeats = 30 +device = 'cuda' +dtype = torch.float16 + +bs_seqlen_vals = [(32, 512), (16, 1024), (8, 2048), (4, 4096), (2, 8192), (1, 16384)] +causal_vals = [False, True] +headdim_vals = [64, 128] +dim = 2048 +dropout_p = 0.0 + +methods = (["Flash2", "Pytorch"] + + (["Triton"] if attention_triton is not None else []) + + (["xformers.c"] if xops is not None else []) + + (["xformers.f"] if xops is not None else [])) + +time_f = {} +time_b = {} +time_f_b = {} +speed_f = {} +speed_b = {} +speed_f_b = {} +for causal in causal_vals: + for headdim in headdim_vals: + for batch_size, seqlen in bs_seqlen_vals: + config = (causal, headdim, batch_size, seqlen) + nheads = dim // headdim + qkv = torch.randn(batch_size, seqlen, 3, nheads, headdim, device=device, dtype=dtype, + requires_grad=True) + f, b = time_fwd_bwd( + flash_attn_qkvpacked_func, qkv, dropout_p, causal=causal, repeats=repeats, verbose=False + ) + time_f[config, "Flash2"] = f + time_b[config, "Flash2"] = b + + try: + qkv = qkv.detach().requires_grad_(True) + f, b = time_fwd_bwd( + attention_pytorch, qkv, dropout_p, causal=causal, repeats=repeats, verbose=False + ) + except: # Skip if OOM + f, b = float('nan'), float('nan') + time_f[config, "Pytorch"] = f + time_b[config, "Pytorch"] = b + + if attention_triton is not None: + q, k, v = [torch.randn(batch_size, nheads, seqlen, headdim, device=device, dtype=dtype, + requires_grad=True) for _ in range(3)] + # Try both values of sequence_parallel and pick the faster one + try: + f, b = time_fwd_bwd( + attention_triton, q, k, v, causal, headdim**(-0.5), + False, repeats=repeats, verbose=False + ) + except: + f, b = float('nan'), float('inf') + try: + _, b0 = time_fwd_bwd( + attention_triton, q, k, v, causal, headdim**(-0.5), + True, repeats=repeats, verbose=False + ) + except: + b0 = float('inf') + time_f[config, "Triton"] = f + time_b[config, "Triton"] = min(b, b0) if min(b, b0) < float('inf') else float('nan') + + if xops is not None: + q, k, v = [torch.randn(batch_size, seqlen, nheads, headdim, device=device, dtype=dtype, + requires_grad=True) for _ in range(3)] + f, b = time_fwd_bwd( + xops.memory_efficient_attention, q, k, v, + attn_bias=xops.LowerTriangularMask() if causal else None, + op=(xops.fmha.cutlass.FwOp, xops.fmha.cutlass.BwOp) + ) + time_f[config, "xformers.c"] = f + time_b[config, "xformers.c"] = b + + if xops is not None: + q, k, v = [torch.randn(batch_size, seqlen, nheads, headdim, device=device, dtype=dtype, + requires_grad=True) for _ in range(3)] + f, b = time_fwd_bwd( + xops.memory_efficient_attention, q, k, v, + attn_bias=xops.LowerTriangularMask() if causal else None, + op=(xops.fmha.flash.FwOp, xops.fmha.flash.BwOp) + ) + time_f[config, "xformers.f"] = f + time_b[config, "xformers.f"] = b + + print(f"### causal={causal}, headdim={headdim}, batch_size={batch_size}, seqlen={seqlen} ###") + for method in methods: + time_f_b[config, method] = time_f[config, method] + time_b[config, method] + speed_f[config, method] = efficiency( + flops(batch_size, seqlen, headdim, nheads, causal, mode="fwd"), + time_f[config, method] + ) + speed_b[config, method] = efficiency( + flops(batch_size, seqlen, headdim, nheads, causal, mode="bwd"), + time_b[config, method] + ) + speed_f_b[config, method] = efficiency( + flops(batch_size, seqlen, headdim, nheads, causal, mode="fwd_bwd"), + time_f_b[config, method] + ) + print( + f"{method} fwd: {speed_f[config, method]:.2f} TFLOPs/s, " + f"bwd: {speed_b[config, method]:.2f} TFLOPs/s, " + f"fwd + bwd: {speed_f_b[config, method]:.2f} TFLOPs/s" + ) + + +# with open('flash2_attn_time.plk', 'wb') as fp: +# pickle.dump((speed_f, speed_b, speed_f_b), fp, protocol=pickle.HIGHEST_PROTOCOL) diff --git a/flash-attention/csrc/cutlass/.github/ISSUE_TEMPLATE/bug_report.md b/flash-attention/csrc/cutlass/.github/ISSUE_TEMPLATE/bug_report.md new file mode 100644 index 0000000000000000000000000000000000000000..8165ec95fcc0feb4e308b4f5decce9a646d5ccaf --- /dev/null +++ b/flash-attention/csrc/cutlass/.github/ISSUE_TEMPLATE/bug_report.md @@ -0,0 +1,23 @@ +--- +name: Bug report +about: Create a bug report to help us improve CUTLASS +title: "[BUG]" +labels: "? - Needs Triage, bug" +assignees: '' + +--- + +**Describe the bug** +A clear and concise description of what the bug is. + +**Steps/Code to reproduce bug** +Follow this guide http://matthewrocklin.com/blog/work/2018/02/28/minimal-bug-reports to craft a minimal bug report. This helps us reproduce the issue you're having and resolve the issue more quickly. + +**Expected behavior** +A clear and concise description of what you expected to happen. + +**Environment details (please complete the following information):** + - Environment location: [Bare-metal, Docker, Cloud(specify cloud provider)] + +**Additional context** +Add any other context about the problem here. diff --git a/flash-attention/csrc/cutlass/.github/ISSUE_TEMPLATE/config.yml b/flash-attention/csrc/cutlass/.github/ISSUE_TEMPLATE/config.yml new file mode 100644 index 0000000000000000000000000000000000000000..4572ae1b9812347adad021123af92bf77b8f6b60 --- /dev/null +++ b/flash-attention/csrc/cutlass/.github/ISSUE_TEMPLATE/config.yml @@ -0,0 +1,5 @@ +blank_issues_enabled: true +contact_links: + - name: CUTLASS Discord + url: https://discord.gg/nvidiadeveloper + about: Come chat about using and contributing to CUTLASS! diff --git a/flash-attention/csrc/cutlass/.github/ISSUE_TEMPLATE/documentation_request.md b/flash-attention/csrc/cutlass/.github/ISSUE_TEMPLATE/documentation_request.md new file mode 100644 index 0000000000000000000000000000000000000000..c9fa21fac936434d6b526de631879e6acd811fc0 --- /dev/null +++ b/flash-attention/csrc/cutlass/.github/ISSUE_TEMPLATE/documentation_request.md @@ -0,0 +1,35 @@ +--- +name: Documentation request +about: Report incorrect or needed documentation to improve CUTLASS +title: "[DOC]" +labels: "? - Needs Triage, documentation" +assignees: '' + +--- + +## Report incorrect documentation + +**Location of incorrect documentation** +Provide links and line numbers if applicable. + +**Describe the problems or issues found in the documentation** +A clear and concise description of what you found to be incorrect. + +**Steps taken to verify documentation is incorrect** +List any steps you have taken: + +**Suggested fix for documentation** +Detail proposed changes to fix the documentation if you have any. + +--- + +## Report needed documentation + +**Report needed documentation** +A clear and concise description of what documentation you believe it is needed and why. + +**Describe the documentation you'd like** +A clear and concise description of what you want to happen. + +**Steps taken to search for needed documentation** +List any steps you have taken: diff --git a/flash-attention/csrc/cutlass/.github/ISSUE_TEMPLATE/feature_request.md b/flash-attention/csrc/cutlass/.github/ISSUE_TEMPLATE/feature_request.md new file mode 100644 index 0000000000000000000000000000000000000000..9ea8e6de070d7f3e9ae348670e311ff3014151b5 --- /dev/null +++ b/flash-attention/csrc/cutlass/.github/ISSUE_TEMPLATE/feature_request.md @@ -0,0 +1,20 @@ +--- +name: Feature request +about: Suggest an idea for CUTLASS +title: "[FEA]" +labels: "? - Needs Triage, feature request" +assignees: '' + +--- + +**Is your feature request related to a problem? Please describe.** +A clear and concise description of what the problem is. Ex. I wish I could use CUTLASS to do [...] + +**Describe the solution you'd like** +A clear and concise description of what you want to happen. + +**Describe alternatives you've considered** +A clear and concise description of any alternative solutions or features you've considered. + +**Additional context** +Add any other context, code examples, or references to existing implementations about the feature request here. diff --git a/flash-attention/csrc/cutlass/.github/ISSUE_TEMPLATE/submit_question.md b/flash-attention/csrc/cutlass/.github/ISSUE_TEMPLATE/submit_question.md new file mode 100644 index 0000000000000000000000000000000000000000..5aa2a672d2fcf1447665a4db010f8dd3fa21843a --- /dev/null +++ b/flash-attention/csrc/cutlass/.github/ISSUE_TEMPLATE/submit_question.md @@ -0,0 +1,10 @@ +--- +name: Submit question +about: Ask a general question about CUTLASS +title: "[QST]" +labels: "? - Needs Triage, question" +assignees: '' + +--- + +**What is your question?** diff --git a/flash-attention/csrc/cutlass/.github/workflows/labeler.yml b/flash-attention/csrc/cutlass/.github/workflows/labeler.yml new file mode 100644 index 0000000000000000000000000000000000000000..23956a02fbdce28965ed47010d0441a12bea2b79 --- /dev/null +++ b/flash-attention/csrc/cutlass/.github/workflows/labeler.yml @@ -0,0 +1,11 @@ +name: "Pull Request Labeler" +on: +- pull_request_target + +jobs: + triage: + runs-on: ubuntu-latest + steps: + - uses: actions/labeler@main + with: + repo-token: "${{ secrets.GITHUB_TOKEN }}" diff --git a/flash-attention/csrc/cutlass/.github/workflows/new-issues-to-triage-projects.yml b/flash-attention/csrc/cutlass/.github/workflows/new-issues-to-triage-projects.yml new file mode 100644 index 0000000000000000000000000000000000000000..a963cb2f89f268f2d3d7f8b661cbe0446debb2c8 --- /dev/null +++ b/flash-attention/csrc/cutlass/.github/workflows/new-issues-to-triage-projects.yml @@ -0,0 +1,35 @@ +name: Auto Assign New Issues to Triage Project + +on: + issues: + types: [opened] + +env: + GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} + +jobs: + assign_one_project: + runs-on: ubuntu-latest + name: Assign to New Issues to Triage Project + steps: + - name: Process bug issues + uses: docker://takanabe/github-actions-automate-projects:v0.0.1 + if: contains(github.event.issue.labels.*.name, 'bug') && contains(github.event.issue.labels.*.name, '? - Needs Triage') + env: + GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} + GITHUB_PROJECT_URL: https://github.com/NVIDIA/cutlass + GITHUB_PROJECT_COLUMN_NAME: 'Needs prioritizing' + - name: Process feature issues + uses: docker://takanabe/github-actions-automate-projects:v0.0.1 + if: contains(github.event.issue.labels.*.name, 'feature request') && contains(github.event.issue.labels.*.name, '? - Needs Triage') + env: + GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} + GITHUB_PROJECT_URL: https://github.com/NVIDIA/cutlass + GITHUB_PROJECT_COLUMN_NAME: 'Needs prioritizing' + - name: Process other issues + uses: docker://takanabe/github-actions-automate-projects:v0.0.1 + if: contains(github.event.issue.labels.*.name, '? - Needs Triage') && (!contains(github.event.issue.labels.*.name, 'bug') && !contains(github.event.issue.labels.*.name, 'feature request')) + env: + GITHUB_TOKEN: ${{ secrets.GITHUB_TOKEN }} + GITHUB_PROJECT_URL: https://github.com/NVIDIA/cutlass + GITHUB_PROJECT_COLUMN_NAME: 'Needs prioritizing' diff --git a/flash-attention/csrc/cutlass/.github/workflows/stale.yml b/flash-attention/csrc/cutlass/.github/workflows/stale.yml new file mode 100644 index 0000000000000000000000000000000000000000..8b65da69aa2f00bd0c8a67843e934a5f4141e593 --- /dev/null +++ b/flash-attention/csrc/cutlass/.github/workflows/stale.yml @@ -0,0 +1,57 @@ +name: Mark inactive issues and pull requests + +on: + schedule: + - cron: "0 * * * *" + +jobs: + mark-inactive-30d: + runs-on: ubuntu-latest + steps: + - name: Mark 30 day inactive issues and pull requests + uses: actions/stale@v3 + with: + repo-token: ${{ secrets.GITHUB_TOKEN }} + stale-issue-message: > + This issue has been labeled `inactive-30d` due to no recent activity in the past 30 days. + Please close this issue if no further response or action is needed. + Otherwise, please respond with a comment indicating any updates or changes to the original issue and/or confirm this issue still needs to be addressed. + This issue will be labeled `inactive-90d` if there is no activity in the next 60 days. + stale-issue-label: "inactive-30d" + exempt-issue-labels: "0 - Blocked,0 - Backlog,good first issue" + days-before-issue-stale: 30 + days-before-issue-close: -1 + stale-pr-message: > + This PR has been labeled `inactive-30d` due to no recent activity in the past 30 days. + Please close this PR if it is no longer required. + Otherwise, please respond with a comment indicating any updates. + This PR will be labeled `inactive-90d` if there is no activity in the next 60 days. + stale-pr-label: "inactive-30d" + exempt-pr-labels: "0 - Blocked,0 - Backlog,good first issue" + days-before-pr-stale: 30 + days-before-pr-close: -1 + operations-per-run: 50 + mark-inactive-90d: + runs-on: ubuntu-latest + steps: + - name: Mark 90 day inactive issues and pull requests + uses: actions/stale@v3 + with: + repo-token: ${{ secrets.GITHUB_TOKEN }} + stale-issue-message: > + This issue has been labeled `inactive-90d` due to no recent activity in the past 90 days. + Please close this issue if no further response or action is needed. + Otherwise, please respond with a comment indicating any updates or changes to the original issue and/or confirm this issue still needs to be addressed. + stale-issue-label: "inactive-90d" + exempt-issue-labels: "0 - Blocked,0 - Backlog,good first issue" + days-before-issue-stale: 90 + days-before-issue-close: -1 + stale-pr-message: > + This PR has been labeled `inactive-90d` due to no recent activity in the past 90 days. + Please close this PR if it is no longer required. + Otherwise, please respond with a comment indicating any updates. + stale-pr-label: "inactive-90d" + exempt-pr-labels: "0 - Blocked,0 - Backlog,good first issue" + days-before-pr-stale: 90 + days-before-pr-close: -1 + operations-per-run: 50 diff --git a/flash-attention/csrc/cutlass/cmake/CTestTestfile.configure.cmake b/flash-attention/csrc/cutlass/cmake/CTestTestfile.configure.cmake new file mode 100644 index 0000000000000000000000000000000000000000..94394a5000934c562907a5be803421bc76b47d60 --- /dev/null +++ b/flash-attention/csrc/cutlass/cmake/CTestTestfile.configure.cmake @@ -0,0 +1,54 @@ +# Copyright (c) 2017 - 2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: BSD-3-Clause +# +# Redistribution and use in source and binary forms, with or without +# modification, are permitted provided that the following conditions are met: +# +# 1. Redistributions of source code must retain the above copyright notice, this +# list of conditions and the following disclaimer. +# +# 2. Redistributions in binary form must reproduce the above copyright notice, +# this list of conditions and the following disclaimer in the documentation +# and/or other materials provided with the distribution. +# +# 3. Neither the name of the copyright holder nor the names of its +# contributors may be used to endorse or promote products derived from +# this software without specific prior written permission. +# +# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" +# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE +# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE +# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE +# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL +# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR +# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER +# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, +# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE +# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + +# Generated file + +set(TEST_SETS_SUPPORTED @TEST_SETS_SUPPORTED@) + +if (NOT DEFINED ENV{CUTLASS_TEST_SETS}) + set(ENV{CUTLASS_TEST_SETS} @CUTLASS_DEFAULT_ACTIVE_TEST_SETS@) +endif() + +foreach(TEST_SET_REQUESTED IN ITEMS $ENV{CUTLASS_TEST_SETS}) + if (NOT TEST_SET_REQUESTED IN_LIST TEST_SETS_SUPPORTED) + message(STATUS "Skipping tests for @TEST_EXE_PATH@ as ${TEST_SET_REQUESTED} is not in the set of [${TEST_SETS_SUPPORTED}].") + return() + endif() +endforeach() + +set(TEST_EXE_PATH @TEST_EXE_PATH@) +set(TEST_EXE_WORKING_DIRECTORY @TEST_EXE_WORKING_DIRECTORY@) +set(CUTLASS_USE_EXTENDED_ADD_TEST_FORMAT @TEST_USE_EXTENDED_FORMAT@) + +if (DEFINED ENV{CUTLASS_TEST_EXECUTION_ENVIRONMENT}) + set(_CUTLASS_TEST_EXECUTION_ENVIRONMENT $ENV{CUTLASS_TEST_EXECUTION_ENVIRONMENT}) +else() + set(_CUTLASS_TEST_EXECUTION_ENVIRONMENT @CUTLASS_TEST_EXECUTION_ENVIRONMENT@) +endif() + +@_INLINE_PER_TEST_CODE@ diff --git a/flash-attention/csrc/cutlass/cmake/CTestTestfile.test.configure.cmake b/flash-attention/csrc/cutlass/cmake/CTestTestfile.test.configure.cmake new file mode 100644 index 0000000000000000000000000000000000000000..fa2ceeb9bd23feb7bdbf71426490c091bcc5c632 --- /dev/null +++ b/flash-attention/csrc/cutlass/cmake/CTestTestfile.test.configure.cmake @@ -0,0 +1,43 @@ +# Copyright (c) 2017 - 2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: BSD-3-Clause +# +# Redistribution and use in source and binary forms, with or without +# modification, are permitted provided that the following conditions are met: +# +# 1. Redistributions of source code must retain the above copyright notice, this +# list of conditions and the following disclaimer. +# +# 2. Redistributions in binary form must reproduce the above copyright notice, +# this list of conditions and the following disclaimer in the documentation +# and/or other materials provided with the distribution. +# +# 3. Neither the name of the copyright holder nor the names of its +# contributors may be used to endorse or promote products derived from +# this software without specific prior written permission. +# +# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" +# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE +# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE +# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE +# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL +# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR +# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER +# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, +# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE +# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + +if (CUTLASS_USE_EXTENDED_ADD_TEST_FORMAT) + # The longform/extended format allows generator expressions to be + # expanded property and is useful in contexts where the files need + # to be immediately included into being-processed cmake code. + add_test(NAME @TEST_NAME@ COMMAND ${_CUTLASS_TEST_EXECUTION_ENVIRONMENT} "${TEST_EXE_PATH}" @TEST_COMMAND_OPTIONS@) +else() + add_test(@TEST_NAME@ ${_CUTLASS_TEST_EXECUTION_ENVIRONMENT} "${TEST_EXE_PATH}" @TEST_COMMAND_OPTIONS@) +endif() + +if (TEST_EXE_WORKING_DIRECTORY) + set_tests_properties(@TEST_NAME@ PROPERTIES WORKING_DIRECTORY "${TEST_EXE_WORKING_DIRECTORY}") +endif() + +set_tests_properties(@TEST_NAME@ PROPERTIES DISABLED @__DISABLE_TESTS@) + diff --git a/flash-attention/csrc/cutlass/cmake/NvidiaCutlassConfig.cmake.in b/flash-attention/csrc/cutlass/cmake/NvidiaCutlassConfig.cmake.in new file mode 100644 index 0000000000000000000000000000000000000000..2fe69119a1786f71ad0e0cf349ffa8478714ace6 --- /dev/null +++ b/flash-attention/csrc/cutlass/cmake/NvidiaCutlassConfig.cmake.in @@ -0,0 +1,9 @@ +get_filename_component(NvidiaCutlass_CMAKE_DIR "${CMAKE_CURRENT_LIST_FILE}" PATH) + +include(CMakeFindDependencyMacro) + +if(TARGET nvidia::cutlass::CUTLASS) + return() +endif() + +include("${NvidiaCutlass_CMAKE_DIR}/NvidiaCutlassTargets.cmake") diff --git a/flash-attention/csrc/cutlass/cmake/NvidiaCutlassPackageConfig.cmake b/flash-attention/csrc/cutlass/cmake/NvidiaCutlassPackageConfig.cmake new file mode 100644 index 0000000000000000000000000000000000000000..364fba7a2014e4f1cc50dee224cf82f452bf1bca --- /dev/null +++ b/flash-attention/csrc/cutlass/cmake/NvidiaCutlassPackageConfig.cmake @@ -0,0 +1,42 @@ +# Copyright (c) 2017 - 2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: BSD-3-Clause +# +# Redistribution and use in source and binary forms, with or without +# modification, are permitted provided that the following conditions are met: +# +# 1. Redistributions of source code must retain the above copyright notice, this +# list of conditions and the following disclaimer. +# +# 2. Redistributions in binary form must reproduce the above copyright notice, +# this list of conditions and the following disclaimer in the documentation +# and/or other materials provided with the distribution. +# +# 3. Neither the name of the copyright holder nor the names of its +# contributors may be used to endorse or promote products derived from +# this software without specific prior written permission. +# +# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" +# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE +# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE +# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE +# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL +# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR +# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER +# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, +# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE +# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + +set(CPACK_PACKAGE_NAME NvidiaCutlass) +set(CPACK_PACKAGE_VENDOR NVIDIA) +set(CPACK_PACKAGE_CONTACT info@nvidia.com) +set(CPACK_PACKAGE_DESCRIPTION_SUMMARY "CUTLASS CUDA C++ Template Linear Algebra Library") +set(CPACK_PACKAGE_INSTALL_DIRECTORY ${CPACK_PACKAGE_NAME}) +set(CPACK_PACKAGE_VERSION_MAJOR ${PROJECT_VERSION_MAJOR}) +set(CPACK_PACKAGE_VERSION_MINOR ${PROJECT_VERSION_MINOR}) +set(CPACK_PACKAGE_VERSION_PATCH ${PROJECT_VERSION_PATCH}) +set(CPACK_VERBATIM_VARIABLES YES) +# set(CPACK_PACKAGE_DESCRIPTION_FILE ${CMAKE_CURRENT_LIST_DIR}/Description.txt) +# set(CPACK_RESOURCE_FILE_WELCOME ${CMAKE_CURRENT_LIST_DIR}/Welcome.txt) +# set(CPACK_RESOURCE_FILE_LICENSE ${CMAKE_CURRENT_LIST_DIR}/License.txt) +# set(CPACK_RESOURCE_FILE_README ${CMAKE_CURRENT_LIST_DIR}/Readme.txt) +include(CPack) diff --git a/flash-attention/csrc/cutlass/cmake/googletest.cmake b/flash-attention/csrc/cutlass/cmake/googletest.cmake new file mode 100644 index 0000000000000000000000000000000000000000..0350fb2dd1064d670744be9d6f74df977cb93162 --- /dev/null +++ b/flash-attention/csrc/cutlass/cmake/googletest.cmake @@ -0,0 +1,51 @@ +# Copyright (c) 2017 - 2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved. +# SPDX-License-Identifier: BSD-3-Clause +# +# Redistribution and use in source and binary forms, with or without +# modification, are permitted provided that the following conditions are met: +# +# 1. Redistributions of source code must retain the above copyright notice, this +# list of conditions and the following disclaimer. +# +# 2. Redistributions in binary form must reproduce the above copyright notice, +# this list of conditions and the following disclaimer in the documentation +# and/or other materials provided with the distribution. +# +# 3. Neither the name of the copyright holder nor the names of its +# contributors may be used to endorse or promote products derived from +# this software without specific prior written permission. +# +# THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" +# AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE +# IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE +# DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE +# FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL +# DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR +# SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER +# CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, +# OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE +# OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + +include(FetchContent) + +set(GOOGLETEST_DIR "" CACHE STRING "Location of local GoogleTest repo to build against") + +if(GOOGLETEST_DIR) + set(FETCHCONTENT_SOURCE_DIR_GOOGLETEST ${GOOGLETEST_DIR} CACHE STRING "GoogleTest source directory override") +endif() + +FetchContent_Declare( + googletest + GIT_REPOSITORY https://github.com/google/googletest.git + GIT_TAG v1.14.0 + ) + +FetchContent_GetProperties(googletest) + +if(NOT googletest_POPULATED) + FetchContent_Populate(googletest) + if (MSVC) + set(gtest_force_shared_crt ON CACHE BOOL "" FORCE) + endif() + add_subdirectory(${googletest_SOURCE_DIR} ${googletest_BINARY_DIR} EXCLUDE_FROM_ALL) +endif() diff --git a/flash-attention/csrc/cutlass/cmake/nop.cu b/flash-attention/csrc/cutlass/cmake/nop.cu new file mode 100644 index 0000000000000000000000000000000000000000..be2b15881ec8401e50c57dc10420e18deda752f2 --- /dev/null +++ b/flash-attention/csrc/cutlass/cmake/nop.cu @@ -0,0 +1,49 @@ +/*************************************************************************************************** + * Copyright (c) 2017 - 2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-License-Identifier: BSD-3-Clause + * + * Redistribution and use in source and binary forms, with or without + * modification, are permitted provided that the following conditions are met: + * + * 1. Redistributions of source code must retain the above copyright notice, this + * list of conditions and the following disclaimer. + * + * 2. Redistributions in binary form must reproduce the above copyright notice, + * this list of conditions and the following disclaimer in the documentation + * and/or other materials provided with the distribution. + * + * 3. Neither the name of the copyright holder nor the names of its + * contributors may be used to endorse or promote products derived from + * this software without specific prior written permission. + * + * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" + * AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE + * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE + * DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE + * FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL + * DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR + * SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER + * CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, + * OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE + * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + * + **************************************************************************************************/ +/*! \file + \brief Basic CUDA file for testing compiler flags. +*/ + +__device__ int inner() +{ + return -1; +} + +__global__ void test() +{ + inner(); +} + +int main() +{ + test<<<1,1>>>(); + return 0; +} diff --git a/flash-attention/csrc/cutlass/cmake/version_extended.h.in b/flash-attention/csrc/cutlass/cmake/version_extended.h.in new file mode 100644 index 0000000000000000000000000000000000000000..361306302275507420590211b43b39ad1eae7df9 --- /dev/null +++ b/flash-attention/csrc/cutlass/cmake/version_extended.h.in @@ -0,0 +1,34 @@ +/*************************************************************************************************** + * Copyright (c) 2017 - 2024 NVIDIA CORPORATION & AFFILIATES. All rights reserved. + * SPDX-License-Identifier: BSD-3-Clause + * + * Redistribution and use in source and binary forms, with or without + * modification, are permitted provided that the following conditions are met: + * + * 1. Redistributions of source code must retain the above copyright notice, this + * list of conditions and the following disclaimer. + * + * 2. Redistributions in binary form must reproduce the above copyright notice, + * this list of conditions and the following disclaimer in the documentation + * and/or other materials provided with the distribution. + * + * 3. Neither the name of the copyright holder nor the names of its + * contributors may be used to endorse or promote products derived from + * this software without specific prior written permission. + * + * THIS SOFTWARE IS PROVIDED BY THE COPYRIGHT HOLDERS AND CONTRIBUTORS "AS IS" + * AND ANY EXPRESS OR IMPLIED WARRANTIES, INCLUDING, BUT NOT LIMITED TO, THE + * IMPLIED WARRANTIES OF MERCHANTABILITY AND FITNESS FOR A PARTICULAR PURPOSE ARE + * DISCLAIMED. IN NO EVENT SHALL THE COPYRIGHT HOLDER OR CONTRIBUTORS BE LIABLE + * FOR ANY DIRECT, INDIRECT, INCIDENTAL, SPECIAL, EXEMPLARY, OR CONSEQUENTIAL + * DAMAGES (INCLUDING, BUT NOT LIMITED TO, PROCUREMENT OF SUBSTITUTE GOODS OR + * SERVICES; LOSS OF USE, DATA, OR PROFITS; OR BUSINESS INTERRUPTION) HOWEVER + * CAUSED AND ON ANY THEORY OF LIABILITY, WHETHER IN CONTRACT, STRICT LIABILITY, + * OR TORT (INCLUDING NEGLIGENCE OR OTHERWISE) ARISING IN ANY WAY OUT OF THE USE + * OF THIS SOFTWARE, EVEN IF ADVISED OF THE POSSIBILITY OF SUCH DAMAGE. + * + **************************************************************************************************/ +#pragma once + +#define CUTLASS_BUILD @CUTLASS_VERSION_BUILD@ +#define CUTLASS_REVISION "@CUTLASS_REVISION@" diff --git a/flash-attention/csrc/cutlass/docs/bc_s.png b/flash-attention/csrc/cutlass/docs/bc_s.png new file mode 100644 index 0000000000000000000000000000000000000000..06f0fde481acf6c31ea15522ab38ca2f98b954ee --- /dev/null +++ b/flash-attention/csrc/cutlass/docs/bc_s.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:9c199b2ee7302c1fc229bb7a125b0db309631088398444af6126423827d0c469 +size 675 diff --git a/flash-attention/csrc/cutlass/docs/bdwn.png b/flash-attention/csrc/cutlass/docs/bdwn.png new file mode 100644 index 0000000000000000000000000000000000000000..81c12e8fbf85dcfa7f6178b3bb3817b47b521e08 --- /dev/null +++ b/flash-attention/csrc/cutlass/docs/bdwn.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:ef157cd29b9e851cb6ec382ea9e5b4771d2c8fa08e0d1ab440d1c6c90d4dac79 +size 147 diff --git a/flash-attention/csrc/cutlass/docs/closed.png b/flash-attention/csrc/cutlass/docs/closed.png new file mode 100644 index 0000000000000000000000000000000000000000..56d6f1601a7746b5c61f30a32074eebb9af7e26d --- /dev/null +++ b/flash-attention/csrc/cutlass/docs/closed.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:2ab6c4fbebdcb6ca91b040113ebe58f11694e060fe0e0d5c967437c5d572e3fa +size 133 diff --git a/flash-attention/csrc/cutlass/docs/cutlass-logo-small.png b/flash-attention/csrc/cutlass/docs/cutlass-logo-small.png new file mode 100644 index 0000000000000000000000000000000000000000..d00ecf1d733d7b3a9b1c80fd73575ecc84bfa16f --- /dev/null +++ b/flash-attention/csrc/cutlass/docs/cutlass-logo-small.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:32352d8624ce532501742ed8039720abeffa7c3f301c10df2bb9c0ca79883d36 +size 1488 diff --git a/flash-attention/csrc/cutlass/docs/doc.png b/flash-attention/csrc/cutlass/docs/doc.png new file mode 100644 index 0000000000000000000000000000000000000000..d5bb142b4f6ab5c031b101888032ce650a7de28e --- /dev/null +++ b/flash-attention/csrc/cutlass/docs/doc.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:facc40f62e4e4714a6d3a87e39ffd68f7cd099687e7c7330ffc1f09de374e565 +size 763 diff --git a/flash-attention/csrc/cutlass/docs/doxygen.png b/flash-attention/csrc/cutlass/docs/doxygen.png new file mode 100644 index 0000000000000000000000000000000000000000..cc45688c549c8f561d7f7ad1be658cc90c576386 --- /dev/null +++ b/flash-attention/csrc/cutlass/docs/doxygen.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:95eebc761f7b9502731f7ca6d0d0baff2db43504ad963d766a4fc4226f75b58c +size 3872 diff --git a/flash-attention/csrc/cutlass/docs/folderclosed.png b/flash-attention/csrc/cutlass/docs/folderclosed.png new file mode 100644 index 0000000000000000000000000000000000000000..9c2db604333b7e2d53920c0fac9c59317fc7ca1e --- /dev/null +++ b/flash-attention/csrc/cutlass/docs/folderclosed.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:a94a39e419e6c7f47921c55707940b12f7b0b65069913bd3377228cdfae99459 +size 637 diff --git a/flash-attention/csrc/cutlass/docs/folderopen.png b/flash-attention/csrc/cutlass/docs/folderopen.png new file mode 100644 index 0000000000000000000000000000000000000000..a48561419107f5452eb24f1a3421d2cd4825896f --- /dev/null +++ b/flash-attention/csrc/cutlass/docs/folderopen.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:3e01065709a6e2d7d68ba2c4a12c31a36951516eea4826379e7643a810eba0fc +size 629 diff --git a/flash-attention/csrc/cutlass/docs/nav_g.png b/flash-attention/csrc/cutlass/docs/nav_g.png new file mode 100644 index 0000000000000000000000000000000000000000..34942a13481666ad7cee7458ecdea1cb279d13af --- /dev/null +++ b/flash-attention/csrc/cutlass/docs/nav_g.png @@ -0,0 +1,3 @@ +version https://git-lfs.github.com/spec/v1 +oid sha256:3171164c16baa5953c17821a7ce5feac673c05a56b287453ce3a0bf5048f0999 +size 95 diff --git a/flash-attention/csrc/cutlass/docs/search/all_0.html b/flash-attention/csrc/cutlass/docs/search/all_0.html new file mode 100644 index 0000000000000000000000000000000000000000..d54e0bd8eab93c9e5717887cb1cb7cf402c9150f --- /dev/null +++ b/flash-attention/csrc/cutlass/docs/search/all_0.html @@ -0,0 +1,26 @@ + + + + + + + + + +
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