.. DO NOT EDIT. .. THIS FILE WAS AUTOMATICALLY GENERATED BY SPHINX-GALLERY. .. TO MAKE CHANGES, EDIT THE SOURCE PYTHON FILE: .. "getting-started/tutorials/02-fused-softmax.py" .. LINE NUMBERS ARE GIVEN BELOW. .. only:: html .. note:: :class: sphx-glr-download-link-note Click :ref:`here ` to download the full example code .. rst-class:: sphx-glr-example-title .. _sphx_glr_getting-started_tutorials_02-fused-softmax.py: Fused Softmax ================= In this tutorial, you will write a fused softmax operation that is significantly faster than PyTorch's native op for a particular class of matrices: those whose rows can fit in the GPU's SRAM. You will learn about: - The benefits of kernel fusion for bandwidth-bound operations. - Reduction operators in Triton. .. GENERATED FROM PYTHON SOURCE LINES 14-18 Motivations ------------ Custom GPU kernels for elementwise additions are educationally valuable but won't get you very far in practice. Let us consider instead the case of a simple (numerically stabilized) softmax operation: .. GENERATED FROM PYTHON SOURCE LINES 18-46 .. code-block:: default import torch import triton import triton.language as tl @torch.jit.script def naive_softmax(x): """Compute row-wise softmax of X using native pytorch We subtract the maximum element in order to avoid overflows. Softmax is invariant to this shift. """ # read MN elements ; write M elements x_max = x.max(dim=1)[0] # read MN + M elements ; write MN elements z = x - x_max[:, None] # read MN elements ; write MN elements numerator = torch.exp(z) # read MN elements ; write M elements denominator = numerator.sum(dim=1) # read MN + M elements ; write MN elements ret = numerator / denominator[:, None] # in total: read 5MN + 2M elements ; wrote 3MN + 2M elements return ret .. GENERATED FROM PYTHON SOURCE LINES 47-55 When implemented naively in PyTorch, computing :code:`y = naive_softmax(x)` for :math:`x \in R^{M \times N}` requires reading :math:`5MN + 2M` elements from DRAM and writing back :math:`3MN + 2M` elements. This is obviously wasteful; we'd prefer to have a custom "fused" kernel that only reads X once and does all the necessary computations on-chip. Doing so would require reading and writing back only :math:`MN` bytes, so we could expect a theoretical speed-up of ~4x (i.e., :math:`(8MN + 4M) / 2MN`). The `torch.jit.script` flags aims to perform this kind of "kernel fusion" automatically but, as we will see later, it is still far from ideal. .. GENERATED FROM PYTHON SOURCE LINES 57-64 Compute Kernel ---------------- Our softmax kernel works as follows: each program loads a row of the input matrix X, normalizes it and writes back the result to the output Y. Note that one important limitation of Triton is that each block must have a power-of-two number of elements, so we need to internally "pad" each row and guard the memory operations properly if we want to handle any possible input shapes: .. GENERATED FROM PYTHON SOURCE LINES 64-93 .. code-block:: default @triton.jit def softmax_kernel( output_ptr, input_ptr, input_row_stride, output_row_stride, n_cols, BLOCK_SIZE: tl.constexpr ): # The rows of the softmax are independent, so we parallelize across those row_idx = tl.program_id(0) # The stride represents how much we need to increase the pointer to advance 1 row row_start_ptr = input_ptr + row_idx * input_row_stride # The block size is the next power of two greater than n_cols, so we can fit each # row in a single block col_offsets = tl.arange(0, BLOCK_SIZE) input_ptrs = row_start_ptr + col_offsets # Load the row into SRAM, using a mask since BLOCK_SIZE may be > than n_cols row = tl.load(input_ptrs, mask=col_offsets < n_cols, other=-float('inf')) # Substract maximum for numerical stability row_minus_max = row - tl.max(row, axis=0) # Note that exponentials in Triton are fast but approximate (i.e., think __expf in CUDA) numerator = tl.exp(row_minus_max) denominator = tl.sum(numerator, axis=0) softmax_output = numerator / denominator # Write back output to DRAM output_row_start_ptr = output_ptr + row_idx * output_row_stride output_ptrs = output_row_start_ptr + col_offsets tl.store(output_ptrs, softmax_output, mask=col_offsets < n_cols) .. GENERATED FROM PYTHON SOURCE LINES 94-95 We can create a helper function that enqueues the kernel and its (meta-)arguments for any given input tensor. .. GENERATED FROM PYTHON SOURCE LINES 95-125 .. code-block:: default def softmax(x): n_rows, n_cols = x.shape # The block size is the smallest power of two greater than the number of columns in `x` BLOCK_SIZE = triton.next_power_of_2(n_cols) # Another trick we can use is to ask the compiler to use more threads per row by # increasing the number of warps (`num_warps`) over which each row is distributed. # You will see in the next tutorial how to auto-tune this value in a more natural # way so you don't have to come up with manual heuristics yourself. num_warps = 4 if BLOCK_SIZE >= 2048: num_warps = 8 if BLOCK_SIZE >= 4096: num_warps = 16 # Allocate output y = torch.empty_like(x) # Enqueue kernel. The 1D launch grid is simple: we have one kernel instance per row o # f the input matrix softmax_kernel[(n_rows,)]( y, x, x.stride(0), y.stride(0), n_cols, num_warps=num_warps, BLOCK_SIZE=BLOCK_SIZE, ) return y .. GENERATED FROM PYTHON SOURCE LINES 126-128 Unit Test ---------- .. GENERATED FROM PYTHON SOURCE LINES 130-132 We make sure that we test our kernel on a matrix with an irregular number of rows and columns. This will allow us to verify that our padding mechanism works. .. GENERATED FROM PYTHON SOURCE LINES 132-139 .. code-block:: default torch.manual_seed(0) x = torch.randn(1823, 781, device='cuda') y_triton = softmax(x) y_torch = torch.softmax(x, axis=1) assert torch.allclose(y_triton, y_torch), (y_triton, y_torch) .. GENERATED FROM PYTHON SOURCE LINES 140-141 As expected, the results are identical. .. GENERATED FROM PYTHON SOURCE LINES 143-147 Benchmark ------------- Here we will benchmark our operation as a function of the number of columns in the input matrix -- assuming 4096 rows. We will then compare its performance against (1) :code:`torch.softmax` and (2) the :code:`naive_softmax` defined above. .. GENERATED FROM PYTHON SOURCE LINES 147-186 .. code-block:: default @triton.testing.perf_report( triton.testing.Benchmark( x_names=['N'], # argument names to use as an x-axis for the plot x_vals=[ 128 * i for i in range(2, 100) ], # different possible values for `x_name` line_arg='provider', # argument name whose value corresponds to a different line in the plot line_vals=[ 'triton', 'torch-native', 'torch-jit', ], # possible values for `line_arg`` line_names=[ "Triton", "Torch (native)", "Torch (jit)", ], # label name for the lines styles=[('blue', '-'), ('green', '-'), ('green', '--')], # line styles ylabel="GB/s", # label name for the y-axis plot_name="softmax-performance", # name for the plot. Used also as a file name for saving the plot. args={'M': 4096}, # values for function arguments not in `x_names` and `y_name` ) ) def benchmark(M, N, provider): x = torch.randn(M, N, device='cuda', dtype=torch.float32) if provider == 'torch-native': ms, min_ms, max_ms = triton.testing.do_bench(lambda: torch.softmax(x, axis=-1)) if provider == 'triton': ms, min_ms, max_ms = triton.testing.do_bench(lambda: softmax(x)) if provider == 'torch-jit': ms, min_ms, max_ms = triton.testing.do_bench(lambda: naive_softmax(x)) gbps = lambda ms: 2 * x.nelement() * x.element_size() * 1e-9 / (ms * 1e-3) return gbps(ms), gbps(max_ms), gbps(min_ms) benchmark.run(show_plots=True, print_data=True) .. image:: /getting-started/tutorials/images/sphx_glr_02-fused-softmax_001.png :alt: 02 fused softmax :class: sphx-glr-single-img .. rst-class:: sphx-glr-script-out Out: .. code-block:: none softmax-performance: N Triton Torch (native) Torch (jit) 0 256.0 546.133347 546.133347 190.511628 1 384.0 614.400016 585.142862 153.600004 2 512.0 655.360017 606.814814 154.566038 3 640.0 706.206879 640.000002 160.000000 4 768.0 722.823517 664.216187 162.754967 .. ... ... ... ... 93 12160.0 812.359066 406.603966 198.834951 94 12288.0 812.429770 415.661740 199.096718 95 12416.0 812.498981 412.149375 198.755369 96 12544.0 810.925276 412.971190 199.012395 97 12672.0 811.007961 412.097543 199.167004 [98 rows x 4 columns] .. GENERATED FROM PYTHON SOURCE LINES 187-192 In the above plot, we can see that: - Triton is 4x faster than the Torch JIT. This confirms our suspicions that the Torch JIT does not do any fusion here. - Triton is noticeably faster than :code:`torch.softmax` -- in addition to being **easier to read, understand and maintain**. Note however that the PyTorch `softmax` operation is more general and will works on tensors of any shape. .. rst-class:: sphx-glr-timing **Total running time of the script:** ( 3 minutes 31.735 seconds) .. _sphx_glr_download_getting-started_tutorials_02-fused-softmax.py: .. only :: html .. container:: sphx-glr-footer :class: sphx-glr-footer-example .. container:: sphx-glr-download sphx-glr-download-python :download:`Download Python source code: 02-fused-softmax.py <02-fused-softmax.py>` .. container:: sphx-glr-download sphx-glr-download-jupyter :download:`Download Jupyter notebook: 02-fused-softmax.ipynb <02-fused-softmax.ipynb>` .. only:: html .. rst-class:: sphx-glr-signature `Gallery generated by Sphinx-Gallery `_