Deprecation of Triton-C and Replacement by decorated Python functions (#86)
This PR implements a major overhaul of the frontend for Triton, and replaces Triton-C by a pure Python API in which kernels are defined as @triton.jit decorated functions. The documentation and tutorials have also been updated to accommodate these changes. See documentations for more information on the new API
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Philippe Tillet
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Philippe Tillet
Philippe Tillet
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@@ -1,139 +1,71 @@
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import torch
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import triton
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"""
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Vector Addition
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=================
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In this tutorial, you will write a simple vector addition using Triton and learn about:
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- The basic syntax of the Triton programming language
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- The best practices for creating PyTorch custom operators using the :code:`triton.kernel` Python API
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- The basic programming model used by Triton
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- The `triton.jit` decorator, which constitutes the main entry point for writing Triton kernels.
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- The best practices for validating and benchmarking custom ops against native reference implementations
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"""
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# %%
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# Compute Kernel
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# --------------------------
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#
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# Each compute kernel is declared using the :code:`__global__` attribute, and executed many times in parallel
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# on different chunks of data (See the `Single Program, Multiple Data <(https://en.wikipedia.org/wiki/SPMD>`_)
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# programming model for more details).
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#
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# .. code-block:: C
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#
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# __global__ void add(float* z, float* x, float* y, int N){
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# // The `get_program_id(i)` returns the i-th coordinate
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# // of the program in the overaching SPMD context
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# // (a.k.a launch grid). This is what allows us to process
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# // different chunks of data in parallel.
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# // For those similar with CUDA, `get_program_id({0,1,2})`
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# // is similar to blockIdx.{x,y,z}
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# int pid = get_program_id(0);
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# // In Triton, arrays are first-class citizen. In other words,
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# // they are primitives data-types and are -- contrary to C and
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# // CUDA -- not implemented as pointers to contiguous chunks of
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# // memory.
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# // In the few lines below, we create an array of `BLOCK` pointers
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# // whose memory values are, e.g.:
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# // [z + pid*BLOCK + 0, z + pid*BLOCK + 1, ..., z + pid*BLOCK + BLOCK - 1]
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# // Note: here BLOCK is expected to be a pre-processor macro defined at compile-time
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# int offset[BLOCK] = pid * BLOCK + 0 ... BLOCK;
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# float* pz [BLOCK] = z + offset;
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# float* px [BLOCK] = x + offset;
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# float* py [BLOCK] = y + offset;
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# // Simple element-wise control-flow for load/store operations can
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# // be achieved using the the ternary operator `cond ? val_true : val_false`
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# // or the conditional dereferencing operator `*?(cond)ptr
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# // Here, we make sure that we do not access memory out-of-bounds when we
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# // write-back `z`
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# bool check[BLOCK] = offset < N;
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# *?(check)pz = *?(check)px + *?(check)py;
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# }
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#
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# The existence of arrays as a primitive data-type for Triton comes with a number of advantages that are highlighted in the `MAPL'2019 Triton paper <http://www.eecs.harvard.edu/~htk/publication/2019-mapl-tillet-kung-cox.pdf>`_.
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@triton.jit
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def _add(
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X, # *Pointer* to first input vector
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Y, # *Pointer* to second input vector
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Z, # *Pointer* to output vector
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N, # Size of the vector
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**meta # Optional meta-parameters for the kernel
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):
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pid = triton.program_id(0)
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# Create an offset for the blocks of pointers to be
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# processed by this program instance
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offsets = pid * meta['BLOCK'] + triton.arange(0, meta['BLOCK'])
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# Create a mask to guard memory operations against
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# out-of-bounds accesses
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mask = offsets < N
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# Load x
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x = triton.load(X + offsets, mask=mask)
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y = triton.load(Y + offsets, mask=mask)
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# Write back x + y
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z = x + y
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triton.store(Z + offsets, z)
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# %%
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# Torch Bindings
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# --------------------------
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# The only thing that matters when it comes to Triton and Torch is the :code:`triton.kernel` class. This allows you to transform the above C-like function into a callable python object that can be used to modify :code:`torch.tensor` objects. To create a :code:`triton.kernel`, you only need three things:
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#
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# - :code:`source: string`: the source-code of the kernel you want to create
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# - :code:`device: torch.device`: the device you want to compile this code for
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# - :code:`defines: dict`: the set of macros that you want the pre-processor to `#define` for you
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import torch
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import triton
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# source-code for Triton compute kernel
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# here we just copy-paste the above code without the extensive comments.
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# you may prefer to store it in a .c file and load it from there instead.
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_src = """
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__global__ void add(float* z, float* x, float* y, int N){
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// program id
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int pid = get_program_id(0);
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// create arrays of pointers
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int offset[BLOCK] = pid * BLOCK + 0 ... BLOCK;
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float* pz[BLOCK] = z + offset;
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float* px[BLOCK] = x + offset;
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float* py[BLOCK] = y + offset;
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// bounds checking
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bool check[BLOCK] = offset < N;
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// write-back
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*?(check)pz = *?(check)px + *?(check)py;
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}
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"""
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# We can also declara a helper function that handles allocating the output vector
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# and enqueueing the kernel.
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# This function returns a callable `triton.kernel` object created from the above source code.
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# For portability, we maintain a cache of kernels for different `torch.device`
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# We compile the kernel with -DBLOCK=1024
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def make_add_kernel(device):
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cache = make_add_kernel.cache
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if device not in cache:
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defines = {'BLOCK': 1024}
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cache[device] = triton.kernel(_src, device=device, defines=defines)
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return cache[device]
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def add(x, y):
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z = torch.empty_like(x)
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N = z.shape[0]
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# The SPMD launch grid denotes the number of kernel instances that should execute in parallel.
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# It is analogous to CUDA launch grids. It can be either Tuple[int], or Callable(metaparameters) -> Tuple[int]
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grid = lambda meta: (triton.cdiv(N, meta['BLOCK']), )
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# NOTE:
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# - torch.tensor objects are implicitly converted to pointers to their first element.
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# - `triton.jit`'ed functions can be subscripted with a launch grid to obtain a callable GPU kernel
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# - don't forget to pass meta-parameters as keywords arguments
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_add[grid](x, y, z, N, BLOCK=1024)
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# We return a handle to z but, since `torch.cuda.synchronize()` hasn't been called, the kernel is still
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# running asynchronously.
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return z
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make_add_kernel.cache = dict()
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# This is a standard torch custom autograd Function;
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# The only difference is that we can now use the above kernel in the `forward` and `backward` functions.`
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class _add(torch.autograd.Function):
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@staticmethod
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def forward(ctx, x, y):
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# constraints of the op
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assert x.dtype == torch.float32
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# *allocate output*
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z = torch.empty_like(x)
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# *create launch grid*:
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# this is a function which takes compilation parameters `opt`
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# as input and returns a tuple of int (i.e., launch grid) for the kernel.
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# triton.cdiv is a shortcut for ceil division:
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# triton.cdiv(a, b) = (a + b - 1) // b
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N = z.shape[0]
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grid = lambda opt: (triton.cdiv(N, opt.BLOCK), )
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# *launch kernel*:
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# pointer to the data of torch tensors can be retrieved with
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# the `.data_ptr()` method
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kernel = make_add_kernel(z.device)
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kernel(z.data_ptr(), x.data_ptr(), y.data_ptr(), N, grid=grid)
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return z
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# Just like we standard PyTorch ops We use the :code:`.apply` method to create a callable object for our function
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add = _add.apply
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# %%
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# We can now use the above function to compute the sum of two `torch.tensor` objects:
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# %%
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# Unit Test
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# -----------
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#
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# Of course, the first thing that we should check is that whether kernel is correct. This is pretty easy to test, as shown below:
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# We can now use the above function to compute the sum of two `torch.tensor` objects and test our results:
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torch.manual_seed(0)
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x = torch.rand(98432, device='cuda')
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y = torch.rand(98432, device='cuda')
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size = 98432
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x = torch.rand(size, device='cuda')
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y = torch.rand(size, device='cuda')
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za = x + y
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zb = add(x, y)
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print(za)
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