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README.md

@@ -35,3 +35,6 @@ Kernel that implements RMS Norm over a row of tensor.
 
 ## `test_layernorm.py`
 Kernel that implements Layer Normalization over a row on tensor
+
+## `test_dotproduct.py`
+Kernel that implements the dot product of two vectors

kernels/test_dot_product.py

@@ -0,0 +1,136 @@
+"""
+Copy pasted directly from the Triton docs
+
+Vector Addition
+===============
+
+In this tutorial, you will write a simple vector addition using Triton.
+
+In doing so, you will learn about:
+
+* The basic programming model of Triton.
+
+* The `triton.jit` decorator, which is used to define Triton kernels.
+
+* The best practices for validating and benchmarking your custom ops against native reference implementations.
+
+"""
+
+# %%
+# Compute Kernel
+# --------------
+
+import torch
+
+import triton
+import triton.language as tl
+
+
+@triton.jit
+def add_kernel(x_ptr,  # *Pointer* to first input vector.
+               y_ptr,  # *Pointer* to second input vector.
+               output_ptr,  # *Pointer* to output vector.
+               n_elements,  # Size of the vector.
+               BLOCK_SIZE: tl.constexpr,  # Number of elements each program should process.
+               # NOTE: `constexpr` so it can be used as a shape value.
+               ):
+    # There are multiple 'programs' processing different data. We identify which program
+    # we are here:
+    pid = tl.program_id(axis=0)  # We use a 1D launch grid so axis is 0.
+    # This program will process inputs that are offset from the initial data.
+    # For instance, if you had a vector of length 256 and block_size of 64, the programs
+    # would each access the elements [0:64, 64:128, 128:192, 192:256].
+    # Note that offsets is a list of pointers:
+    block_start = pid * BLOCK_SIZE
+    offsets = block_start + tl.arange(0, BLOCK_SIZE)
+    # Create a mask to guard memory operations against out-of-bounds accesses.
+    mask = offsets < n_elements
+    # Load x and y from DRAM, masking out any extra elements in case the input is not a
+    # multiple of the block size.
+    x = tl.load(x_ptr + offsets, mask=mask)
+    y = tl.load(y_ptr + offsets, mask=mask)
+    output = x + y
+    # Write x + y back to DRAM.
+    tl.store(output_ptr + offsets, output, mask=mask)
+
+
+# %%
+# Let's also declare a helper function to (1) allocate the `z` tensor
+# and (2) enqueue the above kernel with appropriate grid/block sizes:
+
+
+def add(x: torch.Tensor, y: torch.Tensor):
+    # We need to preallocate the output.
+    output = torch.empty_like(x)
+    assert x.is_cuda and y.is_cuda and output.is_cuda
+    n_elements = output.numel()
+    # The SPMD launch grid denotes the number of kernel instances that run in parallel.
+    # It is analogous to CUDA launch grids. It can be either Tuple[int], or Callable(metaparameters) -> Tuple[int].
+    # In this case, we use a 1D grid where the size is the number of blocks:
+    grid = lambda meta: (triton.cdiv(n_elements, meta['BLOCK_SIZE']), )
+    # NOTE:
+    #  - Each torch.tensor object is implicitly converted into a pointer to its first element.
+    #  - `triton.jit`'ed functions can be indexed with a launch grid to obtain a callable GPU kernel.
+    #  - Don't forget to pass meta-parameters as keywords arguments.
+    add_kernel[grid](x, y, output, n_elements, BLOCK_SIZE=1024)
+    # We return a handle to z but, since `torch.cuda.synchronize()` hasn't been called, the kernel is still
+    # running asynchronously at this point.
+    return output
+
+
+# %%
+# We can now use the above function to compute the element-wise sum of two `torch.tensor` objects and test its correctness:
+
+torch.manual_seed(0)
+size = 98432
+x = torch.rand(size, device='cuda')
+y = torch.rand(size, device='cuda')
+output_torch = x + y
+output_triton = add(x, y)
+print(output_torch)
+print(output_triton)
+print(f'The maximum difference between torch and triton is '
+      f'{torch.max(torch.abs(output_torch - output_triton))}')
+
+# %%
+# Seems like we're good to go!
+
+# %%
+# Benchmark
+# ---------
+#
+# We can now benchmark our custom op on vectors of increasing sizes to get a sense of how it does relative to PyTorch.
+# To make things easier, Triton has a set of built-in utilities that allow us to concisely plot the performance of our custom ops.
+# for different problem sizes.
+
+
+@triton.testing.perf_report(
+    triton.testing.Benchmark(
+        x_names=['size'],  # Argument names to use as an x-axis for the plot.
+        x_vals=[2**i for i in range(12, 28, 1)],  # Different possible values for `x_name`.
+        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', 'torch'],  # Possible values for `line_arg`.
+        line_names=['Triton', 'Torch'],  # Label name for the lines.
+        styles=[('blue', '-'), ('green', '-')],  # Line styles.
+        ylabel='GB/s',  # Label name for the y-axis.
+        plot_name='vector-add-performance',  # Name for the plot. Used also as a file name for saving the plot.
+        args={},  # Values for function arguments not in `x_names` and `y_name`.
+    ))
+def benchmark(size, provider):
+    x = torch.rand(size, device='cuda', dtype=torch.float32)
+    y = torch.rand(size, device='cuda', dtype=torch.float32)
+    quantiles = [0.5, 0.2, 0.8]
+    if provider == 'torch':
+        ms, min_ms, max_ms = triton.testing.do_bench(lambda: x + y, quantiles=quantiles)
+    if provider == 'triton':
+        ms, min_ms, max_ms = triton.testing.do_bench(lambda: add(x, y), quantiles=quantiles)
+    gbps = lambda ms: 3 * x.numel() * x.element_size() * 1e-9 / (ms * 1e-3)
+    return gbps(ms), gbps(max_ms), gbps(min_ms)
+
+
+# %%
+# We can now run the decorated function above. Pass `print_data=True` to see the performance number, `show_plots=True` to plot them, and/or
+# `save_path='/path/to/results/' to save them to disk along with raw CSV data:
+def test_benchmark():
+  benchmark.run(save_path="./perf-artifacts/dot_product", show_plots=True, print_data=True)