Studying Triton One Kernel at a Time: Matrix Multiplication

Disclaimer: all the next figures and animations had been made by the writer until said in any other case.

Naive GEMM

Let’s begin easy: we wish to multiply two matrices X and Y with shapes (M,N) and (N,Okay) respectively. The output matrix Z=X@Y will subsequently have form (M,Okay).

This operation includes computing the dot merchandise of all pairs of rows and columns in X and Y respectively. An easy NumPy implementation would possibly look one thing like this:

Whereas straightforward to jot down, learn and perceive, this implementation is very inefficient by way of reminiscence entry and caching. As talked about within the first article of this collection, a elementary facet of GPU optimisation is minimising knowledge transfers. 

Nonetheless, our present implementation begins by loading a row from X, iteratively hundreds all Okay columns of Y, computes their dot product and repeats the method for each row in X. This leads to a complete of M(Okay+1) loading operations. 

As seen within the animation, the reminiscence entry sample is wasteful, as each column of Y is loaded M instances. As an analogy: that is like working to the grocery retailer (world reminiscence) each time you want a brand new ingredient for a dish as an alternative of getting ready all of the components in your kitchen counter (shared reminiscence). Ideally, we wish to minimise the variety of instances every chunk of information is loaded and maximise its reusability as soon as loaded. This leaves us with two fundamental axes of optimisation:

1. How can we enhance the entry sample to minimise redundant hundreds?
2. How a lot knowledge can we load directly, and the place ought to it’s saved on the GPU?

Tiled GEMM

As talked about beforehand, the naive method to GEMM leads to many redundant hundreds, which induces pointless overhead. Ideally, we’d wish to load every section of information solely as soon as and carry out all of the operations during which they’re used earlier than dropping them from reminiscence.

A sublime method to this drawback is tiling, which includes dividing giant matrices in smaller “tiles” or sub-matrices. Contemplate two matrices X and Y with shapes (4,6) and (6,4) respectively, X@Y leads to a matrix Z with form (4,4). 

To be able to compute the primary ingredient of Z, Z[0,0], we have to compute the dot product between the primary row of X and the primary column of Y: Z[0,0] = dot(X[0, :], Y[:, 0]). We are able to additionally break down the dot product into smaller chunks, for example in teams of three parts: Z[0,0] = dot(X[0,0:3], Y[0:3, 0]) + dot(X[0,3:6], Y[3:6, 0]). 

Alternatively, we will increase this method to 2 dimensions and compute a complete (2,2) block of Z at a time: Z[0:2, 0:2] = dot(X[0:2, 0:2], Y[0:2, 0:2]) + dot(X[0:2, 2:4], Y[2:4, 0:2]) + dot(X[0:2, 4:6], Y[4:6, 0:2]). 

Right here’s a visible illustration of tiled matrix multiplication:

The above animation illustrates how knowledge is reused in tiled GEMM. For every 2×2 block in X and Y, we compute 4 dot merchandise, which ends up in a (2,2) output matrix in Z. Since every tile accommodates 3 blocks, we have to accumulate 3 of those matrices to compute the ultimate (2,2) output in Z. This accumulation is represented by coloured cells in Z. 

Within the kitchen analogy, that is like fetching components from the shop and getting ready them on the kitchen counter (i.e. small shared reminiscence), reusing them a number of instances earlier than going again to the shop.

Importantly, reusing loaded knowledge over a number of steps permits this method to drastically cut back the variety of load operations. For (2,2) blocks, every X row and Y column is utilized in two dot merchandise. Subsequently, we’re performing twice as many operations with every block of loaded knowledge, roughly halving the variety of load operations! Word that this generalises to bigger blocks as effectively, utilizing a (32,32) block would cut back the variety of hundreds by an element of round 32. 

Now you’re in all probability questioning “how giant can these blocks be”? To reply this query, let’s recall how reminiscence is managed in trendy GPUs.

GPU Reminiscence Hierarchy

We distinguish 4 fundamental forms of reminiscence in Nvidia GPUs. Right here, we take the instance of an A100:

• Registers: The quickest and smallest kind of reminiscence on the GPU, residing straight inside every Streaming Multiprocessor (SM). On the A100, every SM offers 256 KB of register file house (65,536 × 32-bit registers), distributed amongst its threads. Every thread will get its personal non-public 32-bit registers for storing short-term variables and intermediate outcomes, avoiding reminiscence site visitors altogether. Nonetheless, register utilization per thread straight impacts occupancy, as utilizing too many registers per thread limits what number of threads can run concurrently.
• L1/Shared Reminiscence: On an A100, every SM has 192KB of SRAM that may be flexibly configured as both a hardware-managed L1 cache or a programmer-managed shared reminiscence. For performance-critical kernels like matrix multiplication, we explicitly use this house as shared reminiscence to stage knowledge tiles near the compute models, bypassing the L1 cache completely. This provides us fine-grained management over knowledge reuse.
• L2 cache: This cache is slower than L1 however a lot bigger, with round 40 MB shared throughout all SMs on the A100. It serves as a worldwide cache for each knowledge and directions, lowering the variety of accesses to high-latency HBM reminiscence. The L2 cache is coherent throughout SMs, which means that updates from one SM are seen to others, enabling synchronisation between thread blocks. Its bandwidth can attain a number of terabytes per second, performing as a buffer between the quick on-chip SRAM and the slower HBM.
• Excessive Bandwidth Reminiscence (HBM): That is the gadget reminiscence, it has a capability of both 40GB or 80GB relying on the A100 mannequin. It offers extraordinarily excessive bandwidth (as much as 2 TB/s on the 80 GB variant) however with a lot larger latency than on-chip caches. HBM is the place giant tensors, mannequin weights, and datasets reside throughout execution. Since accessing HBM is dear, environment friendly kernels goal to minimise knowledge motion and maximise on-chip knowledge reuse through registers and shared reminiscence.

As you possibly can see, the reminiscence hierarchy usually trades off capability with latency. Subsequently, maximising efficiency boils right down to loading knowledge from HBM into shared reminiscence effectively and reusing it as a lot as attainable.

Selecting our block measurement is crucial. We would like blocks to be giant sufficient to create lots of parallel work, however sufficiently small that their knowledge suits within the SM’s shared reminiscence and registers. A BLOCK_SIZE of 64 is a standard start line as a result of it’s a a number of of the warp measurement (32 threads), making certain full {hardware} utilisation.

Parallel Tiled GEMM

With these issues in thoughts, a pure follow-up to our tiled GEMM is to parallelise the computation of every pairs of tiles over a number of thread blocks, as depicted on the next animation.

Reminiscence Coalescing

Earlier than writing tiled GEMM in Triton, we have to contemplate one final element: reminiscence coalescing, a way that permits optimum use of worldwide reminiscence bandwidth. Reminiscence coalescing is achieved when subsequent threads in a warp entry subsequent reminiscence addresses. Think about a librarian needing to fetch books for a shopper, if all books are side-by-side on a shelf, they will seize them suddenly. In distinction, if all books are mendacity on totally different cabinets, they’ll must seize them one after the other, which takes considerably longer.

To know how this is applicable to our case, notice that matrices are saved linearly in reminiscence, in different phrases a (2,2) matrix is saved as a sequence of 4 consecutive parts. Frameworks like PyTorch undertake a row-major format, which means that parts of a matrix are per-row contiguous in reminiscence. As an example, parts of our (2,2) matrix can be saved as follows: [(0,0), (0,1), (1,0), (1,1)], discover that parts of the identical row are contiguous (touching) whereas parts of the identical column have a stride of 1 (separated by one ingredient).

This means that we will load rows utilizing coalesced hundreds, however columns do not fulfill this situation. Nonetheless, we have to entry columns of Y to compute dot merchandise. To be able to maximise efficiency, an excellent follow is to transpose Y in order that we iterate on its rows slightly than its columns. 

Nonetheless, transposing Y isn’t sufficient to change its format in reminiscence. As talked about beforehand, PyTorch shops matrices in a flat array. Every matrix dimension is related to a stride attribute, denoting the bounce essential to go from one ingredient to the following one alongside this dimension. As an example, a (10,10) matrix would have strides=(10,1). Certainly, ranging from ingredient [0,0], ingredient [1,0] is 10 reminiscence slots (i.e. one row) away, whereas ingredient [0,1] is adjoining. 

When transposing a tensor, PyTorch doesn’t modify the format in reminiscence however merely recomputes the strides. To be able to make the transpose efficient from a reminiscence standpoint we have to name Y.T.contiguous().

These are the required steps the load columns of Y effectively, nevertheless we’ll have to transpose the loaded blocks throughout the kernel to carry out the dot product correctly: z_block = tl.dot(X_block, Y_block.T).

Triton Implementation

From right here on, we first describe the kernel with out reminiscence coalescing to simplify the logic and pointer arithmetic earlier than summarising the adjustments required to make the load operations coalesced on Y columns.

Let’s begin by specializing in the PyTorch wrapper across the kernel. We have to learn M, N, Okay from the enter matrices and compute their strides since these constants might be helpful later within the kernel. Then, we outline the BLOCK_SIZE and declare the grid.

Now let’s dive into the precise kernel code. We’re going to utilize Triton’s make_block_ptr utility, which simplifies the pointer arithmetic. We create one block pointer per matrix and move the matrix form, its strides, and the scale of the block as inputs. Moreover, we specify the offset, the coordinate of the top-left ingredient within the present block. For X, this corresponds to (m_idx * BLOCK_SIZE, 0) the place m_idx is the index of the present block alongside the M dimension. 

From there, we outline z_acc, a zero matrix that may obtain the partial dot-products as we iterate by means of tiles. We now iterate by means of the shared dimension N, loading blocks of measurement (BLOCK_SIZE, BLOCK_SIZE), and accumulate their dot merchandise in z_acc. We then transfer the block pointers alongside the shared dimension by utilizing .advance.

You might need seen that when loading knowledge, we use boundary_check and padding_option as an alternative of masks and different as within the earlier article. These arguments are particular to using block pointers and specify which axes to test for out-of-bound operations (right here (0,1) for x and y) and find out how to deal with these invalid values. Right here we set them to zero to be ignored within the dot product.

We are able to now check out the efficiency of this kernel by utilizing the next perform:

def bench(fn: callable, x: torch.Tensor, y: torch.Tensor, repeat: int):
  flops = []
  med_latency = []

  for _ in tqdm(vary(repeat), desc=f"Benchmarking {fn.__name__}"):
    latency_ms = triton.testing.do_bench(
      lambda: fn(x, y),
      quantiles=[0.5], # get the median latency
      return_mode="all",
      )
    n_flops = 2 * M * N * Okay # matmul roughly requires 2*M*N*Okay operations
    tflops = n_flops / (latency_ms / 1e3) / 1e12

    med_latency.append(latency_ms)
    flops.append(tflops)

  flops = np.array(flops)
  med_latency = np.array(med_latency)
  print(f"Absolute Error: {torch.sum(torch.abs(X@Y - fn(x, y)))}")
  print(f"Median Latency: {med_latency.imply():.4f} ± {med_latency.std():.3f} ms")
  print(f"Throughput: {flops.imply():.4f} ± {flops.std():.3f} TeraFLOPS")

M = 8192
N = 6144
Okay = 4096

X = torch.randn((M, N), gadget="cuda", dtype=torch.float32)
Y = torch.randn((N, Okay), gadget="cuda", dtype=torch.float32)

bench(block_matmul, X, Y, repeat=10)

We get the next outputs (utilizing a T4 GPU on Colab):

Absolute Error: 0.0 # the kernel outputs the proper consequence!
Median Latency: 130.7831 ± 1.794 ms
Throughput: 3.1533 ± 0.043 TeraFLOPS

Now let’s assessment the adjustments required for coalesced hundreds on Y: we primarily have to flip the form, strides and offsets when defining the block pointer for Y. Moreover, we replace the block pointer to maneuver alongside the column dimension (beforehand row dimension). The complete code for this implementation is offered on GitHub.

@triton.jit
def coalesced_block_matmul_kernel(
    X_ptr, X_m_stride, X_n_stride,
    Y_ptr, Y_k_stride, Y_n_stride,
    Z_ptr, Z_m_stride, Z_k_stride,
    M, N, Okay,
    BLOCK_SIZE: tl.constexpr,
):
    ... 
    y_block_ptr = tl.make_block_ptr(
        base=Y_ptr,
        # flip the form, strides and offsets to match Y.T
        form=(Okay, N),
        strides=(Y_k_stride, Y_n_stride), 
        offsets=(k_idx * BLOCK_SIZE, 0),
        block_shape=(BLOCK_SIZE, BLOCK_SIZE),
        order=(0, 1),
    )
    ...

    for _ in vary(0, N, BLOCK_SIZE):
        ... # hundreds
        z_acc += tl.dot(x, y.T)  # transpose Y again for dot product
        x_block_ptr = tl.advance(x_block_ptr, offsets=(0, BLOCK_SIZE))
        # advance the block pointer alongside columns of Y.T (i.e rows of Y)
        y_block_ptr = tl.advance(y_block_ptr, offsets=(0, BLOCK_SIZE))

    tl.retailer(pointer=z_block_ptr, worth=z_acc, boundary_check=(0, 1))

def coalesced_block_matmul(X, Y):
    Y = Y.T.contiguous()  # Y is now (Okay,N)
    M, N = X.form
    Okay, _ = Y.form
    Z = torch.empty((M, Okay), gadget="cuda")

    x_stride_m, x_stride_n = X.stride()
    y_stride_k, y_stride_n = Y.stride()
    z_stride_m, z_stride_k = Z.stride()

    ...  # outline BLOCK_SIZE and grid

    coalesced_block_matmul_kernel[grid](
        X, x_stride_m, x_stride_n,
        Y, y_stride_n, y_stride_k,
        Z, z_stride_m, z_stride_k,
        M, N, Okay,
        BLOCK_SIZE,
    )

    return Z

Listed below are the outcomes of our benchmark for the kernel with coalesced hundreds for Y:

Absolute Error: 0.0 # Once more, the kernel is appropriate!
Median Latency: 261.9420 ± 0.858 ms
Throughput: 1.5741 ± 0.005 TeraFLOPS

Surprisingly, the throughput of this second kernel is simply half of what we obtained with the primary one, regardless of enhancing the effectivity of load operations 🤔

A fast inspection utilizing nsight (Nvidia’s kernel profiler, extra on that in a future article) reveals that the transpose operation throughout the kernel creates a “site visitors jam”. Particularly, the transpose creates financial institution conflicts, inflicting threads to stay idle more often than not. Notably, the warp scheduler has no eligible warp to dispatch 87.6% of the time as they’re ready for the financial institution battle to resolve. Moreover, the report reads:

———————– ———– ————–
Metric Title Metric Unit Metric Worth
———————– ———– ————–
…
DRAM Throughput % 8.20
Compute (SM) Throughput % 21.14
…

This means that the kernel is latency sure (i.e. neither reminiscence nor compute sure, seek advice from the earlier article for extra particulars). In distinction, the primary kernel is compute sure (i.e. rising compute will enhance efficiency) for the reason that compute throughput is excessive in comparison with the DRAM throughput.

———————– ———– ————–
Metric Title Metric Unit Metric Worth
———————– ———– ————–
…
DRAM Throughput % 29.35
Compute (SM) Throughput % 74.39
…

Conclusion

This experiment highlights the significance of profiling and empirical validation. Even well-intentioned optimisations like coalescing reminiscence accesses can introduce new bottlenecks if not evaluated rigorously. The primary kernel, although less complicated, was compute-bound and higher matched the {hardware} traits.

Within the subsequent articles of this collection, we’ll implement a softmax kernel, paying specific consideration to integrating Triton with PyTorch’s autograd and profiling kernels utilizing Nsight.

Till subsequent time! 👋

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