Studying Triton One Kernel at a Time: Matrix Multiplication

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

Naive GEMM

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

This operation entails computing the dot merchandise of all pairs of rows and columns in X and Y respectively. A simple NumPy implementation may look one thing like this:

Whereas simple to jot down, learn and perceive, this implementation is very inefficient when it comes to reminiscence entry and caching. As talked about within the first article of this collection, a basic side of GPU optimisation is minimising information transfers. 

Nevertheless, our present implementation begins by loading a row from X, iteratively masses all Ok columns of Y, computes their dot product and repeats the method for each row in X. This leads to a complete of M(Ok+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 (international reminiscence) each time you want a brand new ingredient for a dish as a substitute of making ready all of the elements in your kitchen counter (shared reminiscence). Ideally, we want 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 predominant axes of optimisation:

1. How can we enhance the entry sample to minimise redundant masses?
2. How a lot information can we load without delay, 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 masses, which induces pointless overhead. Ideally, we’d prefer to load every phase of information solely as soon as and carry out all of the operations by which they’re used earlier than dropping them from reminiscence.

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

As a way to compute the primary factor 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 components: Z[0,0] = dot(X[0,0:3], Y[0:3, 0]) + dot(X[0,3:6], Y[3:6, 0]). 

Alternatively, we are able to broaden this method to 2 dimensions and compute a whole (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 information is reused in tiled GEMM. For every 2×2 block in X and Y, we compute 4 dot merchandise, which leads to a (2,2) output matrix in Z. Since every tile incorporates 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 elements from the shop and making 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 information 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. Due to this fact, we’re performing twice as many operations with every block of loaded information, roughly halving the variety of load operations! Be aware that this generalises to bigger blocks as properly, utilizing a (32,32) block would scale back the variety of masses by an element of round 32. 

Now you’re most likely 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 predominant varieties of reminiscence in Nvidia GPUs. Right here, we take the instance of an A100:

• Registers: The quickest and smallest sort of reminiscence on the GPU, residing straight inside every Streaming Multiprocessor (SM). On the A100, every SM gives 256 KB of register file area (65,536 × 32-bit registers), distributed amongst its threads. Every thread will get its personal personal 32-bit registers for storing momentary variables and intermediate outcomes, avoiding reminiscence visitors altogether. Nevertheless, 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 area as shared reminiscence to stage information tiles near the compute items, bypassing the L1 cache totally. This provides us fine-grained management over information 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 world cache for each information and directions, decreasing the variety of accesses to high-latency HBM reminiscence. The L2 cache is coherent throughout SMs, that 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 machine reminiscence, it has a capability of both 40GB or 80GB relying on the A100 mannequin. It gives 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 purpose to minimise information motion and maximise on-chip information reuse by way of registers and shared reminiscence.

As you may see, the reminiscence hierarchy usually trades off capability with latency. Due to this fact, maximising efficiency boils right down to loading information from HBM into shared reminiscence effectively and reusing it as a lot as doable.

Selecting our block dimension is crucial. We wish blocks to be giant sufficient to create quite a lot of parallel work, however sufficiently small that their information matches within the SM’s shared reminiscence and registers. A BLOCK_SIZE of 64 is a typical start line as a result of it’s a a number of of the warp dimension (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 enables optimum use of world 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 consumer, if all books are side-by-side on a shelf, they will seize them . In distinction, if all books are mendacity on completely different cabinets, they’ll need to seize them one after the other, which takes considerably longer.

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

This means that we are able to load rows utilizing coalesced masses, however columns do not fulfill this situation. Nevertheless, we have to entry columns of Y to compute dot merchandise. As a way to maximise efficiency, a great apply is to transpose Y in order that we iterate on its rows quite than its columns. 

Nevertheless, transposing Y isn’t sufficient to change its structure 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 factor to the subsequent one alongside this dimension. As an illustration, a (10,10) matrix would have strides=(10,1). Certainly, ranging from factor [0,0], factor [1,0] is 10 reminiscence slots (i.e. one row) away, whereas factor [0,1] is adjoining. 

When transposing a tensor, PyTorch doesn’t modify the structure in reminiscence however merely recomputes the strides. As a way 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 modifications 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, Ok from the enter matrices and compute their strides since these constants can 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 cross the matrix form, its strides, and the scale of the block as inputs. Moreover, we specify the offset, the coordinate of the top-left factor 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 can obtain the partial dot-products as we iterate via tiles. We now iterate via the shared dimension N, loading blocks of dimension (BLOCK_SIZE, BLOCK_SIZE), and accumulate their dot merchandise in z_acc. We then transfer the block pointers alongside the shared dimension through the use of .advance.

You might need seen that when loading information, we use boundary_check and padding_option as a substitute of masks and different as within the earlier article. These arguments are particular to the usage of block pointers and specify which axes to test for out-of-bound operations (right here (0,1) for x and y) and how you can 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 through the use of 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 * Ok # matmul roughly requires 2*M*N*Ok 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
Ok = 4096

X = torch.randn((M, N), machine="cuda", dtype=torch.float32)
Y = torch.randn((N, Ok), machine="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 right consequence!
Median Latency: 130.7831 ± 1.794 ms
Throughput: 3.1533 ± 0.043 TeraFLOPS

Now let’s overview the modifications required for coalesced masses 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 total code for this implementation is accessible 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, Ok,
    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=(Ok, 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):
        ... # masses
        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 (Ok,N)
    M, N = X.form
    Ok, _ = Y.form
    Z = torch.empty((M, Ok), machine="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, Ok,
        BLOCK_SIZE,
    )

    return Z

Listed here are the outcomes of our benchmark for the kernel with coalesced masses for Y:

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

Surprisingly, the throughput of this second kernel is just half of what we obtained with the primary one, regardless of bettering 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 “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. growing compute will enhance efficiency) because the 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 easier, 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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