Delete triton_test.py
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Andrey Glushenkov
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triton_test.py
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triton_test.py
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import torch
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import triton
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import triton.language as tl
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# %
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# :code:`triton.jit`'ed functions can be auto-tuned by using the `triton.autotune`
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# decorator, which consumes:
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# - A list of :code:`triton.Config` objects that define different configurations of
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# meta-parameters (e.g., BLOCK_SIZE_M) and compilation options (e.g., num_warps) to try
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# - An autotuning *key* whose change in values will trigger evaluation of all the
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# provided configs
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@triton.autotune(
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configs=[
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triton.Config({'BLOCK_SIZE_M': 128, 'BLOCK_SIZE_N': 256, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=3, num_warps=8),
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triton.Config({'BLOCK_SIZE_M': 256, 'BLOCK_SIZE_N': 128, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=3, num_warps=8),
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triton.Config({'BLOCK_SIZE_M': 256, 'BLOCK_SIZE_N': 64, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4),
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triton.Config({'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_N': 256, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4),
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triton.Config({'BLOCK_SIZE_M': 128, 'BLOCK_SIZE_N': 128, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4),
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triton.Config({'BLOCK_SIZE_M': 128, 'BLOCK_SIZE_N': 64, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4),
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triton.Config({'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_N': 128, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4),
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triton.Config({'BLOCK_SIZE_M': 128, 'BLOCK_SIZE_N': 32, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4),
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triton.Config({'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_N': 32, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=5, num_warps=2),
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triton.Config({'BLOCK_SIZE_M': 32, 'BLOCK_SIZE_N': 64, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=5, num_warps=2),
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],
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key=['M', 'N', 'K'],
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)
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@triton.jit
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def matmul_kernel(
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# Pointers to matrices
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a_ptr, b_ptr, c_ptr,
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# Matrix dimensions
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M, N, K,
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# The stride variables represent how much to increase the ptr by when moving by 1
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# element in a particular dimension. E.g. stride_am is how much to increase a_ptr
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# by to get the element one row down (A has M rows)
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stride_am, stride_ak,
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stride_bk, stride_bn,
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stride_cm, stride_cn,
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# Meta-parameters
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BLOCK_SIZE_M: tl.constexpr, BLOCK_SIZE_N: tl.constexpr, BLOCK_SIZE_K: tl.constexpr,
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GROUP_SIZE_M: tl.constexpr,
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ACTIVATION: tl.constexpr,
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):
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"""Kernel for computing the matmul C = A x B.
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A has shape (M, K), B has shape (K, N) and C has shape (M, N)
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"""
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# -----------------------------------------------------------
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# Map program ids `pid` to the block of C it should compute.
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# This is done in a grouped ordering to promote L2 data reuse
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# See above `L2 Cache Optimizations` section for details
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pid = tl.program_id(axis=0)
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num_pid_m = tl.cdiv(M, BLOCK_SIZE_M)
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num_pid_n = tl.cdiv(N, BLOCK_SIZE_N)
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num_pid_in_group = GROUP_SIZE_M * num_pid_n
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group_id = pid // num_pid_in_group
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first_pid_m = group_id * GROUP_SIZE_M
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group_size_m = min(num_pid_m - first_pid_m, GROUP_SIZE_M)
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pid_m = first_pid_m + (pid % group_size_m)
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pid_n = (pid % num_pid_in_group) // group_size_m
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# ----------------------------------------------------------
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# Create pointers for the first blocks of A and B.
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# We will advance this pointer as we move in the K direction
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# and accumulate
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# a_ptrs is a block of [BLOCK_SIZE_M, BLOCK_SIZE_K] pointers
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# b_ptrs is a block of [BLOCK_SIZE_K, BLOCK_SIZE_n] pointers
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# see above `Pointer Arithmetics` section for details
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offs_am = pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M)
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offs_bn = pid_n * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N)
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offs_k = tl.arange(0, BLOCK_SIZE_K)
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a_ptrs = a_ptr + (offs_am[:, None] * stride_am + offs_k[None, :] * stride_ak)
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b_ptrs = b_ptr + (offs_k[:, None] * stride_bk + offs_bn[None, :] * stride_bn)
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# -----------------------------------------------------------
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# Iterate to compute a block of the C matrix
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# We accumulate into a `[BLOCK_SIZE_M, BLOCK_SIZE_N]` block
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# of fp32 values for higher accuracy.
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# `accumulator` will be converted back to fp16 after the loop
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accumulator = tl.zeros((BLOCK_SIZE_M, BLOCK_SIZE_N), dtype=tl.float32)
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for k in range(0, K, BLOCK_SIZE_K):
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# Note that for simplicity, we don't apply a mask here.
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# This means that if K is not a multiple of BLOCK_SIZE_K,
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# this will access out-of-bounds memory and produce an
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# error or (worse!) incorrect results.
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a = tl.load(a_ptrs)
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b = tl.load(b_ptrs)
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# We accumulate along the K dimension
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accumulator += tl.dot(a, b)
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# Advance the ptrs to the next K block
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a_ptrs += BLOCK_SIZE_K * stride_ak
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b_ptrs += BLOCK_SIZE_K * stride_bk
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# you can fuse arbitrary activation functions here
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# while the accumulator is still in FP32!
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if ACTIVATION == "leaky_relu":
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accumulator = leaky_relu(accumulator)
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c = accumulator.to(tl.float16)
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# -----------------------------------------------------------
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# Write back the block of the output matrix C
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offs_cm = pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M)
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offs_cn = pid_n * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N)
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c_ptrs = c_ptr + stride_cm * offs_cm[:, None] + stride_cn * offs_cn[None, :]
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c_mask = (offs_cm[:, None] < M) & (offs_cn[None, :] < N)
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tl.store(c_ptrs, c, mask=c_mask)
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# we can fuse `leaky_relu` by providing it as an `ACTIVATION` meta-parameter in `_matmul`
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@triton.jit
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def leaky_relu(x):
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x = x + 1
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return tl.where(x >= 0, x, 0.01 * x)
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def matmul(a, b, activation=""):
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# checks constraints
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assert a.shape[1] == b.shape[0], "incompatible dimensions"
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assert a.is_contiguous(), "matrix A must be contiguous"
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assert b.is_contiguous(), "matrix B must be contiguous"
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M, K = a.shape
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K, N = b.shape
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assert (
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K % 32 == 0
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), "We don't check memory-out-of-bounds with K so K must be divisible by BLOCK_SIZE_K"
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# allocates output
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c = torch.empty((M, N), device=a.device, dtype=a.dtype)
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# 1D launch kernel where each block gets its own program.
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grid = lambda META: (
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triton.cdiv(M, META['BLOCK_SIZE_M']) * triton.cdiv(N, META['BLOCK_SIZE_N']),
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)
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matmul_kernel[grid](
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a, b, c,
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M, N, K,
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a.stride(0), a.stride(1),
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b.stride(0), b.stride(1),
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c.stride(0), c.stride(1),
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ACTIVATION=activation,
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)
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return c
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torch.manual_seed(0)
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a = torch.randn((512, 512), device='cuda', dtype=torch.float16)
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b = torch.randn((512, 512), device='cuda', dtype=torch.float16)
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triton_output = matmul(a, b)
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torch_output = torch.matmul(a, b)
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print(f"triton_output={triton_output}")
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print(f"torch_output={torch_output}")
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if triton.testing.allclose(triton_output, torch_output):
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print("✅ Triton and Torch match")
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else:
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print("❌ Triton and Torch differ")
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