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[TUTORIAL] Enable all types in gemm tutorial (#456)
* Enable all types in gemm tutorial Co-authored-by: Shucai Xiao <shucai.xiao@amd.com>
This commit is contained in:
Lixun Zhang
330
python/perf-kernels/03-matrix-multiplication-all-types.py
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330
python/perf-kernels/03-matrix-multiplication-all-types.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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import sys
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import argparse
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import pytest
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import re
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@triton.autotune(
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configs=[
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triton.Config({'BLOCK_SIZE_M': 256, 'BLOCK_SIZE_N': 256, 'BLOCK_SIZE_K': 128, 'GROUP_SIZE_M': 4, 'waves_per_eu': 0}, num_warps=8, num_stages=0),
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triton.Config({'BLOCK_SIZE_M': 128, 'BLOCK_SIZE_N': 256, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 4, 'waves_per_eu': 0}, num_warps=8, num_stages=0),
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triton.Config({'BLOCK_SIZE_M': 128, 'BLOCK_SIZE_N': 256, 'BLOCK_SIZE_K': 16, 'GROUP_SIZE_M': 4, 'waves_per_eu': 2}, num_warps=4, num_stages=0),
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triton.Config({'BLOCK_SIZE_M': 128, 'BLOCK_SIZE_N': 128, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 1, 'waves_per_eu': 2}, num_warps=8, num_stages=0),
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triton.Config({'BLOCK_SIZE_M': 128, 'BLOCK_SIZE_N': 64, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 32, 'waves_per_eu': 2}, num_warps=4, num_stages=0),
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],
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key=['M', 'N', 'K'],
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)
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@triton.heuristics({
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'EVEN_K': lambda args: args['K'] % args['BLOCK_SIZE_K'] == 0,
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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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EVEN_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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if GROUP_SIZE_M == 1:
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pid_m = pid // num_pid_n
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pid_n = pid % num_pid_n
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else:
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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_k = tl.arange(0, BLOCK_SIZE_K)
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if torch.version.hip is None:
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offs_am = (pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M)) % M
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offs_bn = (pid_n * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N)) % N
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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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else:
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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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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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acc_dtype = tl.float32 if c_ptr.type.element_ty != tl.int8 else tl.int32
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accumulator = tl.zeros((BLOCK_SIZE_M, BLOCK_SIZE_N), dtype=acc_dtype)
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for k in range(0, tl.cdiv(K, BLOCK_SIZE_K)):
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# Load the next block of A and B, generate a mask by checking the K dimension.
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# If it is out of bounds, set it to 0.
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if EVEN_K:
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a = tl.load(a_ptrs)
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b = tl.load(b_ptrs)
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else:
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a = tl.load(a_ptrs, mask=offs_k[None, :] < K - k * BLOCK_SIZE_K, other=0.0)
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b = tl.load(b_ptrs, mask=offs_k[:, None] < K - k * BLOCK_SIZE_K, other=0.0)
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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(c_ptr.type.element_ty)
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# -----------------------------------------------------------
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# Write back the block of the output matrix C with masks.
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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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# %%
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# We can now create a convenience wrapper function that only takes two input tensors,
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# and (1) checks any shape constraint; (2) allocates the output; (3) launches the above kernel.
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def matmul(a, b, c, activation=""):
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# Check 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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# 1D launch kernel where each block gets its own program.
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grid = lambda META: (triton.cdiv(M, META['BLOCK_SIZE_M']) * triton.cdiv(N, META['BLOCK_SIZE_N']), )
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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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TORCH_HAS_FP8E5B16 = hasattr(torch, 'float8_e5m2fnuz')
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TORCH_HAS_FP8E4B8 = hasattr(torch, 'float8_e4m3fnuz')
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tl_to_torch_types = {
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tl.float16: torch.float16,
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tl.bfloat16: torch.bfloat16,
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tl.float32: torch.float32,
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tl.int8: torch.int8,
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tl.int32: torch.int32,
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}
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if TORCH_HAS_FP8E5B16:
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tl_to_torch_types[tl.float8e5b16] = torch.float8_e5m2fnuz
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if TORCH_HAS_FP8E4B8:
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tl_to_torch_types[tl.float8e4b8] = torch.float8_e4m3fnuz
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name_to_tl_types = {
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'int8': tl.int8,
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'int32': tl.int32,
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'fp16': tl.float16,
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'fp32': tl.float32,
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'bf16': tl.bfloat16,
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'fp8e4': tl.float8e4b8,
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'fp8e5': tl.float8e5b16,
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}
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def gen_input(M, N, ty_name, seed, device='cuda'):
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d_type = name_to_tl_types[ty_name]
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torch.manual_seed(seed)
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torch.cuda.manual_seed(seed)
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@triton.jit
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def copy_kernel(input_ptr, output_ptr, n_elements, BLOCK_SIZE: tl.constexpr):
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offsets = tl.program_id(axis=0) * BLOCK_SIZE + tl.arange(0, BLOCK_SIZE)
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mask = offsets < n_elements
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input = tl.load(input_ptr + offsets, mask=mask)
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output = input
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tl.store(output_ptr + offsets, output, mask=mask)
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raw_data = torch.randn((M, N), dtype=torch.float32, device='cuda')
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if (d_type == tl.float8e4b8 and TORCH_HAS_FP8E4B8) or \
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(d_type == tl.float8e5b16 and TORCH_HAS_FP8E5B16) or not d_type.is_fp8():
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input = raw_data.to(tl_to_torch_types[d_type])
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input_f16 = input.to(torch.float16)
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else:
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f8_tensor = raw_data.to(torch.int8)
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# keep only two bits of exponent to avoid overflow
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f8_tensor = f8_tensor & 0b00111111
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input = triton.reinterpret(f8_tensor, d_type)
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input_f16 = torch.empty_like(f8_tensor, dtype=torch.float16)
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grid = lambda meta: (triton.cdiv(n_elements, meta['BLOCK_SIZE']),)
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n_elements = raw_data.numel()
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copy_kernel[grid](input, input_f16, n_elements, BLOCK_SIZE=1024)
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return input, input_f16
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# %%
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# Unit Test
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# ---------
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#
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# We can test our custom matrix multiplication operation against a native torch implementation (i.e., rocBLAS).
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@pytest.mark.parametrize("M, N, K, in_dtype, out_dtype",
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[ (*shape, in_dtype, out_dtype)
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for shape in [(128, 256, 32), (128, 16, 32), (32, 128, 64),
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(128, 128, 64), (64, 128, 128), (32, 128, 64),
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(64, 64, 32), (32, 32, 128), (128, 128, 64),
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(64, 128, 128), (512, 512, 512), (1024, 1024, 1024)]
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for in_dtype, out_dtype in [('fp16', 'fp16'),
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('bf16', 'bf16'),
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('fp16', 'fp32'),
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('fp32', 'fp32'),
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('fp8e4', 'fp16'),
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('fp8e5', 'fp16'),
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('int8', 'int8'),
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('int8', 'int32')]]
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)
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def test_correctness(M, N, K, in_dtype, out_dtype):
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a, a_fp16 = gen_input(M, K, in_dtype, 1, device='cuda')
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b, b_fp16 = gen_input(K, N, in_dtype, 2, device='cuda')
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# Allocates output.
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tl_out_dtype = name_to_tl_types[out_dtype]
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c = torch.empty((M, N), device=a.device, dtype=tl_to_torch_types[tl_out_dtype])
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matmul(a, b, c, activation="")
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torch_output = torch.matmul(a_fp16, b_fp16)
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print(f"triton_output={c}")
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print(f"torch_output={torch_output}")
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rtol = 0 if torch.version.hip is None else 1e-2
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if torch.allclose(c.to(torch.float16), torch_output, atol=5e-2, rtol=rtol):
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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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assert torch.allclose(c, torch_output, atol=1e-2, rtol=rtol)
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# %%
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# Benchmark
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# ---------
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#
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# Square Matrix Performance
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# ~~~~~~~~~~~~~~~~~~~~~~~~~~
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#
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# We can now compare the performance of our kernel against that of rocBLAS. Here we focus on square matrices,
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# but feel free to arrange this script as you wish to benchmark any other matrix shape.
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def get_type(provider):
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res = re.findall(r'\(.*?\)', provider)
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return res[0][1:-1]
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def get_x_vals():
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x_vals = [(1024 * v, 1024 * v, 1024 * v) for v in range (1, 9)]
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x_vals += [(4864, 4096, 8192), (9728, 8192, 65536)]
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return x_vals
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inout_dtype = {
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'int8': torch.int8,
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'fp16': torch.float16,
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'fp32': torch.float32,
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'bf16': torch.bfloat16,
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'fp8e4': torch.float16,
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'fp8e5': torch.float16,
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}
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@triton.testing.perf_report(
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triton.testing.Benchmark(
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x_names=['M', 'N', 'K'], # Argument names to use as an x-axis for the plot
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x_vals = get_x_vals(),
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line_arg='provider', # Argument name whose value corresponds to a different line in the plot
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# Possible values for `line_arg`
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line_vals=['rocblas(fp16)', 'rocblas(bf16)', 'triton(fp16)', 'triton(bf16)', 'triton(int8)', 'triton(fp8e4)', 'triton(fp8e5)'],
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# Label name for the lines
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line_names=["rocBLAS.Fp16", "rocBLAS.Bf16", "Triton.Fp16", "Triton.Bf16", "Triton.Int8", "Triton.Fp8E4", "Triton.Fp8E5"],
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ylabel="TFLOPS", # Label name for the y-axis
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plot_name="matmul-performance", # Name for the plot, used also as a file name for saving the plot.
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args={},
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))
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def benchmark(M, N, K, provider):
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in_dtype = get_type(provider)
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out_dtype = inout_dtype[in_dtype]
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quantiles = [0.5, 0.2, 0.8]
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if 'rocblas' in provider:
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a = torch.randn((M, K), dtype=tl_to_torch_types[name_to_tl_types[in_dtype]], device='cuda')
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b = torch.randn((K, N), dtype=tl_to_torch_types[name_to_tl_types[in_dtype]], device='cuda')
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ms, min_ms, max_ms = triton.testing.do_bench(lambda: torch.matmul(a, b), quantiles=quantiles)
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else: # triton, different data types
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assert "triton" in provider
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a, _ = gen_input(M, K, in_dtype, 1, device='cuda')
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b, _ = gen_input(K, N, in_dtype, 2, device='cuda')
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# Allocates output.
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c = torch.empty((M, N), device=a.device, dtype=out_dtype)
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ms, min_ms, max_ms = triton.testing.do_bench(lambda: matmul(a, b, c, activation=""), quantiles=quantiles)
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global verbose
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if verbose:
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print(f'SIZE: {M},{N},{K} Best tuning config: ({matmul_kernel.get_best_config()})')
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perf = lambda ms: 2 * M * N * K * 1e-12 / (ms * 1e-3)
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return perf(ms), perf(max_ms), perf(min_ms)
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def parse_args():
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parser = argparse.ArgumentParser(
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prog="GEMM tutorial example",
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allow_abbrev=False,
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)
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parser.add_argument("-v", action='store_true', default=False, help="Print out the best tuning config")
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args = parser.parse_args()
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return args
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def main():
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# assign to a global verbose var to indicate whether print
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# best tuning config
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global verbose
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args = parse_args()
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verbose=args.v
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benchmark.run(show_plots=True, print_data=True)
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if __name__ == '__main__':
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sys.exit(main())
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