469 lines
15 KiB
Python
469 lines
15 KiB
Python
import torch
|
|
|
|
import triton
|
|
import triton.language as tl
|
|
|
|
DEVICE = "cuda"
|
|
|
|
|
|
def is_cuda():
|
|
return triton.runtime.driver.active.get_current_target().backend == "cuda"
|
|
|
|
|
|
def is_hip_cdna2():
|
|
target = triton.runtime.driver.active.get_current_target()
|
|
return target.backend == "hip" and target.arch == "gfx90a"
|
|
|
|
|
|
def get_cuda_autotune_config():
|
|
return [
|
|
triton.Config(
|
|
{
|
|
"BLOCK_SIZE_M": 128,
|
|
"BLOCK_SIZE_N": 256,
|
|
"BLOCK_SIZE_K": 64,
|
|
"GROUP_SIZE_M": 8,
|
|
},
|
|
num_stages=3,
|
|
num_warps=8,
|
|
),
|
|
triton.Config(
|
|
{
|
|
"BLOCK_SIZE_M": 64,
|
|
"BLOCK_SIZE_N": 256,
|
|
"BLOCK_SIZE_K": 32,
|
|
"GROUP_SIZE_M": 8,
|
|
},
|
|
num_stages=4,
|
|
num_warps=4,
|
|
),
|
|
triton.Config(
|
|
{
|
|
"BLOCK_SIZE_M": 128,
|
|
"BLOCK_SIZE_N": 128,
|
|
"BLOCK_SIZE_K": 32,
|
|
"GROUP_SIZE_M": 8,
|
|
},
|
|
num_stages=4,
|
|
num_warps=4,
|
|
),
|
|
triton.Config(
|
|
{
|
|
"BLOCK_SIZE_M": 128,
|
|
"BLOCK_SIZE_N": 64,
|
|
"BLOCK_SIZE_K": 32,
|
|
"GROUP_SIZE_M": 8,
|
|
},
|
|
num_stages=4,
|
|
num_warps=4,
|
|
),
|
|
triton.Config(
|
|
{
|
|
"BLOCK_SIZE_M": 64,
|
|
"BLOCK_SIZE_N": 128,
|
|
"BLOCK_SIZE_K": 32,
|
|
"GROUP_SIZE_M": 8,
|
|
},
|
|
num_stages=4,
|
|
num_warps=4,
|
|
),
|
|
triton.Config(
|
|
{
|
|
"BLOCK_SIZE_M": 128,
|
|
"BLOCK_SIZE_N": 32,
|
|
"BLOCK_SIZE_K": 32,
|
|
"GROUP_SIZE_M": 8,
|
|
},
|
|
num_stages=4,
|
|
num_warps=4,
|
|
),
|
|
triton.Config(
|
|
{
|
|
"BLOCK_SIZE_M": 64,
|
|
"BLOCK_SIZE_N": 32,
|
|
"BLOCK_SIZE_K": 32,
|
|
"GROUP_SIZE_M": 8,
|
|
},
|
|
num_stages=5,
|
|
num_warps=2,
|
|
),
|
|
triton.Config(
|
|
{
|
|
"BLOCK_SIZE_M": 32,
|
|
"BLOCK_SIZE_N": 64,
|
|
"BLOCK_SIZE_K": 32,
|
|
"GROUP_SIZE_M": 8,
|
|
},
|
|
num_stages=5,
|
|
num_warps=2,
|
|
),
|
|
# Good config for fp8 inputs.
|
|
triton.Config(
|
|
{
|
|
"BLOCK_SIZE_M": 128,
|
|
"BLOCK_SIZE_N": 256,
|
|
"BLOCK_SIZE_K": 128,
|
|
"GROUP_SIZE_M": 8,
|
|
},
|
|
num_stages=3,
|
|
num_warps=8,
|
|
),
|
|
triton.Config(
|
|
{
|
|
"BLOCK_SIZE_M": 256,
|
|
"BLOCK_SIZE_N": 128,
|
|
"BLOCK_SIZE_K": 128,
|
|
"GROUP_SIZE_M": 8,
|
|
},
|
|
num_stages=3,
|
|
num_warps=8,
|
|
),
|
|
triton.Config(
|
|
{
|
|
"BLOCK_SIZE_M": 256,
|
|
"BLOCK_SIZE_N": 64,
|
|
"BLOCK_SIZE_K": 128,
|
|
"GROUP_SIZE_M": 8,
|
|
},
|
|
num_stages=4,
|
|
num_warps=4,
|
|
),
|
|
triton.Config(
|
|
{
|
|
"BLOCK_SIZE_M": 64,
|
|
"BLOCK_SIZE_N": 256,
|
|
"BLOCK_SIZE_K": 128,
|
|
"GROUP_SIZE_M": 8,
|
|
},
|
|
num_stages=4,
|
|
num_warps=4,
|
|
),
|
|
triton.Config(
|
|
{
|
|
"BLOCK_SIZE_M": 128,
|
|
"BLOCK_SIZE_N": 128,
|
|
"BLOCK_SIZE_K": 128,
|
|
"GROUP_SIZE_M": 8,
|
|
},
|
|
num_stages=4,
|
|
num_warps=4,
|
|
),
|
|
triton.Config(
|
|
{
|
|
"BLOCK_SIZE_M": 128,
|
|
"BLOCK_SIZE_N": 64,
|
|
"BLOCK_SIZE_K": 64,
|
|
"GROUP_SIZE_M": 8,
|
|
},
|
|
num_stages=4,
|
|
num_warps=4,
|
|
),
|
|
triton.Config(
|
|
{
|
|
"BLOCK_SIZE_M": 64,
|
|
"BLOCK_SIZE_N": 128,
|
|
"BLOCK_SIZE_K": 64,
|
|
"GROUP_SIZE_M": 8,
|
|
},
|
|
num_stages=4,
|
|
num_warps=4,
|
|
),
|
|
triton.Config(
|
|
{
|
|
"BLOCK_SIZE_M": 128,
|
|
"BLOCK_SIZE_N": 32,
|
|
"BLOCK_SIZE_K": 64,
|
|
"GROUP_SIZE_M": 8,
|
|
},
|
|
num_stages=4,
|
|
num_warps=4,
|
|
),
|
|
]
|
|
|
|
|
|
def get_hip_autotune_config():
|
|
return [
|
|
triton.Config(
|
|
{
|
|
"BLOCK_SIZE_M": 128,
|
|
"BLOCK_SIZE_N": 256,
|
|
"BLOCK_SIZE_K": 16,
|
|
"GROUP_SIZE_M": 1,
|
|
"waves_per_eu": 2,
|
|
},
|
|
num_warps=4,
|
|
num_stages=2,
|
|
),
|
|
triton.Config(
|
|
{
|
|
"BLOCK_SIZE_M": 256,
|
|
"BLOCK_SIZE_N": 256,
|
|
"BLOCK_SIZE_K": 16,
|
|
"GROUP_SIZE_M": 4,
|
|
"waves_per_eu": 2,
|
|
},
|
|
num_warps=8,
|
|
num_stages=2,
|
|
),
|
|
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=2,
|
|
),
|
|
triton.Config(
|
|
{
|
|
"BLOCK_SIZE_M": 64,
|
|
"BLOCK_SIZE_N": 128,
|
|
"BLOCK_SIZE_K": 32,
|
|
"GROUP_SIZE_M": 8,
|
|
"waves_per_eu": 3,
|
|
},
|
|
num_warps=4,
|
|
num_stages=2,
|
|
),
|
|
triton.Config(
|
|
{
|
|
"BLOCK_SIZE_M": 64,
|
|
"BLOCK_SIZE_N": 64,
|
|
"BLOCK_SIZE_K": 32,
|
|
"GROUP_SIZE_M": 1,
|
|
"waves_per_eu": 8,
|
|
},
|
|
num_warps=4,
|
|
num_stages=2,
|
|
),
|
|
]
|
|
|
|
|
|
def get_autotune_config():
|
|
if is_cuda():
|
|
return get_cuda_autotune_config()
|
|
else:
|
|
return get_hip_autotune_config()
|
|
|
|
|
|
# `triton.jit`'ed functions can be auto-tuned by using the `triton.autotune` decorator, which consumes:
|
|
# - A list of `triton.Config` objects that define different configurations of
|
|
# meta-parameters (e.g., `BLOCK_SIZE_M`) and compilation options (e.g., `num_warps`) to try
|
|
# - An auto-tuning *key* whose change in values will trigger evaluation of all the
|
|
# provided configs
|
|
@triton.autotune(
|
|
configs=get_autotune_config(),
|
|
key=["M", "N", "K"],
|
|
)
|
|
@triton.jit
|
|
def matmul_kernel(
|
|
# Pointers to matrices
|
|
a_ptr,
|
|
b_ptr,
|
|
c_ptr,
|
|
# Matrix dimensions
|
|
M,
|
|
N,
|
|
K,
|
|
# The stride variables represent how much to increase the ptr by when moving by 1
|
|
# element in a particular dimension. E.g. `stride_am` is how much to increase `a_ptr`
|
|
# by to get the element one row down (A has M rows).
|
|
stride_am,
|
|
stride_ak, #
|
|
stride_bk,
|
|
stride_bn, #
|
|
stride_cm,
|
|
stride_cn,
|
|
# Meta-parameters
|
|
BLOCK_SIZE_M: tl.constexpr,
|
|
BLOCK_SIZE_N: tl.constexpr,
|
|
BLOCK_SIZE_K: tl.constexpr, #
|
|
GROUP_SIZE_M: tl.constexpr, #
|
|
ACTIVATION: tl.constexpr, #
|
|
):
|
|
"""Kernel for computing the matmul C = A x B.
|
|
A has shape (M, K), B has shape (K, N) and C has shape (M, N)
|
|
"""
|
|
# -----------------------------------------------------------
|
|
# Map program ids `pid` to the block of C it should compute.
|
|
# This is done in a grouped ordering to promote L2 data reuse.
|
|
# See above `L2 Cache Optimizations` section for details.
|
|
pid = tl.program_id(axis=0)
|
|
num_pid_m = tl.cdiv(M, BLOCK_SIZE_M)
|
|
num_pid_n = tl.cdiv(N, BLOCK_SIZE_N)
|
|
num_pid_in_group = GROUP_SIZE_M * num_pid_n
|
|
group_id = pid // num_pid_in_group
|
|
first_pid_m = group_id * GROUP_SIZE_M
|
|
group_size_m = min(num_pid_m - first_pid_m, GROUP_SIZE_M)
|
|
pid_m = first_pid_m + ((pid % num_pid_in_group) % group_size_m)
|
|
pid_n = (pid % num_pid_in_group) // group_size_m
|
|
|
|
# ----------------------------------------------------------
|
|
# Create pointers for the first blocks of A and B.
|
|
# We will advance this pointer as we move in the K direction
|
|
# and accumulate
|
|
# `a_ptrs` is a block of [BLOCK_SIZE_M, BLOCK_SIZE_K] pointers
|
|
# `b_ptrs` is a block of [BLOCK_SIZE_K, BLOCK_SIZE_N] pointers
|
|
# See above `Pointer Arithmetic` section for details
|
|
offs_am = (pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M)) % M
|
|
offs_bn = (pid_n * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N)) % N
|
|
offs_k = tl.arange(0, BLOCK_SIZE_K)
|
|
a_ptrs = a_ptr + (offs_am[:, None] * stride_am + offs_k[None, :] * stride_ak)
|
|
b_ptrs = b_ptr + (offs_k[:, None] * stride_bk + offs_bn[None, :] * stride_bn)
|
|
|
|
# -----------------------------------------------------------
|
|
# Iterate to compute a block of the C matrix.
|
|
# We accumulate into a `[BLOCK_SIZE_M, BLOCK_SIZE_N]` block
|
|
# of fp32 values for higher accuracy.
|
|
# `accumulator` will be converted back to fp16 after the loop.
|
|
accumulator = tl.zeros((BLOCK_SIZE_M, BLOCK_SIZE_N), dtype=tl.float32)
|
|
for k in range(0, tl.cdiv(K, BLOCK_SIZE_K)):
|
|
# Load the next block of A and B, generate a mask by checking the K dimension.
|
|
# If it is out of bounds, set it to 0.
|
|
a = tl.load(a_ptrs, mask=offs_k[None, :] < K - k * BLOCK_SIZE_K, other=0.0)
|
|
b = tl.load(b_ptrs, mask=offs_k[:, None] < K - k * BLOCK_SIZE_K, other=0.0)
|
|
# We accumulate along the K dimension.
|
|
accumulator = tl.dot(a, b, accumulator)
|
|
# Advance the ptrs to the next K block.
|
|
a_ptrs += BLOCK_SIZE_K * stride_ak
|
|
b_ptrs += BLOCK_SIZE_K * stride_bk
|
|
# You can fuse arbitrary activation functions here
|
|
# while the accumulator is still in FP32!
|
|
if ACTIVATION == "leaky_relu":
|
|
accumulator = leaky_relu(accumulator)
|
|
c = accumulator.to(tl.float16)
|
|
|
|
# -----------------------------------------------------------
|
|
# Write back the block of the output matrix C with masks.
|
|
offs_cm = pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M)
|
|
offs_cn = pid_n * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N)
|
|
c_ptrs = c_ptr + stride_cm * offs_cm[:, None] + stride_cn * offs_cn[None, :]
|
|
c_mask = (offs_cm[:, None] < M) & (offs_cn[None, :] < N)
|
|
tl.store(c_ptrs, c, mask=c_mask)
|
|
|
|
|
|
# We can fuse `leaky_relu` by providing it as an `ACTIVATION` meta-parameter in `matmul_kernel`.
|
|
@triton.jit
|
|
def leaky_relu(x):
|
|
return tl.where(x >= 0, x, 0.01 * x)
|
|
|
|
|
|
def matmul(a, b, activation=""):
|
|
# Check constraints.
|
|
assert a.shape[1] == b.shape[0], "Incompatible dimensions"
|
|
assert a.is_contiguous(), "Matrix A must be contiguous"
|
|
M, K = a.shape
|
|
K, N = b.shape
|
|
# Allocates output.
|
|
c = torch.empty((M, N), device=a.device, dtype=torch.float16)
|
|
# 1D launch kernel where each block gets its own program.
|
|
grid = lambda META: (
|
|
triton.cdiv(M, META["BLOCK_SIZE_M"]) * triton.cdiv(N, META["BLOCK_SIZE_N"]),
|
|
)
|
|
matmul_kernel[grid](
|
|
a,
|
|
b,
|
|
c, #
|
|
M,
|
|
N,
|
|
K, #
|
|
a.stride(0),
|
|
a.stride(1), #
|
|
b.stride(0),
|
|
b.stride(1), #
|
|
c.stride(0),
|
|
c.stride(1), #
|
|
ACTIVATION=activation, #
|
|
)
|
|
return c
|
|
|
|
|
|
torch.manual_seed(0)
|
|
a = torch.randn((512, 512), device=DEVICE, dtype=torch.float16)
|
|
b = torch.randn((512, 512), device=DEVICE, dtype=torch.float16)
|
|
triton_output = matmul(a, b)
|
|
torch_output = torch.matmul(a, b)
|
|
print(f"triton_output_with_fp16_inputs={triton_output}")
|
|
print(f"torch_output_with_fp16_inputs={torch_output}")
|
|
# Bigger tolerance for AMD CDNA2 devices.
|
|
# CDNA2 devices use reduced precision fp16 and bf16 and flush input and
|
|
# output denormal values to zero. Detailed info is at: https://pytorch.org/docs/stable/notes/numerical_accuracy.html#reduced-precision-fp16-and-bf16-gemms-and-convolutions-on-amd-instinct-mi200-devices
|
|
rtol = 1e-2 if is_hip_cdna2() else 0
|
|
if torch.allclose(triton_output, torch_output, atol=1e-2, rtol=rtol):
|
|
print("✅ Triton and Torch match")
|
|
else:
|
|
print("❌ Triton and Torch differ")
|
|
|
|
TORCH_HAS_FP8 = hasattr(torch, "float8_e5m2")
|
|
if TORCH_HAS_FP8 and is_cuda():
|
|
torch.manual_seed(0)
|
|
a = torch.randn((512, 512), device=DEVICE, dtype=torch.float16)
|
|
b = torch.randn((512, 512), device=DEVICE, dtype=torch.float16)
|
|
a = a.to(torch.float8_e5m2)
|
|
# pre-transpose b for efficiency.
|
|
b = b.T
|
|
b = b.to(torch.float8_e5m2)
|
|
triton_output = matmul(a, b)
|
|
torch_output = torch.matmul(a.to(torch.float16), b.to(torch.float16))
|
|
print(f"triton_output_with_fp8_inputs={triton_output}")
|
|
print(f"torch_output_with_fp8_inputs={torch_output}")
|
|
if torch.allclose(triton_output, torch_output, atol=0.125, rtol=0):
|
|
print("✅ Triton and Torch match")
|
|
else:
|
|
print("❌ Triton and Torch differ")
|
|
|
|
ref_lib = "cuBLAS" if is_cuda() else "rocBLAS"
|
|
|
|
configs = []
|
|
for fp8_inputs in [False, True]:
|
|
if fp8_inputs and (not TORCH_HAS_FP8 or not is_cuda()):
|
|
continue
|
|
configs.append(
|
|
triton.testing.Benchmark(
|
|
x_names=["M", "N", "K"], # Argument names to use as an x-axis for the plot
|
|
x_vals=[
|
|
128 * i for i in range(2, 33)
|
|
], # Different possible values for `x_name`
|
|
line_arg="provider", # Argument name whose value corresponds to a different line in the plot
|
|
# Possible values for `line_arg`
|
|
# Don't compare to cublas for fp8 cases as torch.matmul doesn't support fp8 at the moment.
|
|
line_vals=(
|
|
["triton"] if fp8_inputs else [ref_lib.lower(), "triton"]
|
|
), # Label name for the lines
|
|
line_names=["Triton"] if fp8_inputs else [ref_lib, "Triton"], # Line styles
|
|
styles=[("green", "-"), ("blue", "-")],
|
|
ylabel="TFLOPS", # Label name for the y-axis
|
|
plot_name="matmul-performance-"
|
|
+ (
|
|
"fp16" if not fp8_inputs else "fp8"
|
|
), # Name for the plot, used also as a file name for saving the plot.
|
|
args={"fp8_inputs": fp8_inputs},
|
|
)
|
|
)
|
|
|
|
|
|
@triton.testing.perf_report(configs)
|
|
def benchmark(M, N, K, provider, fp8_inputs):
|
|
a = torch.randn((M, K), device=DEVICE, dtype=torch.float16)
|
|
b = torch.randn((K, N), device=DEVICE, dtype=torch.float16)
|
|
if TORCH_HAS_FP8 and fp8_inputs:
|
|
a = a.to(torch.float8_e5m2)
|
|
b = b.T
|
|
b = b.to(torch.float8_e5m2)
|
|
quantiles = [0.5, 0.2, 0.8]
|
|
if provider == ref_lib.lower():
|
|
ms, min_ms, max_ms = triton.testing.do_bench(
|
|
lambda: torch.matmul(a, b), quantiles=quantiles
|
|
)
|
|
if provider == "triton":
|
|
ms, min_ms, max_ms = triton.testing.do_bench(
|
|
lambda: matmul(a, b), quantiles=quantiles
|
|
)
|
|
perf = lambda ms: 2 * M * N * K * 1e-12 / (ms * 1e-3)
|
|
return perf(ms), perf(max_ms), perf(min_ms)
|
|
|
|
|
|
benchmark.run(show_plots=True, print_data=True)
|