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alpaca_lora_4bit/triton_utils.py

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import triton
import triton.language as tl
import torch
import custom_autotune
# code based https://github.com/fpgaminer/GPTQ-triton
@custom_autotune.autotune(
configs=[
triton.Config({'BLOCK_SIZE_M': 256, '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': 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),
# These provided a benefit on a 3090
triton.Config({'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_N': 64, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4),
triton.Config({'BLOCK_SIZE_M': 32, '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': 32, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4),
triton.Config({'BLOCK_SIZE_M': 32, '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': 64, 'BLOCK_SIZE_K': 64, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4),
triton.Config({'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_N': 32, 'BLOCK_SIZE_K': 64, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4),
triton.Config({'BLOCK_SIZE_M': 32, 'BLOCK_SIZE_N': 64, 'BLOCK_SIZE_K': 128, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4),
],
key=['M', 'N'],
nearest_power_of_two=True,
)
@triton.jit
def matmul_248_kernel(a_ptr, b_ptr, c_ptr,
scales_ptr, zeros_ptr, g_ptr,
M, N, K, bits, maxq,
stride_am, stride_ak,
stride_bk, stride_bn,
stride_cm, stride_cn,
stride_scales, stride_zeros,
BLOCK_SIZE_M: tl.constexpr, BLOCK_SIZE_N: tl.constexpr, BLOCK_SIZE_K: tl.constexpr,
GROUP_SIZE_M: tl.constexpr):
"""
Compute the matrix multiplication C = A x B.
A is of shape (M, K) float16
B is of shape (K//8, N) int32
C is of shape (M, N) float16
scales is of shape (G, N) float16
zeros is of shape (G, N) float16
g_ptr is of shape (K) int32
"""
infearure_per_bits = 32 // bits
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_k = tl.cdiv(K, BLOCK_SIZE_K)
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 % group_size_m)
pid_n = (pid % num_pid_in_group) // group_size_m
offs_am = pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M)
offs_bn = pid_n * BLOCK_SIZE_N + tl.arange(0, BLOCK_SIZE_N)
offs_k = tl.arange(0, BLOCK_SIZE_K)
a_ptrs = a_ptr + (offs_am[:, None] * stride_am + offs_k[None, :] * stride_ak) # (BLOCK_SIZE_M, BLOCK_SIZE_K)
a_mask = (offs_am[:, None] < M)
# b_ptrs is set up such that it repeats elements along the K axis 8 times
b_ptrs = b_ptr + ((offs_k[:, None] // infearure_per_bits) * stride_bk + offs_bn[None, :] * stride_bn) # (BLOCK_SIZE_K, BLOCK_SIZE_N)
g_ptrs = g_ptr + offs_k
# shifter is used to extract the N bits of each element in the 32-bit word from B
scales_ptrs = scales_ptr + offs_bn[None, :]
zeros_ptrs = zeros_ptr + (offs_bn[None, :] // infearure_per_bits)
shifter = (offs_k % infearure_per_bits) * bits
zeros_shifter = (offs_bn % infearure_per_bits) * bits
accumulator = tl.zeros((BLOCK_SIZE_M, BLOCK_SIZE_N), dtype=tl.float32)
for k in range(0, num_pid_k):
g_idx = tl.load(g_ptrs)
# Fetch scales and zeros; these are per-outfeature and thus reused in the inner loop
scales = tl.load(scales_ptrs + g_idx[:, None] * stride_scales) # (BLOCK_SIZE_K, BLOCK_SIZE_N,)
zeros = tl.load(zeros_ptrs + g_idx[:, None] * stride_zeros) # (BLOCK_SIZE_K, BLOCK_SIZE_N,)
zeros = (zeros >> zeros_shifter[None, :]) & maxq
zeros = (zeros + 1)
a = tl.load(a_ptrs, mask=a_mask, other=0.0) # (BLOCK_SIZE_M, BLOCK_SIZE_K)
b = tl.load(b_ptrs) # (BLOCK_SIZE_K, BLOCK_SIZE_N), but repeated
# Now we need to unpack b (which is N-bit values) into 32-bit values
b = (b >> shifter[:, None]) & maxq # Extract the N-bit values
b = (b - zeros) * scales # Scale and shift
# ! Convert to fp16
b = b.to(tl.float16)
a = a.to(tl.float16)
accumulator += tl.dot(a, b)
a_ptrs += BLOCK_SIZE_K
b_ptrs += (BLOCK_SIZE_K // infearure_per_bits) * stride_bk
g_ptrs += BLOCK_SIZE_K
c = accumulator.to(tl.float16)
c_ptrs = c_ptr + stride_cm * offs_am[:, None] + stride_cn * offs_bn[None, :]
c_mask = (offs_am[:, None] < M) & (offs_bn[None, :] < N)
tl.store(c_ptrs, c, mask=c_mask)
# code based https://github.com/fpgaminer/GPTQ-triton
@custom_autotune.autotune(
configs=[
triton.Config({'BLOCK_SIZE_M': 256, 'BLOCK_SIZE_K': 64, 'BLOCK_SIZE_N': 32, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4),
triton.Config({'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_K': 256, 'BLOCK_SIZE_N': 32, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4),
triton.Config({'BLOCK_SIZE_M': 128, 'BLOCK_SIZE_K': 128, 'BLOCK_SIZE_N': 32, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4),
triton.Config({'BLOCK_SIZE_M': 128, 'BLOCK_SIZE_K': 64, 'BLOCK_SIZE_N': 32, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4),
triton.Config({'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_K': 128, 'BLOCK_SIZE_N': 32, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4),
triton.Config({'BLOCK_SIZE_M': 128, 'BLOCK_SIZE_K': 32, 'BLOCK_SIZE_N': 32, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4),
# These provided a benefit on a 3090
triton.Config({'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_K': 64, 'BLOCK_SIZE_N': 32, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4),
triton.Config({'BLOCK_SIZE_M': 32, 'BLOCK_SIZE_K': 64, 'BLOCK_SIZE_N': 32, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4),
triton.Config({'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_K': 32, 'BLOCK_SIZE_N': 32, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4),
triton.Config({'BLOCK_SIZE_M': 32, 'BLOCK_SIZE_K': 64, 'BLOCK_SIZE_N': 64, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4),
triton.Config({'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_K': 64, 'BLOCK_SIZE_N': 64, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4),
triton.Config({'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_K': 32, 'BLOCK_SIZE_N': 64, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4),
triton.Config({'BLOCK_SIZE_M': 32, 'BLOCK_SIZE_K': 64, 'BLOCK_SIZE_N': 128, 'GROUP_SIZE_M': 8}, num_stages=4, num_warps=4),
],
key=['M', 'K'],
nearest_power_of_two=True,
)
@triton.jit
def trans_matmul_248_kernel(a_ptr, b_ptr, c_ptr,
scales_ptr, zeros_ptr, g_ptr,
M, N, K, bits, maxq,
stride_am, stride_ak,
stride_bk, stride_bn,
stride_cm, stride_cn,
stride_scales, stride_zeros,
BLOCK_SIZE_M: tl.constexpr, BLOCK_SIZE_N: tl.constexpr, BLOCK_SIZE_K: tl.constexpr,
GROUP_SIZE_M: tl.constexpr):
"""
Compute the matrix multiplication C = A x B.
A is of shape (M, N) float16
B is of shape (K//8, N) int32
C is of shape (M, K) float16
scales is of shape (G, N) float16
zeros is of shape (G, N) float16
g_ptr is of shape (K) int32
"""
infearure_per_bits = 32 // bits
pid = tl.program_id(axis=0)
num_pid_m = tl.cdiv(M, BLOCK_SIZE_M)
num_pid_k = tl.cdiv(K, BLOCK_SIZE_K)
num_pid_n = tl.cdiv(N, BLOCK_SIZE_N)
num_pid_in_group = GROUP_SIZE_M * num_pid_k
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 % group_size_m)
pid_k = (pid % num_pid_in_group) // group_size_m
offs_am = pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M)
offs_bk = pid_k * BLOCK_SIZE_K + tl.arange(0, BLOCK_SIZE_K)
offs_n = tl.arange(0, BLOCK_SIZE_N)
a_ptrs = a_ptr + (offs_am[:, None] * stride_am + offs_n[None, :] * stride_ak) # (BLOCK_SIZE_M, BLOCK_SIZE_N)
a_mask = (offs_am[:, None] < M)
# b_ptrs is set up such that it repeats elements along the K axis 8 times
b_ptrs = b_ptr + ((offs_bk[:, None] // infearure_per_bits) * stride_bk + offs_n[None, :] * stride_bn) # (BLOCK_SIZE_K, BLOCK_SIZE_N)
g_ptrs = g_ptr + offs_bk
g_idx = tl.load(g_ptrs)
# shifter is used to extract the N bits of each element in the 32-bit word from B
scales_ptrs = scales_ptr + offs_n[None, :] + g_idx[:, None] * stride_scales
zeros_ptrs = zeros_ptr + (offs_n[None, :] // infearure_per_bits) + g_idx[:, None] * stride_zeros
shifter = (offs_bk % infearure_per_bits) * bits
zeros_shifter = (offs_n % infearure_per_bits) * bits
accumulator = tl.zeros((BLOCK_SIZE_M, BLOCK_SIZE_K), dtype=tl.float32)
for k in range(0, num_pid_n):
# Fetch scales and zeros; these are per-outfeature and thus reused in the inner loop
scales = tl.load(scales_ptrs) # (BLOCK_SIZE_K, BLOCK_SIZE_N,)
zeros = tl.load(zeros_ptrs) # (BLOCK_SIZE_K, BLOCK_SIZE_N,)
zeros = (zeros >> zeros_shifter[None, :]) & maxq
zeros = (zeros + 1)
a = tl.load(a_ptrs, mask=a_mask, other=0.) # (BLOCK_SIZE_M, BLOCK_SIZE_N)
b = tl.load(b_ptrs) # (BLOCK_SIZE_K, BLOCK_SIZE_N), but repeated
# Now we need to unpack b (which is N-bit values) into 32-bit values
b = (b >> shifter[:, None]) & maxq # Extract the N-bit values
b = (b - zeros) * scales # Scale and shift
b = tl.trans(b)
# ! Convert to fp16
b = b.to(tl.float16)
a = a.to(tl.float16)
accumulator += tl.dot(a, b)
a_ptrs += BLOCK_SIZE_N
b_ptrs += BLOCK_SIZE_N
scales_ptrs += BLOCK_SIZE_N
zeros_ptrs += (BLOCK_SIZE_N // infearure_per_bits)
c = accumulator.to(tl.float16)
c_ptrs = c_ptr + stride_cm * offs_am[:, None] + stride_cn * offs_bk[None, :]
c_mask = (offs_am[:, None] < M) & (offs_bk[None, :] < K)
tl.store(c_ptrs, c, mask=c_mask)
def triton_matmul(input, qweight, scales, qzeros, g_idx, bits, maxq):
assert input.shape[-1] == qweight.shape[0] * 32 // bits
outshape = input.shape[:-1] + (qweight.shape[1],)
input = input.reshape(-1, input.shape[-1])
output = torch.empty((input.shape[0], qweight.shape[1]), device=scales.device, dtype=torch.float16)
grid = lambda META: (triton.cdiv(input.shape[0], META['BLOCK_SIZE_M']) * triton.cdiv(qweight.shape[1], META['BLOCK_SIZE_N']),)
matmul_248_kernel[grid](input, qweight, output,
scales, qzeros, g_idx,
input.shape[0], qweight.shape[1], input.shape[1], bits, maxq,
input.stride(0), input.stride(1),
qweight.stride(0), qweight.stride(1),
output.stride(0), output.stride(1),
scales.stride(0), qzeros.stride(0))
output = output.reshape(outshape)
return output
def triton_matmul_transpose(input, qweight, scales, qzeros, g_idx, bits, maxq):
assert input.shape[-1] == qweight.shape[1]
out_dim = qweight.shape[0] * 32 // bits
outshape = input.shape[:-1] + (out_dim,)
input = input.reshape(-1, input.shape[-1])
output_shape_mid = (input.shape[0], out_dim)
output = torch.empty((output_shape_mid[0], output_shape_mid[1]), device=scales.device, dtype=torch.float16)
grid = lambda META: (triton.cdiv(input.shape[0], META['BLOCK_SIZE_M']) * triton.cdiv(output_shape_mid[1], META['BLOCK_SIZE_K']),)
trans_matmul_248_kernel[grid](input, qweight, output,
scales, qzeros, g_idx,
input.shape[0], qweight.shape[1], output_shape_mid[1], bits, maxq,
input.stride(0), input.stride(1),
qweight.stride(0), qweight.stride(1),
output.stride(0), output.stride(1),
scales.stride(0), qzeros.stride(0))
output = output.reshape(outshape)
return output
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