chore: import upstream snapshot with attribution
This commit is contained in:
wehub-resource-sync
@@ -0,0 +1,393 @@
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import numpy as np
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import torch
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import torch.nn as nn
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from torch.cuda.amp import custom_bwd, custom_fwd
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import math
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import triton
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import triton.language as tl
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from models.custom_autotune import *
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def find_layers(module, layers=[nn.Conv2d, nn.Linear], name=''):
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if type(module) in layers:
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return {name: module}
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res = {}
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for name1, child in module.named_children():
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res.update(find_layers(
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child, layers=layers, name=name + '.' + name1 if name != '' else name1
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))
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return res
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# code based https://github.com/fpgaminer/GPTQ-triton
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@autotune(
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configs=[
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triton.Config({'BLOCK_SIZE_M': 256, 'BLOCK_SIZE_N': 64, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8},
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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},
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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},
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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},
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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},
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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},
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num_stages=4, num_warps=4),
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# These provided a benefit on a 3090
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triton.Config({'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_N': 64, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=4,
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num_warps=4),
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triton.Config({'BLOCK_SIZE_M': 32, 'BLOCK_SIZE_N': 64, 'BLOCK_SIZE_K': 32, 'GROUP_SIZE_M': 8}, num_stages=4,
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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=4,
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num_warps=4),
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triton.Config({'BLOCK_SIZE_M': 32, 'BLOCK_SIZE_N': 64, 'BLOCK_SIZE_K': 64, 'GROUP_SIZE_M': 8}, num_stages=4,
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num_warps=4),
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triton.Config({'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_N': 64, 'BLOCK_SIZE_K': 64, 'GROUP_SIZE_M': 8}, num_stages=4,
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num_warps=4),
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triton.Config({'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_N': 32, 'BLOCK_SIZE_K': 64, 'GROUP_SIZE_M': 8}, num_stages=4,
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num_warps=4),
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triton.Config({'BLOCK_SIZE_M': 32, 'BLOCK_SIZE_N': 64, 'BLOCK_SIZE_K': 128, 'GROUP_SIZE_M': 8},
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num_stages=4, num_warps=4),
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],
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key=['M', 'N'],
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nearest_power_of_two=True,
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)
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@triton.jit
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def matmul_248_kernel(a_ptr, b_ptr, c_ptr,
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scales_ptr, zeros_ptr, g_ptr,
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M, N, K, bits, maxq,
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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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stride_scales, stride_zeros,
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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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"""
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Compute the matrix multiplication C = A x B.
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A is of shape (M, K) float16
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B is of shape (K//8, N) int32
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C is of shape (M, N) float16
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scales is of shape (G, N) float16
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zeros is of shape (G, N) float16
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g_ptr is of shape (K) int32
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"""
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infearure_per_bits = 32 // bits
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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_k = tl.cdiv(K, BLOCK_SIZE_K)
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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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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) # (BLOCK_SIZE_M, BLOCK_SIZE_K)
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a_mask = (offs_am[:, None] < M)
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# b_ptrs is set up such that it repeats elements along the K axis 8 times
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b_ptrs = b_ptr + ((offs_k[:, None] // infearure_per_bits) * stride_bk + offs_bn[None,
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:] * stride_bn) # (BLOCK_SIZE_K, BLOCK_SIZE_N)
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g_ptrs = g_ptr + offs_k
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# shifter is used to extract the N bits of each element in the 32-bit word from B
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scales_ptrs = scales_ptr + offs_bn[None, :]
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zeros_ptrs = zeros_ptr + (offs_bn[None, :] // infearure_per_bits)
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shifter = (offs_k % infearure_per_bits) * bits
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zeros_shifter = (offs_bn % infearure_per_bits) * bits
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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, num_pid_k):
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g_idx = tl.load(g_ptrs)
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# Fetch scales and zeros; these are per-outfeature and thus reused in the inner loop
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scales = tl.load(scales_ptrs + g_idx[:, None] * stride_scales) # (BLOCK_SIZE_K, BLOCK_SIZE_N,)
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zeros = tl.load(zeros_ptrs + g_idx[:, None] * stride_zeros) # (BLOCK_SIZE_K, BLOCK_SIZE_N,)
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zeros = (zeros >> zeros_shifter[None, :]) & maxq
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zeros = (zeros + 1)
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a = tl.load(a_ptrs, mask=a_mask, other=0.) # (BLOCK_SIZE_M, BLOCK_SIZE_K)
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b = tl.load(b_ptrs) # (BLOCK_SIZE_K, BLOCK_SIZE_N), but repeated
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# Now we need to unpack b (which is N-bit values) into 32-bit values
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b = (b >> shifter[:, None]) & maxq # Extract the N-bit values
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b = (b - zeros) * scales # Scale and shift
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accumulator += tl.dot(a, b)
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a_ptrs += BLOCK_SIZE_K
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b_ptrs += (BLOCK_SIZE_K // infearure_per_bits) * stride_bk
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g_ptrs += BLOCK_SIZE_K
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c = accumulator.to(tl.float16)
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c_ptrs = c_ptr + stride_cm * offs_am[:, None] + stride_cn * offs_bn[None, :]
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c_mask = (offs_am[:, None] < M) & (offs_bn[None, :] < N)
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tl.store(c_ptrs, accumulator, mask=c_mask)
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# code based https://github.com/fpgaminer/GPTQ-triton
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@autotune(
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configs=[
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triton.Config({'BLOCK_SIZE_M': 256, 'BLOCK_SIZE_K': 64, 'BLOCK_SIZE_N': 32, 'GROUP_SIZE_M': 8},
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num_stages=4, num_warps=4),
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triton.Config({'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_K': 256, 'BLOCK_SIZE_N': 32, 'GROUP_SIZE_M': 8},
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num_stages=4, num_warps=4),
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triton.Config({'BLOCK_SIZE_M': 128, 'BLOCK_SIZE_K': 128, 'BLOCK_SIZE_N': 32, 'GROUP_SIZE_M': 8},
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num_stages=4, num_warps=4),
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triton.Config({'BLOCK_SIZE_M': 128, 'BLOCK_SIZE_K': 64, 'BLOCK_SIZE_N': 32, 'GROUP_SIZE_M': 8},
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num_stages=4, num_warps=4),
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triton.Config({'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_K': 128, 'BLOCK_SIZE_N': 32, 'GROUP_SIZE_M': 8},
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num_stages=4, num_warps=4),
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triton.Config({'BLOCK_SIZE_M': 128, 'BLOCK_SIZE_K': 32, 'BLOCK_SIZE_N': 32, 'GROUP_SIZE_M': 8},
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num_stages=4, num_warps=4),
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# These provided a benefit on a 3090
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triton.Config({'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_K': 64, 'BLOCK_SIZE_N': 32, 'GROUP_SIZE_M': 8}, num_stages=4,
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num_warps=4),
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triton.Config({'BLOCK_SIZE_M': 32, 'BLOCK_SIZE_K': 64, 'BLOCK_SIZE_N': 32, 'GROUP_SIZE_M': 8}, num_stages=4,
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num_warps=4),
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triton.Config({'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_K': 32, 'BLOCK_SIZE_N': 32, 'GROUP_SIZE_M': 8}, num_stages=4,
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num_warps=4),
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triton.Config({'BLOCK_SIZE_M': 32, 'BLOCK_SIZE_K': 64, 'BLOCK_SIZE_N': 64, 'GROUP_SIZE_M': 8}, num_stages=4,
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num_warps=4),
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triton.Config({'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_K': 64, 'BLOCK_SIZE_N': 64, 'GROUP_SIZE_M': 8}, num_stages=4,
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num_warps=4),
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triton.Config({'BLOCK_SIZE_M': 64, 'BLOCK_SIZE_K': 32, 'BLOCK_SIZE_N': 64, 'GROUP_SIZE_M': 8}, num_stages=4,
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num_warps=4),
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triton.Config({'BLOCK_SIZE_M': 32, 'BLOCK_SIZE_K': 64, 'BLOCK_SIZE_N': 128, 'GROUP_SIZE_M': 8},
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num_stages=4, num_warps=4),
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],
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key=['M', 'K'],
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nearest_power_of_two=True,
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)
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@triton.jit
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def trans_matmul_248_kernel(a_ptr, b_ptr, c_ptr,
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scales_ptr, zeros_ptr, g_ptr,
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M, N, K, bits, maxq,
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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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stride_scales, stride_zeros,
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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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"""
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Compute the matrix multiplication C = A x B.
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A is of shape (M, N) float16
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B is of shape (K//8, N) int32
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C is of shape (M, K) float16
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scales is of shape (G, N) float16
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zeros is of shape (G, N) float16
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g_ptr is of shape (K) int32
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"""
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infearure_per_bits = 32 // bits
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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_k = tl.cdiv(K, BLOCK_SIZE_K)
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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_k
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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_k = (pid % num_pid_in_group) // group_size_m
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offs_am = pid_m * BLOCK_SIZE_M + tl.arange(0, BLOCK_SIZE_M)
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offs_bk = pid_k * BLOCK_SIZE_K + tl.arange(0, BLOCK_SIZE_K)
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offs_n = tl.arange(0, BLOCK_SIZE_N)
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a_ptrs = a_ptr + (offs_am[:, None] * stride_am + offs_n[None, :] * stride_ak) # (BLOCK_SIZE_M, BLOCK_SIZE_N)
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a_mask = (offs_am[:, None] < M)
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# b_ptrs is set up such that it repeats elements along the K axis 8 times
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b_ptrs = b_ptr + ((offs_bk[:, None] // infearure_per_bits) * stride_bk + offs_n[None,
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:] * stride_bn) # (BLOCK_SIZE_K, BLOCK_SIZE_N)
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g_ptrs = g_ptr + offs_bk
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g_idx = tl.load(g_ptrs)
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# shifter is used to extract the N bits of each element in the 32-bit word from B
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scales_ptrs = scales_ptr + offs_n[None, :] + g_idx[:, None] * stride_scales
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zeros_ptrs = zeros_ptr + (offs_n[None, :] // infearure_per_bits) + g_idx[:, None] * stride_zeros
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shifter = (offs_bk % infearure_per_bits) * bits
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zeros_shifter = (offs_n % infearure_per_bits) * bits
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accumulator = tl.zeros((BLOCK_SIZE_M, BLOCK_SIZE_K), dtype=tl.float32)
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for k in range(0, num_pid_n):
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# Fetch scales and zeros; these are per-outfeature and thus reused in the inner loop
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scales = tl.load(scales_ptrs) # (BLOCK_SIZE_K, BLOCK_SIZE_N,)
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zeros = tl.load(zeros_ptrs) # (BLOCK_SIZE_K, BLOCK_SIZE_N,)
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zeros = (zeros >> zeros_shifter[None, :]) & maxq
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zeros = (zeros + 1)
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a = tl.load(a_ptrs, mask=a_mask, other=0.) # (BLOCK_SIZE_M, BLOCK_SIZE_N)
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b = tl.load(b_ptrs) # (BLOCK_SIZE_K, BLOCK_SIZE_N), but repeated
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# Now we need to unpack b (which is N-bit values) into 32-bit values
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b = (b >> shifter[:, None]) & maxq # Extract the N-bit values
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b = (b - zeros) * scales # Scale and shift
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b = tl.trans(b)
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accumulator += tl.dot(a, b)
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a_ptrs += BLOCK_SIZE_N
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b_ptrs += BLOCK_SIZE_N
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scales_ptrs += BLOCK_SIZE_N
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zeros_ptrs += (BLOCK_SIZE_N // infearure_per_bits)
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c = accumulator.to(tl.float16)
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c_ptrs = c_ptr + stride_cm * offs_am[:, None] + stride_cn * offs_bk[None, :]
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c_mask = (offs_am[:, None] < M) & (offs_bk[None, :] < K)
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tl.store(c_ptrs, accumulator, mask=c_mask)
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def matmul248(input, qweight, scales, qzeros, g_idx, bits, maxq):
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output = torch.empty((input.shape[0], qweight.shape[1]), device='cuda', dtype=torch.float16)
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grid = lambda META: (
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triton.cdiv(input.shape[0], META['BLOCK_SIZE_M']) * triton.cdiv(qweight.shape[1], META['BLOCK_SIZE_N']),)
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matmul_248_kernel[grid](input, qweight, output,
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scales, qzeros, g_idx,
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input.shape[0], qweight.shape[1], input.shape[1], bits, maxq,
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input.stride(0), input.stride(1),
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qweight.stride(0), qweight.stride(1),
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output.stride(0), output.stride(1),
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scales.stride(0), qzeros.stride(0))
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return output
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def transpose_matmul248(input, qweight, scales, qzeros, g_idx, bits, maxq):
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output_dim = (qweight.shape[0] * 32) // bits
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output = torch.empty((input.shape[0], output_dim), device='cuda', dtype=torch.float16)
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grid = lambda META: (
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triton.cdiv(input.shape[0], META['BLOCK_SIZE_M']) * triton.cdiv(output_dim, META['BLOCK_SIZE_K']),)
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transpose_matmul_248_kernel[grid](input, qweight, output,
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scales, qzeros, g_idx,
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input.shape[0], qweight.shape[1], output_dim, bits, maxq,
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input.stride(0), input.stride(1),
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qweight.stride(0), qweight.stride(1),
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output.stride(0), output.stride(1),
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scales.stride(0), qzeros.stride(0))
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return output
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class QuantLinearFunction(torch.autograd.Function):
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@staticmethod
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@custom_fwd(cast_inputs=torch.float16)
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def forward(ctx, input, qweight, scales, qzeros, g_idx, bits, maxq):
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output = matmul248(input, qweight, scales, qzeros, g_idx, bits, maxq)
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ctx.save_for_backward(qweight, scales, qzeros, g_idx)
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ctx.bits, ctx.maxq = bits, maxq
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return output
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@staticmethod
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@custom_bwd
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def backward(ctx, grad_output):
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qweight, scales, qzeros, g_idx = ctx.saved_tensors
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bits, maxq = ctx.bits, ctx.maxq
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grad_input = None
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if ctx.needs_input_grad[0]:
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grad_input = transpose_matmul248(grad_output, qweight, scales, qzeros, g_idx, bits, maxq)
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return grad_input, None, None, None, None, None, None
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class QuantLinear(nn.Module):
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def __init__(self, bits, groupsize, infeatures, outfeatures, bias):
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super().__init__()
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if bits not in [2, 4, 8]:
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raise NotImplementedError("Only 2,4,8 bits are supported.")
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self.infeatures = infeatures
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self.outfeatures = outfeatures
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self.bits = bits
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self.maxq = 2 ** self.bits - 1
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self.groupsize = groupsize if groupsize != -1 else infeatures
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self.register_buffer('qweight', torch.zeros((infeatures // 32 * self.bits, outfeatures), dtype=torch.int32))
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self.register_buffer('qzeros', torch.zeros((math.ceil(infeatures / self.groupsize), outfeatures // 32 * self.bits), dtype=torch.int32))
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self.register_buffer('scales', torch.zeros((math.ceil(infeatures / self.groupsize), outfeatures), dtype=torch.float16))
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self.register_buffer('g_idx', torch.tensor([i // self.groupsize for i in range(infeatures)], dtype=torch.int32))
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if bias:
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self.register_buffer('bias', torch.zeros((outfeatures), dtype=torch.float16))
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else:
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self.bias = None
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def pack(self, linear, scales, zeros, g_idx=None):
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self.g_idx = g_idx.clone() if g_idx is not None else self.g_idx
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scales = scales.t().contiguous()
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zeros = zeros.t().contiguous()
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scale_zeros = zeros * scales
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self.scales = scales.clone().half()
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if linear.bias is not None:
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self.bias = linear.bias.clone().half()
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intweight = []
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for idx in range(self.infeatures):
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intweight.append(torch.round(
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(linear.weight.data[:, idx] + scale_zeros[self.g_idx[idx]]) / self.scales[self.g_idx[idx]]).to(
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torch.int)[:, None])
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||||
intweight = torch.cat(intweight, dim=1)
|
||||
intweight = intweight.t().contiguous()
|
||||
intweight = intweight.numpy().astype(np.uint32)
|
||||
qweight = np.zeros((intweight.shape[0] // 32 * self.bits, intweight.shape[1]), dtype=np.uint32)
|
||||
i = 0
|
||||
row = 0
|
||||
while row < qweight.shape[0]:
|
||||
if self.bits in [2, 4, 8]:
|
||||
for j in range(i, i + (32 // self.bits)):
|
||||
qweight[row] |= intweight[j] << (self.bits * (j - i))
|
||||
i += 32 // self.bits
|
||||
row += 1
|
||||
else:
|
||||
raise NotImplementedError("Only 2,4,8 bits are supported.")
|
||||
|
||||
qweight = qweight.astype(np.int32)
|
||||
self.qweight = torch.from_numpy(qweight)
|
||||
|
||||
zeros -= 1
|
||||
zeros = zeros.numpy().astype(np.uint32)
|
||||
qzeros = np.zeros((zeros.shape[0], zeros.shape[1] // 32 * self.bits), dtype=np.uint32)
|
||||
i = 0
|
||||
col = 0
|
||||
while col < qzeros.shape[1]:
|
||||
if self.bits in [2, 4, 8]:
|
||||
for j in range(i, i + (32 // self.bits)):
|
||||
qzeros[:, col] |= zeros[:, j] << (self.bits * (j - i))
|
||||
i += 32 // self.bits
|
||||
col += 1
|
||||
else:
|
||||
raise NotImplementedError("Only 2,4,8 bits are supported.")
|
||||
|
||||
qzeros = qzeros.astype(np.int32)
|
||||
self.qzeros = torch.from_numpy(qzeros)
|
||||
|
||||
def forward(self, x):
|
||||
out_shape = x.shape[:-1] + (self.outfeatures,)
|
||||
out = QuantLinearFunction.apply(x.reshape(-1, x.shape[-1]), self.qweight, self.scales,
|
||||
self.qzeros, self.g_idx, self.bits, self.maxq)
|
||||
out = out + self.bias if self.bias is not None else out
|
||||
return out.reshape(out_shape)
|
||||
|
||||
def make_quant(module, names, bits, groupsize, name=''):
|
||||
if isinstance(module, QuantLinear):
|
||||
return
|
||||
for attr in dir(module):
|
||||
tmp = getattr(module, attr)
|
||||
name1 = name + '.' + attr if name != '' else attr
|
||||
if name1 in names:
|
||||
delattr(module, attr)
|
||||
setattr(module, attr, QuantLinear(bits, groupsize, tmp.in_features, tmp.out_features, tmp.bias is not None))
|
||||
for name1, child in module.named_children():
|
||||
make_quant(child, names, bits, groupsize, name + '.' + name1 if name != '' else name1)
|
||||
|
||||
|
||||
def quantize_with_gptq(model, wbits, groupsize):
|
||||
model = model.eval()
|
||||
layers = find_layers(model)
|
||||
for name in ['lm_head']:
|
||||
if name in layers:
|
||||
del layers[name]
|
||||
make_quant(model, layers, wbits, groupsize)
|
||||
# model.load_state_dict(torch.load(checkpoint))
|
||||
return model
|
||||
Reference in New Issue
Block a user