forked from mrq/bitsandbytes-rocm
1876 lines
65 KiB
Python
1876 lines
65 KiB
Python
import pytest
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import math
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import random
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import time
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import torch
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import bitsandbytes as bnb
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import einops
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from itertools import product
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from bitsandbytes import functional as F
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torch.set_printoptions(precision=4, sci_mode=False, linewidth=120, edgeitems=20, threshold=10000)
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k = 20
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def assert_all_approx_close(a, b, rtol, atol, count):
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idx = torch.isclose(a, b, rtol, atol)
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sumval = (idx==0).sum().item()
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if sumval > count:
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print(f'Too many values not close: assert {sumval} < {count}')
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torch.testing.assert_allclose(a, b, rtol, atol)
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class FFN(torch.nn.Module):
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def __init__(self, input_features, hidden_size, bias=True):
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super(FFN, self).__init__()
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self.fc1 = torch.nn.Linear(input_features, hidden_size, bias=bias)
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self.fc2 = torch.nn.Linear(hidden_size, input_features, bias=bias)
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with torch.no_grad():
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torch.nn.init.xavier_uniform_(self.fc1.weight)
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torch.nn.init.xavier_uniform_(self.fc2.weight)
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def forward(self, x):
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x = torch.relu(self.fc1(x))
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x = self.fc2(x)
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return x
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class Timer(object):
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def __init__(self):
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self.starts = {}
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self.ends = {}
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self.agg = {}
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def tick(self, name='default'):
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if name not in self.starts:
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self.starts[name] = torch.cuda.Event(enable_timing=True)
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self.ends[name] = torch.cuda.Event(enable_timing=True)
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self.starts[name].record()
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else:
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ms = self.tock(name, evict=True, print_ms=False)
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def tock(self, name='default', evict=True, print_ms=True):
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if name in self.ends:
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self.ends[name].record()
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torch.cuda.synchronize()
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ms = self.starts[name].elapsed_time(self.ends[name])
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if name not in self.agg: self.agg[name] = 0.0
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self.agg[name] += ms
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if evict:
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self.starts.pop(name)
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self.ends.pop(name)
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if print_ms and name in self.agg:
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print('{0} took: {1:.5f}s'.format(name, self.agg[name]/1000.0))
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return self.agg[name]
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def reset(self):
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self.starts = {}
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self.ends = {}
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self.agg = {}
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print('Resetting benchmark data')
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def setup():
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pass
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def teardown():
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pass
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@pytest.mark.parametrize("dtype", [torch.float32, torch.float16], ids=['float', 'half'])
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def test_estimate_quantiles(dtype):
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A = torch.rand(1024, 1024, device='cuda')
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A = A.to(dtype)
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code = F.estimate_quantiles(A)
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percs = torch.linspace(1/512, 511/512, 256, device=A.device)
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torch.testing.assert_allclose(percs, code, atol=1e-3, rtol=1e-2)
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A = torch.randn(1024, 1024, device='cuda')
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A = A.to(dtype)
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code = F.estimate_quantiles(A)
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quantiles = torch.quantile(A.float(), percs)
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diff = torch.abs(code-quantiles)
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assert (diff > 5e-02).sum().item() == 0
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def test_quantile_quantization():
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for i in range(100):
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A1 = torch.randn(1024, 1024, device='cuda')
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code = F.estimate_quantiles(A1)
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C = F.quantize_no_absmax(A1, code)
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A2 = F.dequantize_no_absmax(C, code)
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diff = torch.abs(A1-A2).mean().item()
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assert diff < 0.0075
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A1 = torch.rand(1024, 1024, device='cuda')
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code = F.estimate_quantiles(A1)
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C = F.quantize_no_absmax(A1, code)
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A2 = F.dequantize_no_absmax(C, code)
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diff = torch.abs(A1-A2).mean().item()
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torch.testing.assert_allclose(A1, A2, atol=5e-3, rtol=0)
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assert diff < 0.001
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def test_dynamic_quantization():
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diffs = []
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reldiffs = []
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for i in range(100):
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A1 = torch.randn(1024, 1024, device='cuda')
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C, S = F.quantize(A1)
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A2 = F.dequantize(C, S)
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diff = torch.abs(A1-A2)
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reldiff = diff/torch.abs(A1+1e-8)
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diffs.append(diff.mean().item())
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reldiffs.append(reldiff.mean().item())
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assert diff.mean().item() < 0.0135
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#print(sum(diffs)/len(diffs))
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#print(sum(reldiffs)/len(reldiffs))
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for i in range(100):
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A1 = torch.rand(1024, 1024, device='cuda')
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C, S = F.quantize(A1)
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A2 = F.dequantize(C, S)
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diff = torch.abs(A1-A2).mean().item()
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torch.testing.assert_allclose(A1, A2, atol=1e-2, rtol=0)
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assert diff < 0.004
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def test_dynamic_blockwise_quantization():
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diffs = []
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reldiffs = []
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for i in range(100):
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A1 = torch.randn(1024, 1024, device='cuda')
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C, S = F.quantize_blockwise(A1)
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A2 = F.dequantize_blockwise(C, S)
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diff = torch.abs(A1-A2)
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reldiff = diff/torch.abs(A1+1e-8)
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diffs.append(diff.mean().item())
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reldiffs.append(reldiff.mean().item())
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assert diffs[-1] < 0.011
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#print(sum(diffs)/len(diffs))
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#print(sum(reldiffs)/len(reldiffs))
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diffs = []
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for i in range(100):
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A1 = torch.rand(1024, 1024, device='cuda')
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C, S = F.quantize_blockwise(A1)
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A2 = F.dequantize_blockwise(C, S)
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diff = torch.abs(A1-A2).mean().item()
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assert diff < 0.0033
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diffs.append(diff)
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torch.testing.assert_allclose(A1, A2, atol=1e-2, rtol=0)
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#print(sum(diffs)/len(diffs))
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def test_dynamic_blockwise_stochastic_quantization():
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diffs = []
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reldiffs = []
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rand = torch.rand(1024).cuda()
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for i in range(100):
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A1 = torch.randn(1024, 1024, device='cuda')
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C1, S1 = F.quantize_blockwise(A1, rand=rand)
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C2, S2 = F.quantize_blockwise(A1)
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# a maximunm distance of quantized values of 1
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torch.testing.assert_allclose(C1, C2, atol=1, rtol=0)
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fraction_smaller = (C1<C2).float().sum()/C1.numel()
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fraction_larger = (C1>C2).float().sum()/C1.numel()
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torch.testing.assert_allclose(fraction_larger, fraction_smaller, atol=0.01, rtol=0)
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@pytest.mark.parametrize("gtype", [torch.float32, torch.float16], ids=['float', 'half'])
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def test_percentile_clipping(gtype):
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gnorm_vec1 = torch.zeros(100, device='cuda')
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gnorm_vec2 = torch.zeros(100, device='cuda')
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n = 4
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step = 0
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percentile=5
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for i in range(k):
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step += 1
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g = torch.randn(n, n, dtype=gtype, device='cuda')
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gnorm1, clip2, gnorm_scale = F.percentile_clipping(g, gnorm_vec2, step, percentile=percentile)
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assert gnorm_scale == 1.0 if gnorm1 < clip2 else clip2/gnorm1
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gnorm2 = torch.norm(g.float())
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if step == 1:
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gnorm_vec1[:] = gnorm2
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else:
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gnorm_vec1[step % 100] = gnorm2
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vals, idx = torch.sort(gnorm_vec1)
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clip1 = vals[percentile]
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torch.testing.assert_allclose(gnorm_vec1, torch.sqrt(gnorm_vec2))
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torch.testing.assert_allclose(clip1, clip2)
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torch.testing.assert_allclose(gnorm1, gnorm2)
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def quant(x):
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max1 = torch.abs(x).max()
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x = torch.round(x/max1*127)
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return max1, x.to(torch.int8)
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def dequant(c, maxC):
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return c.float()*(maxC/127)
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def mm_dequant(maxA, maxB, C):
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return C.float()*(maxA/127)*(maxB/127)
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def quant_multi(x, dim):
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max1 = torch.amax(torch.abs(x), dim=dim, keepdim=True)
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max1[max1==0] = 1.0
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x = torch.round(x/max1*127)
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return max1, x.to(torch.int8)
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def quant_multi_chunk(x, dim, chunk_size=32):
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if dim==1:
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x_chunked = einops.rearrange(x, '(c a) b -> c a b', c=chunk_size)
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max1 = torch.amax(torch.abs(x_chunked), dim=dim+1, keepdim=True)
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max1 = torch.tile(max1, (1, 1, x.shape[1]))
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max1 = max1.view(x.shape)
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elif dim==0:
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x_chunked = einops.rearrange(x, 'a (b c) -> a b c', c=chunk_size)
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max1 = torch.amax(torch.abs(x_chunked), dim=dim, keepdim=True)
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max1 = torch.tile(max1, (x.shape[0], 1, 1))
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max1 = max1.view(x.shape)
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max1[max1==0] = 1.0
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x = torch.round(x/max1*127)
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return max1, x.to(torch.int8)
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def quant_minmax(A):
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minA = A.min()
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maxA = A.max()
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def mean(xx):
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return sum(xx)/float(len(xx))
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#dim1 = torch.randint(1,1024*4, size=(4,)).tolist()
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#dim2 = torch.randint(1,1024*4, size=(4,)).tolist()
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dim1 = [1024*2]
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dim2 = [1024*16]
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methods = [(lambda x, dim: quant(x), lambda x, dim: quant(x), dequant, dequant, mm_dequant)]
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methods.append((quant_multi, quant_multi, dequant, dequant, mm_dequant))
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#methods.append((lambda x: quant_multi_chunk(x, dim=-1), lambda x: quant_multi_chunk(x, dim=0), dequant, dequant, mm_dequant))
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method_names = ['linear', 'vectorwise']
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batched = [False, True]
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values = list(product(dim1,dim2, methods, batched))
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values_names = list(product(dim1,dim2, method_names, batched))
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names = ['dim1_{0}_dim2_{1}_quant_{2}_batched_{3}'.format(*vals) for vals in values_names]
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@pytest.mark.parametrize("dim1, dim2, quant_methods, batched", values, ids=names)
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def test_approx_igemm(dim1, dim2, quant_methods, batched):
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dim1 = dim1 - (dim1 % 32)
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dim2 = dim2 - (dim2 % 32)
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errors = []
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relerrors = []
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print('')
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for i in range(5):
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if batched:
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A = torch.normal(0, 0.5, size=(32, dim1, dim2//32), device='cuda')
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B = torch.normal(0, 0.5, size=(32, dim2//32, dim1), device='cuda')
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maxA, Ac = quant_methods[0](A, 2)
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maxB, Bc = quant_methods[1](B, 1)
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else:
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A = torch.normal(0, 0.5, size=(dim1, dim2), device='cuda')
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B = torch.normal(0, 0.5, size=(dim2, dim1), device='cuda')
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maxA, Ac = quant_methods[0](A, 1)
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maxB, Bc = quant_methods[1](B, 0)
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torch.testing.assert_allclose(quant_methods[2](maxA, Ac), A, atol=0.025, rtol=0.05)
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if batched:
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out2 = torch.bmm(A, B)
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C = torch.bmm(Ac.float(), Bc.float())
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else:
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out2 = torch.mm(A, B)
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C = F.igemm(Ac, Bc)
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out = quant_methods[4](maxA, maxB, C)
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std = out2.std()
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out/= std
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out2/= std
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err = torch.abs(out-out2)
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relerr = err/torch.abs(out2)
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errors.append(err.mean().item())
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relerrors.append(relerr.mean().item())
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print(mean(errors))
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print(mean(relerrors))
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def test_stable_embedding():
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layer = bnb.nn.StableEmbedding(1024, 1024)
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layer.reset_parameters()
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n = 2
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hidden_dim = torch.randint(32,256, size=(n,)).tolist()
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batch_dim = torch.randint(16,256, size=(n,)).tolist()
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seq_dim = torch.randint(16,256, size=(n,)).tolist()
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transpose = [(False, False), (False, True), (True, False), (True, True)]
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values = list(product(hidden_dim,batch_dim, transpose, seq_dim))
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names = ['hidden_dim_{0}_batch_dim_{1},transpose_{2}_seq_dim_{3}'.format(*vals) for vals in values]
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@pytest.mark.parametrize("hidden_dim, batch_dim, transpose, seq_dim", values, ids=names)
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def test_igemm(hidden_dim, batch_dim, transpose, seq_dim):
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hidden_dim = hidden_dim - (hidden_dim % 32)
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batch_dim = batch_dim - (batch_dim % 16)
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seq_dim = seq_dim - (seq_dim % 16)
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for i in range(k):
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shapeA = (batch_dim, hidden_dim) if not transpose[0] else (hidden_dim, batch_dim)
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shapeB = ((32*random.randint(1, 4), hidden_dim) if transpose[1] else (hidden_dim, 32*random.randint(1, 4)))
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A = torch.randint(-128, 127, size=shapeA, device='cuda').to(torch.int8)
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B = torch.randint(-128, 127, size=shapeB, device='cuda').to(torch.int8)
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if not transpose[0] and not transpose[1]:
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out2 = torch.matmul(A.float(), B.float())
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out = F.igemm(A, B)
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elif not transpose[0] and transpose[1]:
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out2 = torch.matmul(A.float(), B.t().float())
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out = F.igemm(A, B.t())
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elif transpose[0] and not transpose[1]:
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out2 = torch.matmul(A.t().float(), B.float())
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out = F.igemm(A.t(), B)
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elif transpose[0] and transpose[1]:
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out2 = torch.matmul(A.t().float(), B.t().float())
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out = F.igemm(A.t(), B.t())
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torch.testing.assert_allclose(out.float(), out2)
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for i in range(k):
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shapeA = (batch_dim, seq_dim, hidden_dim)
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shapeB = ((32*random.randint(1, 4), hidden_dim) if transpose[1] else (hidden_dim, 32*random.randint(1, 4)))
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A = torch.randint(-128, 127, size=shapeA, device='cuda').to(torch.int8)
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B = torch.randint(-128, 127, size=shapeB, device='cuda').to(torch.int8)
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if not transpose[0] and not transpose[1]:
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out2 = torch.matmul(A.float(), B.float())
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out = F.igemm(A, B)
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elif not transpose[0] and transpose[1]:
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out2 = torch.matmul(A.float(), B.t().float())
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out = F.igemm(A, B.t())
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torch.testing.assert_allclose(out.float(), out2)
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n = 3
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seq_dim = torch.randint(32,512, size=(n,)).tolist()
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hidden_dim = torch.randint(32,1024*4, size=(n,)).tolist()
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batch_dim = torch.randint(2,16, size=(n,)).tolist()
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values = list(product(seq_dim,hidden_dim,batch_dim))
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names = ['seq_dim{0}_hidden_dim{1}_batch_dim{2}'.format(*vals) for vals in values]
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@pytest.mark.parametrize("seq_dim, hidden_dim, batch_dim", values, ids=names)
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def test_dim3_igemm(seq_dim, hidden_dim, batch_dim):
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seq_dim = seq_dim - (seq_dim % 32)
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hidden_dim = hidden_dim - (hidden_dim % 32)
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batch_dim = batch_dim - (batch_dim % 2)
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for i in range(25):
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A = torch.randint(-128, 127, size=(batch_dim, seq_dim, hidden_dim), device='cuda').to(torch.int8)
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B = torch.randint(-128, 127, size=(batch_dim, seq_dim, 1024), device='cuda').to(torch.int8)
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out2 = torch.einsum('bsi, bso->io', A.float(), B.float())
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iout = torch.empty(A.shape[2], B.shape[2], dtype=torch.int32, device=A.device)
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out = F.igemm(A, B, out=iout)
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torch.testing.assert_allclose(out.float(), out2)
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n = 2
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seq_dim = torch.randint(32,512, size=(n,)).tolist()
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hidden_dim = torch.randint(32,1024*4, size=(n,)).tolist()
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batch_dim = torch.randint(2,16, size=(n,)).tolist()
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transpose = [False, True]
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values = list(product(seq_dim,hidden_dim,batch_dim, transpose))
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names = ['seq_dim={0}_hidden_dim={1}_batch_dim={2}_transpose{3}'.format(*vals) for vals in values]
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@pytest.mark.parametrize("seq_dim, hidden_dim, batch_dim, transpose", values, ids=names)
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def test_minmax_igemm(seq_dim, hidden_dim, batch_dim, transpose):
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def min_max(x):
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maxA = torch.amax(x, dim=2, keepdim=True)
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minA = torch.amin(x, dim=2, keepdim=True)
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scale = (maxA-minA)/2.0
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return (127*(x-minA-scale)/scale).to(torch.int8), minA, scale
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seq_dim = seq_dim - (seq_dim % 16)
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hidden_dim = hidden_dim - (hidden_dim % 16)
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batch_dim = batch_dim - (batch_dim % 2)
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errs = []
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relerrs = []
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errs2 = []
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relerrs2 = []
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for i in range(k):
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A = torch.normal(0.0, 0.5, size=(batch_dim, seq_dim, hidden_dim), device='cuda')
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if transpose:
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B = torch.normal(0, 0.5, size=(256, hidden_dim), device='cuda')
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else:
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B = torch.normal(0, 0.5, size=(hidden_dim, 256), device='cuda')
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Ac, minA, scale = min_max(A)
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if transpose:
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maxB, Bc = quant_multi(B, dim=(1 if transpose else 0))
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out = F.igemm(Ac, Bc.t())
|
|
out2 = torch.matmul(A,B.t())
|
|
offset = B.t().sum(0)*(minA+scale)
|
|
out = out.float()
|
|
out = (out*maxB.t()*scale/(127*127))+offset
|
|
|
|
maxA, Ac = quant_multi(A, dim=2)
|
|
out3 = F.igemm(Ac, Bc.t())
|
|
out3 = mm_dequant(maxA, maxB.t(), out3)
|
|
else:
|
|
maxB, Bc = quant_multi(B, dim=0)
|
|
offset = B.sum(0)*(minA+scale)
|
|
out = F.igemm(Ac, Bc)
|
|
out2 = torch.matmul(A,B)
|
|
out = out.float()
|
|
out = (out*maxB*scale/(127*127))+offset
|
|
|
|
maxA, Ac = quant_multi(A, dim=2)
|
|
out3 = F.igemm(Ac, Bc)
|
|
out3 = mm_dequant(maxA, maxB, out3)
|
|
|
|
std = out2.std()
|
|
out2 /= std
|
|
out /= std
|
|
out3 /= std
|
|
|
|
err = torch.abs(out-out2)
|
|
relerr = err/(torch.abs(out2)+1e-7)
|
|
|
|
err2 = torch.abs(out3-out2)
|
|
relerr2 = err2/(torch.abs(out2)+1e-7)
|
|
|
|
errs.append(err.mean().item())
|
|
relerrs.append(relerr.mean().item())
|
|
errs2.append(err2.mean().item())
|
|
relerrs2.append(relerr2.mean().item())
|
|
#print(mean(errs))
|
|
#print(mean(relerrs))
|
|
#print(mean(errs2))
|
|
#print(mean(relerrs2))
|
|
assert mean(errs) < 0.015
|
|
assert mean(relerrs) < 0.3
|
|
|
|
n = 2
|
|
dim1 = torch.randint(1,64, size=(n,)).tolist()
|
|
dim2 = torch.randint(32,128, size=(n,)).tolist()
|
|
dim3 = torch.randint(32,256, size=(n,)).tolist()
|
|
dim4 = torch.randint(32,256, size=(n,)).tolist()
|
|
transpose = [(False, False), (True, False), (False, True), (True, True)]
|
|
values = list(product(dim1,dim2,dim3,dim4,transpose))
|
|
names = ['dim1_{0}_dim2_{1}_dim3_{2}_dim4_{3}_transpose_{4}'.format(*vals) for vals in values]
|
|
@pytest.mark.parametrize("dim1, dim2, dim3, dim4, transpose", values, ids=names)
|
|
def test_ibmm(dim1, dim2, dim3, dim4, transpose):
|
|
dim2 = dim2 - (dim2 % 16)
|
|
dim3 = dim3 - (dim3 % 16)
|
|
dim4 = dim4 - (dim4 % 16)
|
|
for i in range(k):
|
|
shapeA = (dim1, dim3, dim2) if transpose[0] else (dim1, dim2, dim3)
|
|
shapeB = (dim1, dim4, dim3) if transpose[1] else (dim1, dim3, dim4)
|
|
A = torch.randint(-128, 127, size=shapeA, device='cuda').to(torch.int8)
|
|
B = torch.randint(-128, 127, size=shapeB, device='cuda').to(torch.int8)
|
|
|
|
if not transpose[0] and not transpose[1]:
|
|
out2 = torch.bmm(A.float(), B.float())
|
|
out = F.igemm(A, B)
|
|
elif not transpose[0] and transpose[1]:
|
|
out2 = torch.bmm(A.float(), B.permute([0, 2, 1]).float())
|
|
out = F.igemm(A, B.permute([0, 2, 1]))
|
|
elif transpose[0] and not transpose[1]:
|
|
out2 = torch.bmm(A.permute([0, 2, 1]).float(), B.float())
|
|
out = F.igemm(A.permute([0, 2, 1]), B)
|
|
elif transpose[0] and transpose[1]:
|
|
out2 = torch.bmm(A.permute([0, 2, 1]).float(), B.permute([0, 2, 1]).float())
|
|
out = F.igemm(A.permute([0, 2, 1]), B.permute([0, 2, 1]))
|
|
torch.testing.assert_allclose(out.float(), out2.float())
|
|
|
|
n = 1
|
|
dim1 = torch.randint(1,64, size=(n,)).tolist()
|
|
dim2 = torch.randint(32,128, size=(n,)).tolist()
|
|
dim3 = torch.randint(32,256, size=(n,)).tolist()
|
|
values = list(product(dim1,dim2,dim3))
|
|
names = ['dim1_{0}_dim2_{1}_dim3_{2}'.format(*vals) for vals in values]
|
|
@pytest.mark.parametrize("dim1, dim2, dim3", values, ids=names)
|
|
def test_vector_quant(dim1, dim2, dim3):
|
|
dim2 = dim2 - (dim2 % 16)
|
|
dim3 = dim3 - (dim3 % 16)
|
|
for i in range(k):
|
|
A = torch.randn(size=(dim2, dim3), device='cuda')
|
|
qA, SA = F.vectorwise_quant(A, dim=0)
|
|
A1 = F.vectorwise_dequant(qA, SA)
|
|
torch.testing.assert_allclose(A1, A, atol=0.01, rtol=0.1)
|
|
|
|
|
|
|
|
n = 2
|
|
dim1 = torch.randint(2,256, size=(n,)).tolist()
|
|
dim2 = torch.randint(2,256, size=(n,)).tolist()
|
|
dim3 = torch.randint(2,256, size=(n,)).tolist()
|
|
#dim1, dim2 = (256,), (256,)
|
|
dtype = [torch.int8, torch.int32]
|
|
a_order = ['row']
|
|
out_order = ['col', 'row', 'col32']
|
|
transpose = [False]
|
|
dims = [2, 3]
|
|
values = list(product(dim1,dim2,dim3, dims,dtype, a_order, out_order, transpose))
|
|
|
|
names = ['dim1_{0}_dim2_{1}_dim3_{2}_dims_{3}_dtype_{4}_orderA_{5}_orderOut_{6}_transpose_{7}'.format(*vals) for vals in values]
|
|
@pytest.mark.parametrize("dim1, dim2, dim3, dims, dtype, orderA, orderOut, transpose", values, ids=names)
|
|
def test_nvidia_transform(dim1, dim2, dim3, dims, dtype, orderA, orderOut, transpose):
|
|
if dims == 3 and out_order != 'col32': return
|
|
if dtype == torch.int32 and out_order != 'col32': return
|
|
func = F.get_transform_func(dtype, orderA, orderOut, transpose)
|
|
|
|
if dims == 2:
|
|
A = torch.randint(-128, 127, size=(dim1, dim2), device='cuda').to(dtype)
|
|
elif dims == 3:
|
|
A = torch.randint(-128, 127, size=(dim1, dim2, dim3), device='cuda').to(dtype)
|
|
|
|
out, S = F.nvidia_transform(A, to_order=orderOut)
|
|
|
|
if orderOut == 'row':
|
|
torch.testing.assert_allclose(A.flatten(), out.flatten())
|
|
elif orderOut == 'col':
|
|
torch.testing.assert_allclose(A.t().flatten(), out.flatten())
|
|
elif orderOut == 'col32':
|
|
if dims == 2:
|
|
n = A.shape[0]*(A.shape[1] + (32 - (A.shape[1]%32)))
|
|
elif dims == 3:
|
|
n = A.shape[0]*A.shape[1]*(A.shape[2] + (32 - (A.shape[2]%32)))
|
|
assert out.numel() == n
|
|
elif orderOut == 'col_turing':
|
|
# 32 col 8 row tiles
|
|
n = (A.shape[0]+(8- A.shape[0]%8))*(A.shape[1] + (32 - (A.shape[1]%32)))
|
|
assert out.numel() == n
|
|
total_coltile = (A.shape[1] // 32) + (1 if A.shape[1] % 32 != 0 else 0)
|
|
for row in range(A.shape[0]):
|
|
for col in range(A.shape[1]):
|
|
i = row*A.shape[1]
|
|
j = col
|
|
|
|
coltile = (col // 32) + (1 if col % 32 != 0 else 0)
|
|
rowtile = ((row // 8) + (1 if row % 8 != 0 else 0))*total_coltile
|
|
offset = 32*8*(rowtile+coltile)
|
|
col2 = col % 32
|
|
row2 = (row%8)*32
|
|
|
|
|
|
assert A.flatten()[i+j] == A[row, col]
|
|
#assert A.flatten()[i+j] == out.flatten()[row2+col2]
|
|
#torch.testing.assert_allclose(A.flatten()[i+j], A[row, col])
|
|
#torch.testing.assert_allclose(A.flatten()[i+j], out.flatten()[row2+ col2+block_offset])
|
|
|
|
if orderOut == 'col32':
|
|
out2, S = F.nvidia_transform(out, from_order=orderOut, to_order='row', state=S)
|
|
torch.testing.assert_allclose(A, out2)
|
|
|
|
|
|
n = 1
|
|
dim1 = torch.randint(1,256, size=(n,)).tolist()
|
|
dim2 = torch.randint(32,512, size=(n,)).tolist()
|
|
dim3 = torch.randint(32,1024, size=(n,)).tolist()
|
|
dim4 = torch.randint(32,1024, size=(n,)).tolist()
|
|
|
|
#dim1 = [2]
|
|
#dim2 = [2]
|
|
#dim3 = [2]
|
|
#dim4 = [2]
|
|
|
|
dims = (2,3)
|
|
ldb = [0]
|
|
#ldb = list(range(256, 1*1024, 256))
|
|
values = list(product(dim1,dim2,dim3,dim4,dims, ldb))
|
|
names = ['dim1_{0}_dim2_{1}_dim3_{2}_dim4_{3}_dims_{4}_ldb_{5}'.format(*vals) for vals in values]
|
|
@pytest.mark.parametrize("dim1, dim2, dim3, dim4, dims, ldb", values, ids=names)
|
|
def test_igemmlt_int(dim1, dim2, dim3, dim4, dims, ldb):
|
|
for i in range(k):
|
|
if dims == 2:
|
|
A = torch.randint(-128, 127, size=(dim1, dim3), device='cuda').to(torch.int8)
|
|
elif dims == 3:
|
|
A = torch.randint(-128, 127, size=(dim1, dim2, dim3), device='cuda').to(torch.int8)
|
|
B = torch.randint(-128, 127, size=(dim4, dim3), device='cuda').to(torch.int8)
|
|
C1 = torch.matmul(A.float(), B.t().float())
|
|
|
|
A2, SA = F.transform(A, 'col32')
|
|
B2, SB = F.transform(B, 'col_turing')
|
|
C2, SC = F.igemmlt(A2, B2, SA, SB)
|
|
C3, S = F.nvidia_transform(C2, 'row', state=SC)
|
|
torch.testing.assert_allclose(C1, C3.float())
|
|
|
|
# transpose
|
|
B = torch.randint(-128, 127, size=(dim3, dim4), device='cuda').to(torch.int8)
|
|
C1 = torch.matmul(A.float(), B.float())
|
|
|
|
B2t, SBt = F.transform(B, 'col_turing', transpose=True)
|
|
C2, SC = F.igemmlt(A2, B2t, SA, SBt)
|
|
C3, S = F.nvidia_transform(C2, 'row', state=SC)
|
|
torch.testing.assert_allclose(C1, C3.float())
|
|
|
|
dim1 = [32]
|
|
dim2 = [32]
|
|
dim3 = [32]
|
|
dim4 = [32]
|
|
|
|
dims = (2,)
|
|
#ldb = list(range(256, 1*1024, 256))
|
|
values = list(product(dim1,dim2,dim3,dim4,dims))
|
|
names = ['dim1_{0}_dim2_{1}_dim3_{2}_dim4_{3}_dims_{4}'.format(*vals) for vals in values]
|
|
@pytest.mark.parametrize("dim1, dim2, dim3, dim4, dims", values, ids=names)
|
|
def test_igemmlt_half(dim1, dim2, dim3, dim4, dims):
|
|
formatB = F.get_special_format_str()
|
|
for i in range(k):
|
|
if dims == 2:
|
|
A = torch.normal(0, 0.5, size=(dim1, dim3), device='cuda').half()
|
|
elif dims == 3:
|
|
A = torch.normal(0, 0.5, size=(dim1, dim2, dim3), device='cuda').half()
|
|
B = torch.randn((dim4, dim3), device='cuda').half()
|
|
torch.nn.init.xavier_uniform_(B)
|
|
C1 = torch.matmul(A, B.t())
|
|
C2 = bnb.matmul(A, B.t())
|
|
|
|
A = A.view(-1, A.shape[-1])
|
|
|
|
CA, CAt, statsA, statsAt, coo_tensor = F.double_quant(A)
|
|
CB, CBt, statsB, statsBt, coo_tensor = F.double_quant(B)
|
|
C32A, SA = F.transform(CA, 'col32')
|
|
CxB, SB = F.transform(CB, to_order=formatB)
|
|
out1_32, Sout1_32 = F.igemmlt(C32A, CxB, SA, SB)
|
|
output = F.mm_dequant(out1_32, Sout1_32, statsAt, statsBt)
|
|
|
|
#print('')
|
|
#print(output.flatten()[:10])
|
|
#print(C1.flatten()[:10])
|
|
#print(C2.flatten()[:10])
|
|
|
|
|
|
#torch.testing.assert_allclose(C1.view(-1, C1.shape[-1]), output, atol=0.025, rtol=0.05)
|
|
|
|
# transpose
|
|
#B = torch.randint(-128, 127, size=(dim3, dim4), device='cuda').to(torch.int8)
|
|
#C1 = torch.matmul(A.float(), B.float())
|
|
|
|
#B2t, SBt = F.transform2(B, 'col_turing', transpose=True)
|
|
#C2, SC = F.igemmlt(A2, B2t, SA, SBt)
|
|
#C3, S = F.transform(C2, 'row', state=SC)
|
|
#torch.testing.assert_allclose(C1, C3.float())
|
|
|
|
batch_size = 2
|
|
seqdim = 512
|
|
#values = [(batch_size, seqdim, 4*1024, 16*1024),(batch_size, seqdim, 5120, 4*5120),(batch_size, seqdim, 12*1024, 4*12*1024)]
|
|
values = [(batch_size, seqdim, 4*1024, 3*4*1024),(batch_size, seqdim, 5120, 3*5120),(batch_size, seqdim, 12*1024, 4*12*1024)]
|
|
|
|
|
|
#values = list(product(batch, seq, model, hidden))
|
|
names = ['batch_{0}_seq_{1}_model_{2}_hidden_{3}'.format(*vals) for vals in values]
|
|
@pytest.mark.parametrize("batch, seq, model, hidden", values, ids=names)
|
|
def test_bench_8bit_training(batch, seq, model, hidden):
|
|
formatB = F.get_special_format_str()
|
|
A = torch.randn(batch, seq, model, device='cuda').half()
|
|
grad = torch.randn(batch, seq, model, device='cuda').half()
|
|
w1 = torch.randint(-128, 127, size=(hidden, model), device='cuda').half()
|
|
w2 = torch.randint(-128, 127, size=(model, hidden), device='cuda').half()
|
|
print('')
|
|
|
|
#torch.cuda.synchronize()
|
|
## warmup
|
|
#for i in range(100):
|
|
# torch.matmul(A, w1.t())
|
|
#torch.cuda.synchronize()
|
|
|
|
dtype = torch.int8
|
|
A = A.view(-1, A.shape[-1]).contiguous()
|
|
grad = grad.view(-1, grad.shape[-1]).contiguous()
|
|
torch.cuda.synchronize()
|
|
t0 = time.time()
|
|
for i in range(k):
|
|
|
|
out1 = torch.matmul(A, w1.t()) # fc1
|
|
#out2 = torch.matmul(out1, w2.t())# fc2
|
|
|
|
#d1 = torch.matmul(grad, w2) # delta1
|
|
#d2 = torch.matmul(d1, w1) # delta2
|
|
|
|
#grad1 = torch.einsum('bo,bh->oh', out1, grad) # grad w2
|
|
#grad2 = torch.einsum('bh,bo->ho', A, d2) # grad w1
|
|
|
|
torch.cuda.synchronize()
|
|
t16 = time.time() - t0
|
|
print(t16)
|
|
|
|
#torch.cuda.empty_cache()
|
|
|
|
#Cw1, Cw1t, statsw1, statsw1t, coo_tensor = F.double_quant(w1)
|
|
#Cw2, Cw2t, statsw2, statsw2t, coo_tensor = F.double_quant(w2)
|
|
|
|
#CTw1, Sw1 = F.transform2(Cw1, formatB)
|
|
#CTw2, Sw2 = F.transform2(Cw2, formatB)
|
|
#CTw2t, Sw2t = F.transform2(Cw2t, formatB, transpose=True)
|
|
#CTw1t, Sw1t = F.transform2(Cw1t, formatB, transpose=True)
|
|
|
|
#CA, CAt, statsA, statsAt, coo_tensor = F.double_quant(A)
|
|
#C32A, SA = F.transform2(CA, 'col32')
|
|
## fc1
|
|
#out1_32, Sout1_32 = F.igemmlt(C32A, CTw1, SA, Sw1, dtype=dtype)
|
|
##out1 = F.mm_dequant(out1_32, Sout1_32, statsAt, statsw1t)
|
|
|
|
## fc2
|
|
#Cout1, Cout1t, statsout1, statsout1t, coo_tensor = F.double_quant(out1)
|
|
#C32out1, Sout1 = F.transform2(Cout1, 'col32')
|
|
#out2_32, Sout2_32 = F.igemmlt(C32out1, CTw2, Sout1, Sw2, dtype=dtype)
|
|
##out2 = F.mm_dequant(out2_32, Sout2_32, statsout1t, statsw2t)
|
|
|
|
## delta1
|
|
#Cgrad, Cgradt, statsgrad, statsgradt, coo_tensor = F.double_quant(grad)
|
|
#C32grad, Sgrad = F.transform2(Cgrad, 'col32')
|
|
##d1_32, Sd1_32 = F.igemmlt(C32grad, CTw2t, Sgrad, Sw2t, dtype=dtype)
|
|
##d1 = F.mm_dequant(d1_32, Sd1_32, statsgradt, statsw2)
|
|
|
|
## delta2
|
|
#Cd1, Cd1t, statsd1, statsd1t, coo_tensor = F.double_quant(d1)
|
|
#C32d1, Sd1 = F.transform2(Cd1, 'col32')
|
|
##d2_32, Sd2_32 = F.igemmlt(C32d1, CTw1t, Sd1, Sw1t, dtype=dtype)
|
|
##d2 = F.mm_dequant(d2_32, Sd2_32, statsd1t, statsw1)
|
|
|
|
## grad1
|
|
#C32out1t, Sout1t = F.transform2(Cout1t, 'col32', transpose=True)
|
|
#CTgradt, Sgradt = F.transform2(Cgradt, formatB, transpose=True)
|
|
##grad1_32, Sgrad1_32 = F.igemmlt(C32out1t, CTgradt, Sout1t, Sgradt, dtype=dtype)
|
|
##grad1 = F.mm_dequant(grad1_32, Sgrad1_32, statsout1, statsgrad)
|
|
|
|
## grad2
|
|
#C32At, SAt = F.transform2(CAt, 'col32', transpose=True)
|
|
#CTd1t, Sd1t = F.transform2(Cd1t, formatB, transpose=True)
|
|
##grad2_32, Sgrad2_32 = F.igemmlt(C32At, CTd1t, SAt, Sd1t, dtype=dtype)
|
|
##grad2 = F.mm_dequant(grad2_32, Sgrad2_32, statsA, statsd1)
|
|
|
|
#Cw2, Cw2t, statsw2, statsw2t, coo_tensor = F.double_quant(w2)
|
|
|
|
#Cw1, Cw1t, statsw1, statsw1t, coo_tensor = F.double_quant(w1)
|
|
#Cw2, Cw2t, statsw2, statsw2t, coo_tensor = F.double_quant(w2)
|
|
|
|
#CTw1, Sw1 = F.transform2(Cw1, formatB)
|
|
#CTw1t, Sw1t = F.transform2(Cw1t, formatB, transpose=True)
|
|
#CTw2, Sw2 = F.transform2(Cw2, formatB)
|
|
#CTw2t, Sw2t = F.transform2(Cw2t, formatB, transpose=True)
|
|
#torch.cuda.synchronize()
|
|
#t0 = time.time()
|
|
#for i in range(k):
|
|
# #Cw1, Cw1t, statsw1, statsw1t, coo_tensor = F.double_quant(w1)
|
|
# #CTw1, Sw1 = F.transform2(Cw1, formatB)
|
|
# #Cw1, Cw1t, statsw1, statsw1t, coo_tensor = F.double_quant(w1)
|
|
# #CTw1, Sw1 = F.transform2(Cw1, formatB)
|
|
|
|
# #CA, CAt, statsA, statsAt, coo_tensor = F.double_quant(A, threshold=3.5)
|
|
# CA, CAt, statsA, statsAt, coo_tensor = F.double_quant(A)
|
|
# #CTw1t, Sw1t = F.transform2(Cw1t, formatB, transpose=True)
|
|
# #CTw2, Sw2 = F.transform2(Cw2, formatB)
|
|
# #CTw2t, Sw2t = F.transform2(Cw2t, formatB, transpose=True)
|
|
|
|
# C32A, SA = F.transform2(CA, 'col32')
|
|
|
|
# # fc1
|
|
# out1_32, Sout1_32 = F.igemmlt(C32A, CTw1, SA, Sw1, dtype=dtype)
|
|
# #out1dn = F.mm_dequant(out1_32, Sout1_32, statsA, statsw1)
|
|
|
|
# #print(coo_tensor.nnz)
|
|
# #out1sp = F.spmm_coo(coo_tensor, w1.t())
|
|
# #print(w1.t().shape)
|
|
# #out1 = out1dn + out1sp
|
|
|
|
# # fc2
|
|
# Cout1, Cout1t, statsout1, statsout1t, coo_tensor = F.double_quant(out1)
|
|
# C32out1, Sout1 = F.transform2(Cout1, 'col32')
|
|
# out2_32, Sout2_32 = F.igemmlt(C32out1, CTw2, Sout1, Sw2, dtype=dtype)
|
|
# #out2 = F.mm_dequant(out2_32, Sout2_32, statsout1, statsw2)
|
|
|
|
# # delta1
|
|
# Cgrad, Cgradt, statsgrad, statsgradt, coo_tensor = F.double_quant(grad)
|
|
# C32grad, Sgrad = F.transform2(Cgrad, 'col32')
|
|
# d1_32, Sd1_32 = F.igemmlt(C32grad, CTw2t, Sgrad, Sw2t, dtype=dtype)
|
|
# #d1 = F.mm_dequant(d1_32, Sd1_32, statsgrad, statsw2t)
|
|
|
|
# # delta2
|
|
# Cd1, Cd1t, statsd1, statsd1t, coo_tensor = F.double_quant(d1)
|
|
# C32d1, Sd1 = F.transform2(Cd1, 'col32')
|
|
# d2_32, Sd2_32 = F.igemmlt(C32d1, CTw1t, Sd1, Sw1t, dtype=dtype)
|
|
# #d2 = F.mm_dequant(d2_32, Sd2_32, statsd1, statsw1t)
|
|
|
|
# # grad1
|
|
# #C32out1t, Sout1t = F.transform2(Cout1t, 'col32', transpose=True)
|
|
# #CTgradt, Sgradt = F.transform2(Cgradt, formatB, transpose=True)
|
|
# #grad1_32, Sgrad1_32 = F.igemmlt(C32out1t, CTgradt, Sout1t, Sgradt, dtype=dtype)
|
|
# #grad1 = F.mm_dequant(grad1_32, Sgrad1_32, statsout1t, statsgradt)
|
|
|
|
# ## grad2
|
|
# #C32At, SAt = F.transform2(CAt, 'col32', transpose=True)
|
|
# #CTd1t, Sd1t = F.transform2(Cd1t, formatB, transpose=True)
|
|
# #grad2_32, Sgrad2_32 = F.igemmlt(C32At, CTd1t, SAt, Sd1t, dtype=dtype)
|
|
# #grad2 = F.mm_dequant(grad2_32, Sgrad2_32, statsAt, statsd1t)
|
|
|
|
#torch.cuda.synchronize()
|
|
#t8 = time.time() - t0
|
|
#print(t8)
|
|
|
|
|
|
|
|
|
|
|
|
n = 2
|
|
dim1 = torch.randint(64,256, size=(n,)).tolist()
|
|
dim4 = torch.randint(64,1024, size=(n,)).tolist()
|
|
|
|
#dim1 = [2*1024]
|
|
#dim4 = [2*1024]
|
|
|
|
#dim1 = [4]
|
|
#dim4 = [4]
|
|
|
|
dims = (2,)
|
|
#ldb = list(range(256, 1*1024, 256))
|
|
formatB = ['col_turing', 'col_ampere']
|
|
values = list(product(dim1,dim4,dims, formatB))
|
|
names = ['dim1_{0}_dim4_{1}_dims_{2}_formatB_{3}'.format(*vals) for vals in values]
|
|
@pytest.mark.parametrize("dim1, dim4, dims, formatB", values, ids=names)
|
|
def test_dequant_mm(dim1, dim4, dims, formatB):
|
|
inner = torch.randint(1, 128, size=(1,)).item()
|
|
formatB = F.get_special_format_str()
|
|
for i in range(k):
|
|
A = torch.randn(dim1, inner, device='cuda')
|
|
B = torch.randn(dim4, inner, device='cuda')
|
|
C1 = torch.matmul(A.half(), B.t().half())
|
|
|
|
A1, maxA = F.vectorwise_quant(A, dim=1)
|
|
B1, maxB = F.vectorwise_quant(B, dim=1)
|
|
|
|
A2, SA = F.nvidia_transform(A1, 'col32')
|
|
B2, SB = F.nvidia_transform(B1, formatB)
|
|
C2, SC = F.igemmlt(A2, B2, SA, SB)
|
|
|
|
C3, S = F.nvidia_transform(C2, 'row', state=SC)
|
|
C4 = F.vectorwise_mm_dequant(C3.float(), maxA, maxB.t())
|
|
|
|
count = (torch.isclose(C1, C4, atol=0.01, rtol=0.1) == 0).sum().item()
|
|
n = C1.numel()
|
|
p = 0.06
|
|
assert count/n < p, f'error in more than {p} of elements: {count}/{n}={count/n}'
|
|
|
|
C5 = F.mm_dequant(C2, SC, maxA.flatten(), maxB.flatten())
|
|
torch.testing.assert_allclose(C5, C4)
|
|
#print(C2)
|
|
|
|
|
|
|
|
n = 2
|
|
dim1 = [1*1024]
|
|
dim2 = [1*1024]
|
|
#dim1 = torch.randint(1,4*1024, size=(n,)).tolist()
|
|
#dim2 = torch.randint(1,4*1024, size=(n,)).tolist()
|
|
|
|
dims = (2,)
|
|
#ldb = list(range(256, 1*1024, 256))
|
|
values = list(product(dim1,dim2,dims))
|
|
names = ['dim1_{0}_dim2_{1}_dims_{2}'.format(*vals) for vals in values]
|
|
@pytest.mark.parametrize("dim1, dim2, dims", values, ids=names)
|
|
def test_colrow_absmax(dim1, dim2, dims):
|
|
for i in range(k):
|
|
threshold = 3.0
|
|
A = torch.randn(dim1, dim2, device='cuda').half()
|
|
A_truncated = A.clone()
|
|
A_truncated[torch.abs(A_truncated) >= 3.0] = 0.0
|
|
if dims == 2:
|
|
row_stats1, _ = torch.abs(A.float()).max(1)
|
|
col_stats1, _ = torch.abs(A.float()).max(0)
|
|
row_stats1_trunc, _ = torch.abs(A_truncated.float()).max(1)
|
|
col_stats1_trunc, _ = torch.abs(A_truncated.float()).max(0)
|
|
else:
|
|
assert False
|
|
|
|
row_stats2, col_stats2, nnz_block_ptr2 = F.get_colrow_absmax(A, threshold=threshold)
|
|
|
|
A_blocked = einops.rearrange(torch.abs(A), '(rows row_tiles) (cols block_size)-> rows cols row_tiles block_size', row_tiles=16, block_size=64*4)
|
|
nnz_rows1_counts = (torch.abs(A_blocked)>=threshold).sum(3).flatten()
|
|
nnz_block_ptr1 = torch.zeros(nnz_rows1_counts.shape[0]+1, dtype=nnz_rows1_counts.dtype, device=nnz_rows1_counts.device)
|
|
nnz_block_ptr1[1:] = nnz_rows1_counts.cumsum(0)
|
|
|
|
torch.testing.assert_allclose(col_stats1_trunc, col_stats2)
|
|
torch.testing.assert_allclose(row_stats1_trunc, row_stats2)
|
|
torch.testing.assert_allclose(nnz_block_ptr1, nnz_block_ptr2)
|
|
|
|
row_stats2, col_stats2, nnz_block_ptr2 = F.get_colrow_absmax(A, threshold=0.0)
|
|
|
|
torch.testing.assert_allclose(col_stats1, col_stats2)
|
|
torch.testing.assert_allclose(row_stats1, row_stats2)
|
|
assert nnz_block_ptr2 is None
|
|
|
|
|
|
|
|
n = 2
|
|
#dim1 = [8*1024]
|
|
#dim2 = [4*1024]
|
|
dim1 = torch.randint(1,4*1024, size=(n,)).tolist()
|
|
dim2 = torch.randint(1,4*1024, size=(n,)).tolist()
|
|
|
|
values = list(product(dim1,dim2))
|
|
names = ['dim1_{0}_dim2_{1}'.format(*vals) for vals in values]
|
|
@pytest.mark.parametrize("dim1, dim2", values, ids=names)
|
|
def test_double_quant(dim1, dim2):
|
|
for i in range(k):
|
|
A = torch.randn(dim1, dim2, device='cuda').half()
|
|
out_col1, Scol = F.vectorwise_quant(A, dim=0)
|
|
out_row1, Srow = F.vectorwise_quant(A, dim=1)
|
|
|
|
CA, CAt, statsA, statsAt, coo_tensor = F.double_quant(A)
|
|
|
|
# max difference is 1 due to rounding differences
|
|
torch.testing.assert_allclose(CA, out_row1, atol=1, rtol=0)
|
|
torch.testing.assert_allclose(CAt, out_col1, atol=1, rtol=0)
|
|
|
|
|
|
n = CAt.numel()
|
|
num_not_close_rows = (torch.isclose(CA, out_row1, atol=1)==0).sum().item()
|
|
num_not_close_cols = (torch.isclose(CAt, out_col1, atol=1)==0).sum().item()
|
|
|
|
# allow for 1:500 error due to rounding differences
|
|
min_error = 1/500
|
|
if num_not_close_cols > (min_error*n):
|
|
print(f'Min error exceeded {num_not_close_cols} elements are different. Error: {num_not_close_cols/n:.4f}')
|
|
assert False
|
|
if num_not_close_rows > (min_error*n):
|
|
print(f'Min error exceeded {num_not_close_rows} elements are different. Error: {num_not_close_rows/n:.4f}')
|
|
assert False
|
|
|
|
torch.testing.assert_allclose(Srow.flatten(), statsA)
|
|
torch.testing.assert_allclose(Scol.flatten(), statsAt)
|
|
|
|
|
|
n = 4
|
|
dim1 = torch.randint(1,4*1024, size=(n,)).tolist()
|
|
dim4 = torch.randint(1,4*1024, size=(n,)).tolist()
|
|
inner = torch.randint(1,4*1024, size=(n,)).tolist()
|
|
|
|
dim1 = [6]
|
|
dim4 = [4]
|
|
inner = [8]
|
|
|
|
values = list(zip(dim1, dim4, inner))
|
|
names = ['dim1_{0}_dim4_{1}_inner_{2}'.format(*vals) for vals in values]
|
|
@pytest.mark.parametrize("dim1, dim4, inner", values, ids=names)
|
|
def test_integrated_igemmlt(dim1, dim4, inner):
|
|
for i in range(k):
|
|
A = torch.randn(dim1, inner, device='cuda').half()
|
|
B = torch.randn(dim4, inner, device='cuda').half()
|
|
|
|
out1 = torch.matmul(A.half(), B.t().half())
|
|
|
|
C1a, C1b, stats1a, stats1b, coo_tensor = F.double_quant(A)
|
|
C2a, C2b, stats2a, stats2b, coo_tensor = F.double_quant(B)
|
|
A1, maxA = F.vectorwise_quant(A, dim=1)
|
|
B1, maxB = F.vectorwise_quant(B, dim=1)
|
|
|
|
torch.testing.assert_allclose(maxA.flatten(), stats1a)
|
|
torch.testing.assert_allclose(maxB.flatten(), stats2a)
|
|
torch.testing.assert_allclose(C1a, A1, rtol=0, atol=1)
|
|
torch.testing.assert_allclose(C2a, B1, rtol=0, atol=1)
|
|
|
|
A2, SA = F.nvidia_transform(C1a, 'col32')
|
|
B2, SB = F.nvidia_transform(C2a, 'col_turing')
|
|
outC32, SC = F.igemmlt(A2, B2, SA, SB)
|
|
out2 = F.mm_dequant(outC32, SC, stats1a, stats2a)
|
|
|
|
A2, SA = F.nvidia_transform(A1, 'col32')
|
|
B2, SB = F.nvidia_transform(B1, 'col_turing')
|
|
C2, SC = F.igemmlt(A2, B2, SA, SB)
|
|
|
|
C3, S = F.nvidia_transform(C2, 'row', state=SC)
|
|
out3 = F.vectorwise_mm_dequant(C3.float(), maxA, maxB.t())
|
|
|
|
err1 = torch.abs(out1-out2).mean().item()
|
|
err2 = torch.abs(out1-out3).mean().item()
|
|
assert err2 <= err1*1.01
|
|
|
|
|
|
n = 6
|
|
dim1 = torch.randint(1,4*1024, size=(n,)).tolist()
|
|
dim4 = torch.randint(1,4*1024, size=(n,)).tolist()
|
|
inner = torch.randint(1,4*1024, size=(n,)).tolist()
|
|
|
|
values = list(zip(dim1, dim4, inner))
|
|
names = ['dim1_{0}_dim4_{1}_inner_{2}'.format(*vals) for vals in values]
|
|
@pytest.mark.parametrize("dim1, dim4, inner", values, ids=names)
|
|
def test_igemmlt_row_scale(dim1, dim4, inner):
|
|
formatB = F.get_special_format_str()
|
|
err1, err2, err3 = [], [], []
|
|
relerr1, relerr2 = [], []
|
|
scale = 1
|
|
for i in range(k):
|
|
A = torch.randn(dim1, inner, device='cuda').half()
|
|
B = torch.randn(dim4, inner, device='cuda').half()
|
|
torch.nn.init.xavier_uniform_(B)
|
|
C1 = torch.matmul(A, B.t())
|
|
|
|
out1 = torch.matmul(A.half(), B.t().half())
|
|
|
|
|
|
C1a, C1b, stats1a, stats1b, coo_tensor = F.double_quant(A)
|
|
CB, absmaxB = F.vectorwise_quant(B, quant_type='linear')
|
|
A2, SA = F.nvidia_transform(C1a, 'col32')
|
|
B2, SB = F.nvidia_transform(CB, formatB)
|
|
A1, maxA = F.vectorwise_quant(A, dim=1)
|
|
|
|
c = 10.0*inner*scale
|
|
row_scale = torch.ones_like(maxA)/c
|
|
outC32, SC = F.igemmlt(A2, B2, SA, SB, dtype=torch.int8, row_scale=row_scale)
|
|
C3, S = F.nvidia_transform(outC32, 'row', state=SC)
|
|
maxval = torch.abs(C3).max()
|
|
if maxval == 127:
|
|
scale = 1.5
|
|
else:
|
|
scale = maxval/120
|
|
out3 = C3*maxA*absmaxB*c/(127*127)
|
|
|
|
C4 = torch.matmul(C1a.float(), CB.float().t())
|
|
|
|
|
|
C2a, C2b, stats2a, stats2b, coo_tensor = F.double_quant(B)
|
|
B2, SB = F.nvidia_transform(C2a, formatB)
|
|
outC32, SC = F.igemmlt(A2, B2, SA, SB)
|
|
out2 = F.mm_dequant(outC32, SC, stats1a, stats2a)
|
|
|
|
CA, SA = F.vectorwise_quant(A, dim=1, quant_type='vector')
|
|
CB, SB = F.vectorwise_quant(B, dim=1, quant_type='linear')
|
|
|
|
C = torch.matmul(CA.float(), CB.t().float())
|
|
out4 = C*SA*SB/(127*127)
|
|
#out4 = torch.clip(torch.round(C*SA/c), -127, 127)*c*SB/(127*127)
|
|
|
|
#print('='*80)
|
|
#print(out1)
|
|
#print(out2)
|
|
#print(out3)
|
|
|
|
#print(out1)
|
|
#print(out2)
|
|
#print(out3)
|
|
err1.append(torch.abs(out1-out2).mean().item())
|
|
err2.append(torch.abs(out1-out3).mean().item())
|
|
err3.append(torch.abs(out1-out4).mean().item())
|
|
|
|
#assert_all_approx_close(C3.float(), torch.round(C4*row_scale), rtol=0, atol=0, count=10)
|
|
print('')
|
|
print(sum(err1)/len(err1))
|
|
print(sum(err2)/len(err2))
|
|
print(sum(err3)/len(err3))
|
|
|
|
|
|
dim1 = [1024, 2048]
|
|
inner = [12288*4, 4096*4]
|
|
dim4 = [12288, 4096]
|
|
|
|
values = list(zip(dim1, dim4, inner))
|
|
names = ['dim1_{0}_dim4_{1}_inner_{2}'.format(*vals) for vals in values]
|
|
@pytest.mark.parametrize("dim1, dim4, inner", values, ids=names)
|
|
def test_row_scale_bench(dim1, dim4, inner):
|
|
err1, err2, err3 = [], [], []
|
|
relerr1, relerr2 = [], []
|
|
scale = 1
|
|
A = torch.randn(dim1, inner, device='cuda').half()
|
|
B = torch.randn(dim4, inner, device='cuda').half()
|
|
torch.nn.init.xavier_uniform_(B)
|
|
# warmpup
|
|
for i in range(k):
|
|
C1 = torch.matmul(A, B.t())
|
|
|
|
torch.cuda.synchronize()
|
|
t0 = time.time()
|
|
for i in range(k):
|
|
C1 = torch.matmul(A, B.t())
|
|
torch.cuda.synchronize()
|
|
print('16', time.time()-t0)
|
|
|
|
C1a, C1b, stats1a, stats1b, coo_tensor = F.double_quant(A)
|
|
CB, absmaxB = F.vectorwise_quant(B, quant_type='linear')
|
|
A2, SA = F.nvidia_transform(C1a, 'col32')
|
|
B2, SB = F.nvidia_transform(CB, formatB)
|
|
A1, maxA = F.vectorwise_quant(A, dim=1)
|
|
|
|
c = 10.0*inner*scale
|
|
row_scale = maxA/c
|
|
torch.cuda.synchronize()
|
|
t0 = time.time()
|
|
for i in range(k):
|
|
outC32, SC = F.igemmlt(A2, B2, SA, SB, dtype=torch.int8, row_scale=row_scale)
|
|
torch.cuda.synchronize()
|
|
print('row-wise', time.time()-t0)
|
|
|
|
|
|
C2a, C2b, stats2a, stats2b, coo_tensor = F.double_quant(B)
|
|
B2, SB = F.nvidia_transform(C2a, formatB)
|
|
torch.cuda.synchronize()
|
|
t0 = time.time()
|
|
for i in range(k):
|
|
outC32, SC = F.igemmlt(A2, B2, SA, SB)
|
|
torch.cuda.synchronize()
|
|
print('vector-wise', time.time()-t0)
|
|
|
|
|
|
|
|
|
|
n = 2
|
|
dim1 = torch.randint(2,1024, size=(n,)).tolist()
|
|
dim2 = torch.randint(2,1024, size=(n,)).tolist()
|
|
#dim1 = [8*1024]
|
|
#dim2 = [4*1024]
|
|
|
|
dim3 = [0]
|
|
dtype = [torch.int8]
|
|
a_order = ['row']
|
|
out_order = ['col32', 'col_turing', 'col_ampere']
|
|
transpose = [False, True]
|
|
dims = [2]
|
|
values = list(product(dim1,dim2,dim3, dims,dtype, a_order, out_order, transpose))
|
|
names = ['dim1_{0}_dim2_{1}_dim3_{2}_dims_{3}_dtype_{4}_orderA_{5}_orderOut_{6}_{7}'.format(*vals) for vals in values]
|
|
@pytest.mark.parametrize("dim1, dim2, dim3, dims, dtype, orderA, orderOut, transpose", values, ids=names)
|
|
def test_transform(dim1, dim2, dim3, dims, dtype, orderA, orderOut, transpose):
|
|
for i in range(k):
|
|
if dims == 2:
|
|
A = torch.randint(10, 99, size=(dim1, dim2), device='cuda').to(dtype)
|
|
elif dims == 3:
|
|
A = torch.randint(10, 99, size=(dim1, dim2, dim3), device='cuda').to(dtype)
|
|
|
|
A.view(-1)[-1] = -1
|
|
if transpose:
|
|
At = A.t().contiguous()
|
|
out1, S1 = F.nvidia_transform(At, to_order=orderOut)
|
|
else:
|
|
out1, S1 = F.nvidia_transform(A, to_order=orderOut)
|
|
out2, S2 = F.transform(A, to_order=orderOut, transpose=transpose)
|
|
|
|
assert S1[0][0] == S2[0][0]
|
|
assert S1[0][1] == S2[0][1]
|
|
#print(out1)
|
|
#print(out2)
|
|
|
|
torch.testing.assert_allclose(out1, out2)
|
|
|
|
n = 2
|
|
#dim1 = torch.randint(2,1024, size=(n,)).tolist()
|
|
#dim2 = torch.randint(2,1024, size=(n,)).tolist()
|
|
dim1 = [1]
|
|
dim2 = [33]
|
|
|
|
dtype = [torch.int8]
|
|
#a_order = ['col_turing', 'col_ampere']
|
|
a_order = ['col_turing']
|
|
out_order = ['row']
|
|
values = list(product(dim1,dim2,dtype, a_order, out_order))
|
|
names = ['dim1_{0}_dim2_{1}_dtype_{2}_orderA_{3}_orderOut_{4}'.format(*vals) for vals in values]
|
|
@pytest.mark.parametrize("dim1, dim2, dtype, orderA, orderOut", values, ids=names)
|
|
def test_transform_to_row(dim1, dim2, dtype, orderA, orderOut):
|
|
for i in range(1):
|
|
A = torch.randint(-127, 127, size=(dim1, dim2), device='cuda').to(dtype)
|
|
|
|
out2, S2 = F.transform(A, to_order=orderA)
|
|
A2, S3 = F.transform(out2, from_order=orderA, to_order='row', state=S2)
|
|
assert A2.shape[0] == A.shape[0]
|
|
assert A2.shape[1] == A.shape[1]
|
|
|
|
|
|
print('')
|
|
print(A)
|
|
print(out2)
|
|
print(A2)
|
|
|
|
|
|
#torch.testing.assert_allclose(A, A2)
|
|
|
|
|
|
|
|
|
|
def test_overflow():
|
|
formatB = F.get_special_format_str()
|
|
for i in range(2):
|
|
a = torch.arange(5, 15).cuda().to(torch.int8).view(-1,1 )
|
|
b = torch.arange(5, 15).cuda().to(torch.int8).view(-1,1 )
|
|
|
|
Ca, Sa = F.nvidia_transform(a, 'col32')
|
|
Cb, Sb = F.nvidia_transform(b, formatB)
|
|
|
|
c = F.igemmlt(Ca, Cb, Sa, Sb, dtype=torch.int8)
|
|
c2 = torch.matmul(a.float(), b.float().t())
|
|
|
|
|
|
n = 2
|
|
dim1 = torch.randint(1,4*1024, size=(n,)).tolist()
|
|
dim2 = torch.randint(1,4*1024, size=(n,)).tolist()
|
|
#dim1 = [4]
|
|
#dim2 = [5]
|
|
|
|
values = list(product(dim1,dim2))
|
|
names = ['dim1_{0}_dim2_{1}'.format(*vals) for vals in values]
|
|
@pytest.mark.parametrize("dim1, dim2", values, ids=names)
|
|
def test_coo_double_quant(dim1, dim2):
|
|
threshold = 3.00
|
|
for i in range(k):
|
|
A = torch.randn(dim1, dim2, device='cuda').half()
|
|
|
|
idx = (torch.abs(A) >= threshold)
|
|
CA2, CAt, statsA, statsAt, coo_tensor = F.double_quant(A)
|
|
CA, CAt, statsA, statsAt, coo_tensor = F.double_quant(A, threshold=threshold)
|
|
|
|
if coo_tensor is not None:
|
|
A1 = A*idx
|
|
A2 = torch.zeros_like(A)
|
|
A2[coo_tensor.rowidx.long(), coo_tensor.colidx.long()] = coo_tensor.values
|
|
torch.testing.assert_allclose(A1, A2)
|
|
|
|
A1 = A*(idx==0)
|
|
A2 = (CA.float()*statsA.unsqueeze(1)/127).half()
|
|
torch.testing.assert_allclose(A*(idx==0), A2, rtol=0.05, atol=1.5e-2)
|
|
|
|
n = 2
|
|
dim1 = torch.randint(1,1*1024, size=(n,)).tolist()
|
|
dim2 = torch.randint(1,1*1024, size=(n,)).tolist()
|
|
#dim1 = [7]
|
|
#dim2 = [11]
|
|
transposed_B = [False, True]
|
|
values = list(product(dim1,dim2, transposed_B))
|
|
names = ['dim1_{0}_dim2_{1}_transposed_B_{2}'.format(*vals) for vals in values]
|
|
@pytest.mark.parametrize("dim1, dim2, transposed_B", values, ids=names)
|
|
def test_spmm_coo(dim1, dim2, transposed_B):
|
|
threshold = 1.5
|
|
dim3 = torch.randint(32, 128, size=(1,)).item()
|
|
#dim3 = 17
|
|
for i in range(k):
|
|
A = torch.randn(dim1, dim2).cuda().half()
|
|
if transposed_B:
|
|
B = torch.randn(dim3, dim2).cuda().half()
|
|
else:
|
|
B = torch.randn(dim2, dim3).cuda().half()
|
|
|
|
idx = torch.abs(A) >= threshold
|
|
nnz = (idx == 1).sum().item()
|
|
rows, cols = torch.where(idx)
|
|
values = A[idx]
|
|
cooA = F.COOSparseTensor(A.shape[0], A.shape[1], nnz, rows.int(), cols.int(), values)
|
|
A2 = A*idx
|
|
|
|
if transposed_B:
|
|
out2 = F.spmm_coo(cooA, B.t())
|
|
out1 = torch.matmul(A2, B.t())
|
|
else:
|
|
out2 = F.spmm_coo(cooA, B)
|
|
out1 = torch.matmul(A2, B)
|
|
|
|
assert_all_approx_close(out1, out2, rtol=0.01, atol=3.0e-2, count=30)
|
|
|
|
|
|
|
|
def test_spmm_bench():
|
|
batch = 2
|
|
model = 1024*1
|
|
hidden = model*4
|
|
seq = 1024
|
|
dim1 = batch*seq
|
|
dim2 = model
|
|
dim3 = hidden
|
|
threshold = 4
|
|
A = torch.randn(dim1, dim2, device='cuda').half()
|
|
B = torch.randn(dim2, dim3, device='cuda').half()
|
|
for i in range(10):
|
|
C1 = bnb.matmul(A, B)
|
|
|
|
torch.cuda.synchronize()
|
|
t0 = time.time()
|
|
for i in range(k):
|
|
C1 = bnb.matmul(A, B)
|
|
torch.cuda.synchronize()
|
|
t8 = time.time()-t0
|
|
|
|
idx = torch.abs(A) >= threshold
|
|
nnz = (idx == 1).sum().item()
|
|
print(nnz/idx.numel())
|
|
rows, cols = torch.where(idx)
|
|
values = A[idx]
|
|
cooA = F.COOSparseTensor(A.shape[0], A.shape[1], nnz, rows.int(), cols.int(), values)
|
|
|
|
for i in range(10):
|
|
out2 = F.spmm_coo(cooA, B)
|
|
|
|
torch.cuda.synchronize()
|
|
t0 = time.time()
|
|
for i in range(k):
|
|
out2 = F.spmm_coo(cooA, B)
|
|
torch.cuda.synchronize()
|
|
tsp = time.time()-t0
|
|
print(tsp, t8)
|
|
print(tsp/t8)
|
|
|
|
|
|
n = 2
|
|
dim1 = torch.randint(256,1*1024, size=(n,)).tolist()
|
|
dim2 = torch.randint(256,1*1024, size=(n,)).tolist()
|
|
values = list(product(dim1,dim2))
|
|
names = ['dim1_{0}_dim2_{1}'.format(*vals) for vals in values]
|
|
@pytest.mark.parametrize("dim1, dim2", values, ids=names)
|
|
def test_integrated_sparse_decomp(dim1, dim2):
|
|
threshold = 3.0
|
|
formatB = 'col_turing'
|
|
for i in range(k):
|
|
A = torch.randn(dim1, dim2).cuda().half()
|
|
w1 = torch.randn(dim1, dim2).cuda().half()
|
|
out1 = torch.matmul(A, w1.t())
|
|
|
|
Cw1, Cw1t, statsw1, statsw1t, coo_tensor = F.double_quant(w1)
|
|
CTw1, Sw1 = F.transform(Cw1, formatB)
|
|
|
|
CA, CAt, statsA, statsAt, coo_tensor = F.double_quant(A)
|
|
C32A, SA = F.transform(CA, 'col32')
|
|
|
|
out1_32, Sout1_32 = F.igemmlt(C32A, CTw1, SA, Sw1)
|
|
out2 = F.mm_dequant(out1_32, Sout1_32, statsA, statsw1)
|
|
|
|
CA, CAt, statsA, statsAt, coo_tensor = F.double_quant(A, threshold=threshold)
|
|
C32A, SA = F.transform(CA, 'col32')
|
|
|
|
out1_32, Sout1_32 = F.igemmlt(C32A, CTw1, SA, Sw1)
|
|
out3 = F.mm_dequant(out1_32, Sout1_32, statsA, statsw1)
|
|
|
|
assert coo_tensor is not None
|
|
|
|
out4 = F.spmm_coo(coo_tensor, w1.t())
|
|
out5 = out3 + out4
|
|
|
|
err1 = torch.abs(out1-out2).mean().item()
|
|
err2 = torch.abs(out1-out5).mean().item()
|
|
assert err2 < err1
|
|
|
|
|
|
def test_matmuls():
|
|
a = torch.randn(256, 256).half().cuda()
|
|
b = torch.randn(256, 256).half().cuda()
|
|
c1 = torch.matmul(a, b)
|
|
c2 = bnb.matmul(a, b)
|
|
c3 = bnb.matmul(a, b)
|
|
|
|
err1 = torch.abs(c1-c2).mean().item()
|
|
err2 = torch.abs(c1-c3).mean().item()
|
|
assert err1 < 0.2
|
|
assert err2 < 0.2
|
|
|
|
|
|
|
|
n = 2
|
|
#dim1 = torch.randint(1,1*1024, size=(n,)).tolist()
|
|
#dim2 = torch.randint(1,4*1024, size=(n,)).tolist()
|
|
dim1 = [1*2048]
|
|
dim2 = [12288]
|
|
#dim1 = [32]
|
|
#dim2 = [32]
|
|
#dtype = [torch.float16, torch.int8]
|
|
dtype = [torch.float16]
|
|
out_function = ['zeros', 'ones']
|
|
values = list(product(dim1,dim2, dtype, out_function))
|
|
names = ['dim1_{0}_dim2_{1}_dtype_{2}_out_func_{3}'.format(*vals) for vals in values]
|
|
@pytest.mark.parametrize("dim1, dim2, dtype, out_func", values, ids=names)
|
|
def test_spmm_coo_very_sparse(dim1, dim2, dtype, out_func):
|
|
out_func = getattr(torch, out_func)
|
|
|
|
threshold = 3.3
|
|
#threshold = 2.8
|
|
#threshold = 0.0
|
|
A = torch.randn(dim1, dim2, device='cuda').half()
|
|
if dtype == torch.float16:
|
|
B = torch.randn(dim2, dim2*4, device='cuda').half()
|
|
torch.nn.init.xavier_uniform_(B)
|
|
else:
|
|
B = torch.randn(dim2, dim2*4, device='cuda').half()
|
|
torch.nn.init.xavier_uniform_(B)
|
|
B, SB = F.vectorwise_quant(B, quant_type='linear')
|
|
#B = torch.randint(-127, 127, size=(dim2, dim2*4), device='cuda').to(torch.int8)
|
|
|
|
print('')
|
|
idx = torch.abs(A) >= threshold
|
|
nnz = (idx == 1).sum().item()
|
|
rows, cols = torch.where(idx)
|
|
values = A[idx]
|
|
cooA = F.COOSparseTensor(A.shape[0], A.shape[1], nnz, rows.int(), cols.int(), values)
|
|
A2 = A*idx
|
|
out1 = torch.matmul(A2.half(), B.half())
|
|
out = out_func(out1.shape, dtype=torch.float16, device=out1.device)
|
|
out1 += out.clone()
|
|
out2 = F.spmm_coo_very_sparse(cooA, B, out=out)
|
|
#print(B)
|
|
#print(out1)
|
|
#print(out2)
|
|
p = 200/(2048*12288*4)
|
|
n = out1.numel()
|
|
count = math.ceil(p*n)
|
|
std = out1.std()
|
|
out1 /= std
|
|
out2 /= std
|
|
assert_all_approx_close(out1, out2.half(), rtol=0.01, atol=3.0e-2, count=count)
|
|
#assert_all_approx_close(out1, out2.half(), rtol=0.05, atol=0.01, count=count)
|
|
|
|
idx_col = torch.randint(0, A2.shape[-1], size=(15,))
|
|
|
|
#torch.testing.assert_allclose(out1, out2.half(), rtol=0.05, atol=0.001)
|
|
|
|
#Bt = torch.randn(dim2*4, dim2, device='cuda').half()
|
|
#torch.cuda.synchronize()
|
|
#t0 = time.time()
|
|
#print(A2.shape, B.shape)
|
|
#for i in range(100):
|
|
# #out3 = F.spmm_coo(cooA, Bt.t())
|
|
# #out2 = F.spmm_coo(cooA, B)
|
|
# #out2 = F.spmm_coo_very_sparse(cooA, B)
|
|
# #out1 = torch.matmul(A, Bt.t())
|
|
|
|
#torch.cuda.synchronize()
|
|
#print(time.time() - t0)
|
|
|
|
def test_layout():
|
|
a1 = torch.rand(16, 64, device='cuda', dtype=torch.float16)
|
|
a1 = torch.arange(16* 64, device='cuda').reshape(16, 64).byte()
|
|
a2, s2 = F.transform(a1, 'col_turing')
|
|
print(a2.shape)
|
|
|
|
print(a1.flatten()[8*64:8*64+32])
|
|
for i in range(4):
|
|
print(a2.flatten()[i*8*32:i*8*32+32], 0)
|
|
|
|
|
|
def test_coo2csr():
|
|
threshold = 1
|
|
A = torch.randn(128, 128).half().cuda()
|
|
idx = torch.abs(A) >= threshold
|
|
nnz = (idx == 1).sum().item()
|
|
rows, cols = torch.where(idx)
|
|
values = A[idx]
|
|
cooA = F.COOSparseTensor(A.shape[0], A.shape[1], nnz, rows.int(), cols.int(), values)
|
|
A2 = A*idx
|
|
csrA = F.coo2csr(cooA)
|
|
counts = csrA.rowptr[1:] - csrA.rowptr[:-1]
|
|
assert counts.numel() == A.shape[0]
|
|
|
|
torch.testing.assert_allclose(counts, (A2!=0).sum(1))
|
|
idx = (A2!=0)
|
|
torch.testing.assert_allclose(A2[idx], csrA.values)
|
|
|
|
|
|
def test_coo2csc():
|
|
threshold = 1
|
|
A = torch.randn(128, 128).half().cuda()
|
|
idx = torch.abs(A) >= threshold
|
|
nnz = (idx == 1).sum().item()
|
|
rows, cols = torch.where(idx)
|
|
values = A[idx]
|
|
cooA = F.COOSparseTensor(A.shape[0], A.shape[1], nnz, rows.int(), cols.int(), values)
|
|
A2 = A*idx
|
|
cscA = F.coo2csc(cooA)
|
|
counts = cscA.colptr[1:] - cscA.colptr[:-1]
|
|
assert counts.numel() == A.shape[1]
|
|
|
|
torch.testing.assert_allclose(counts, (A2!=0).sum(0))
|
|
# torch uses row-major -> use transpose to transfer to col-major
|
|
idx = (A2.t()!=0)
|
|
torch.testing.assert_allclose(A2.t()[idx], cscA.values)
|
|
|
|
|
|
|
|
n = 2
|
|
#dim1 = torch.randint(1,1*1024, size=(n,)).tolist()
|
|
#dim2 = torch.randint(1,4*1024, size=(n,)).tolist()
|
|
dim1 = [1*2048]
|
|
#dim2 = [12288]
|
|
dim2 = [2048]
|
|
#dim1 = [2]
|
|
#dim2 = [2]
|
|
dtype = [torch.int8]
|
|
values = list(product(dim1,dim2, dtype))
|
|
names = ['dim1_{0}_dim2_{1}_dtype_{2}'.format(*vals) for vals in values]
|
|
@pytest.mark.parametrize("dim1, dim2, dtype", values, ids=names)
|
|
def test_spmm_coo_dequant(dim1, dim2, dtype):
|
|
threshold = 6.0
|
|
#threshold = 2.8
|
|
#threshold = 0.0
|
|
A = torch.randn(dim1, dim2, device='cuda').half()
|
|
B = torch.empty(dim2, dim2*4, device='cuda', dtype=torch.float16)
|
|
torch.nn.init.xavier_uniform_(B)
|
|
Bt = B.t().contiguous()
|
|
|
|
|
|
CB, CBt, statsB, statsBt, coo_tensor = F.double_quant(B)
|
|
|
|
rowidx = torch.randint(0, A.shape[-1], size=(15,))
|
|
|
|
A[:, rowidx] = 8.0
|
|
|
|
idx = torch.abs(A) >= threshold
|
|
nnz = (idx == 1).sum().item()
|
|
rows, cols = torch.where(idx)
|
|
values = A[idx]
|
|
cooA = F.COOSparseTensor(A.shape[0], A.shape[1], nnz, rows.int(), cols.int(), values)
|
|
A2 = A*idx
|
|
out2 = F.spmm_coo_very_sparse(cooA, CBt, dequant_stats=statsBt)
|
|
out1 = torch.matmul(A2, B.half())
|
|
out3 = F.spmm_coo_very_sparse(cooA, CBt.half())
|
|
out3 = out3*statsBt.half()/127
|
|
|
|
values, counts = torch.unique(cooA.rowidx, return_counts=True)
|
|
offset = counts.cumsum(0).int()
|
|
max_count, max_idx = torch.sort(counts, descending=True)
|
|
print(torch.median(max_count.float()))
|
|
|
|
torch.testing.assert_allclose(out2, out3, rtol=0.05, atol=0.001)
|
|
|
|
p = 200/(2048*12288*4)
|
|
n = out1.numel()
|
|
count = math.ceil(p*n)
|
|
assert_all_approx_close(out1, out2, rtol=0.01, atol=3.0e-2, count=count)
|
|
|
|
|
|
|
|
#torch.cuda.synchronize()
|
|
#t0 = time.time()
|
|
#for i in range(100):
|
|
# out2 = F.spmm_coo_very_sparse(cooA, B)
|
|
#torch.cuda.synchronize()
|
|
#print('fp16', time.time() - t0)
|
|
|
|
torch.cuda.synchronize()
|
|
t0 = time.time()
|
|
for i in range(100):
|
|
out2 = F.spmm_coo(cooA, B)
|
|
torch.cuda.synchronize()
|
|
print('cusparse fp16', time.time() - t0)
|
|
|
|
torch.cuda.synchronize()
|
|
t0 = time.time()
|
|
for i in range(100):
|
|
out2 = F.spmm_coo_very_sparse(cooA, CBt)
|
|
torch.cuda.synchronize()
|
|
print('int8', time.time() - t0)
|
|
|
|
torch.cuda.synchronize()
|
|
t0 = time.time()
|
|
for i in range(100):
|
|
out2 = F.spmm_coo_very_sparse(cooA, CBt, dequant_stats=statsBt)
|
|
torch.cuda.synchronize()
|
|
print('int8+dequant', time.time() - t0)
|
|
|
|
torch.cuda.synchronize()
|
|
t0 = time.time()
|
|
for i in range(100):
|
|
out2 = torch.matmul(A, B)
|
|
torch.cuda.synchronize()
|
|
print('matmul', time.time() - t0)
|
|
|
|
torch.cuda.synchronize()
|
|
t0 = time.time()
|
|
for i in range(100):
|
|
out1 = bnb.matmul(A, Bt)
|
|
out2 = F.spmm_coo_very_sparse(cooA, CBt, dequant_stats=statsBt)
|
|
out = out1+out2
|
|
torch.cuda.synchronize()
|
|
print('sparse+ matmul', time.time() - t0)
|
|
|
|
torch.cuda.synchronize()
|
|
t0 = time.time()
|
|
for i in range(100):
|
|
out1 = bnb.matmul(A, Bt)
|
|
torch.matmul(A[:, rowidx], Bt.t()[rowidx], out=out1)
|
|
torch.cuda.synchronize()
|
|
print('partial matmul', time.time() - t0)
|
|
|
|
torch.cuda.synchronize()
|
|
t0 = time.time()
|
|
for i in range(100):
|
|
out1 = bnb.matmul(A, Bt)
|
|
torch.cuda.synchronize()
|
|
print('partial matmul', time.time() - t0)
|
|
|
|
batch_size = 1
|
|
seqdim = 2048
|
|
values = []
|
|
values.append((batch_size, seqdim, 768, 4*768))
|
|
#values.append((batch_size, seqdim, 1024, 4*1024))
|
|
#values.append((batch_size, seqdim, 1536, 4*1536))
|
|
#values.append((batch_size, seqdim, 2048, 4*2048))
|
|
#values.append((batch_size, seqdim, 2560, 4*2560))
|
|
#values.append((batch_size, seqdim, 4096, 4*4096))
|
|
#values.append((batch_size, seqdim, 5140, 4*5140))
|
|
#values.append((batch_size, seqdim, 12288, 4*12288))
|
|
names = ['batch_{0}_seq_{1}_model_{2}_hidden_{3}'.format(*vals) for vals in values]
|
|
@pytest.mark.parametrize("batch, seq, model, hidden", values, ids=names)
|
|
def test_bench_matmul(batch, seq, model, hidden):
|
|
formatB = F.get_special_format_str()
|
|
|
|
A = torch.randn(batch, seq, model, device='cuda').half()
|
|
B = torch.empty(hidden, model, dtype=torch.float16, device='cuda')
|
|
torch.nn.init.xavier_uniform_(B)
|
|
|
|
linear8bit = bnb.nn.Linear8bitLt(model, hidden, False).cuda().half()
|
|
linear8bit.eval()
|
|
|
|
outliers = torch.randint(0, model, size=(5,)).cuda()
|
|
A[:, :, outliers] = 8.0
|
|
|
|
linearMixedBit = bnb.nn.Linear8bitLt(model, hidden, False, threshold=6.0).cuda().half()
|
|
linearMixedBit.eval()
|
|
|
|
# warmup
|
|
for i in range(100):
|
|
torch.matmul(A, B.t())
|
|
torch.cuda.synchronize()
|
|
print('')
|
|
|
|
torch.cuda.synchronize()
|
|
t0 = time.time()
|
|
for i in range(100):
|
|
torch.matmul(A, B.t())
|
|
torch.cuda.synchronize()
|
|
print(f'pytorch: [{batch},{seq},{model}], [{model},{hidden}]->[{batch},{seq},{hidden}]: {time.time()-t0:.4f}s')
|
|
|
|
torch.cuda.synchronize()
|
|
t0 = time.time()
|
|
for i in range(100):
|
|
bnb.matmul(A, B)
|
|
torch.cuda.synchronize()
|
|
print(f'bnb lt: [{batch},{seq},{model}], [{model},{hidden}]->[{batch},{seq},{hidden}]: {time.time()-t0:.4f}s')
|
|
|
|
CA, CAt, SCA, SCAt, coo_tensorA = F.double_quant(A, threshold=0.0)
|
|
C32A, SA = F.transform(CA, 'col32')
|
|
CB, CBt, SCB, SCBt, coo_tensorB = F.double_quant(B)
|
|
CxB, SB = F.transform(CB, to_order=formatB)
|
|
torch.cuda.synchronize()
|
|
t0 = time.time()
|
|
for i in range(100):
|
|
out32, Sout32 = F.igemmlt(C32A, CxB, SA, SB)
|
|
torch.cuda.synchronize()
|
|
print(f'igemmlt: [{batch},{seq},{model}], [{model},{hidden}]->[{batch},{seq},{hidden}]: {time.time()-t0:.4f}s')
|
|
|
|
BA, statsB = F.vectorwise_quant(B, dim=1)
|
|
CxB, SB = F.nvidia_transform(CB, to_order=formatB)
|
|
torch.cuda.synchronize()
|
|
t0 = time.time()
|
|
for i in range(100):
|
|
A2 = A.view(-1, A.shape[-1]).contiguous()
|
|
CA, statsA = F.vectorwise_quant(A2, dim=1)
|
|
C32A, SA = F.nvidia_transform(CA, 'col32')
|
|
out32, Sout32 = F.igemmlt(C32A, CxB, SA, SB)
|
|
Cout, Sout = F.nvidia_transform(out32, 'row', state=Sout32)
|
|
F.vectorwise_mm_dequant(Cout, statsA, statsB.t())
|
|
torch.cuda.synchronize()
|
|
print(f'vector pytorch + nvidia: [{batch},{seq},{model}], [{model},{hidden}]->[{batch},{seq},{hidden}]: {time.time()-t0:.4f}s')
|
|
|
|
BA, statsB = F.vectorwise_quant(B, dim=1, quant_type='linear')
|
|
CxB, SB = F.nvidia_transform(CB, to_order=formatB)
|
|
torch.cuda.synchronize()
|
|
t0 = time.time()
|
|
for i in range(100):
|
|
A2 = A.view(-1, A.shape[-1]).contiguous()
|
|
CA, statsA = F.vectorwise_quant(A2, dim=1, quant_type='linear')
|
|
C32A, SA = F.nvidia_transform(CA, 'col32')
|
|
out32, Sout32 = F.igemmlt(C32A, CxB, SA, SB)
|
|
Cout, Sout = F.nvidia_transform(out32, 'row', state=Sout32)
|
|
out = Cout*statsB*statsA*(1.0/(127*127))
|
|
torch.cuda.synchronize()
|
|
print(f'linear pytorch + nvidia: [{batch},{seq},{model}], [{model},{hidden}]->[{batch},{seq},{hidden}]: {time.time()-t0:.4f}s')
|
|
|
|
linear8bit(A)
|
|
torch.cuda.synchronize()
|
|
t0 = time.time()
|
|
for i in range(100):
|
|
linear8bit(A)
|
|
torch.cuda.synchronize()
|
|
print(f'bnb linear8bitlt: [{batch},{seq},{model}], [{model},{hidden}]->[{batch},{seq},{hidden}]: {time.time()-t0:.4f}s')
|
|
|
|
|
|
linearMixedBit(A)
|
|
torch.cuda.synchronize()
|
|
t0 = time.time()
|
|
for i in range(100):
|
|
linearMixedBit(A)
|
|
torch.cuda.synchronize()
|
|
print(f'bnb linear8bitlt with threshold: [{batch},{seq},{model}], [{model},{hidden}]->[{batch},{seq},{hidden}]: {time.time()-t0:.4f}s')
|
|
|
|
|
|
def test_zeropoint():
|
|
def min_max(x):
|
|
maxA = torch.amax(x, dim=1, keepdim=True)
|
|
minA = torch.amin(x, dim=1, keepdim=True)
|
|
midpoint = (maxA-minA)/2.0
|
|
dyna = 252/(maxA-minA)
|
|
#dyna *= 0.98
|
|
x = dyna*x
|
|
x = x - torch.round((dyna*(minA+midpoint)))
|
|
return x.to(torch.int8), minA, midpoint, dyna
|
|
batch = 2
|
|
seq = 2
|
|
model = 4
|
|
hidden = 2*model
|
|
#batch = 4
|
|
#seq = 2048
|
|
#model = 1024
|
|
#hidden = 8*model
|
|
A = torch.randn(batch*seq, model, device='cuda').half()-0.4
|
|
B = torch.nn.Parameter(torch.randn(model, hidden, device='cuda').half())
|
|
|
|
#A[0] = 0
|
|
#B[:, 0] = 0
|
|
#A = A*(A>0)
|
|
#A[0, 0] = 0
|
|
#A[0, 0] = 6.0
|
|
|
|
Ac, minA, midpoint, dyna = min_max(A)
|
|
#print(Ac[0, 0], 'zero')
|
|
#print(Ac, Ac.min(), Ac.max())
|
|
Bc, maxB = F.vectorwise_quant(B, quant_type='linear')
|
|
out = F.igemm(Ac, Bc)
|
|
out2 = torch.matmul(A,B)
|
|
offset = B.sum(0)*torch.round(dyna*(minA+midpoint))/dyna
|
|
out = out.float()
|
|
#print(out.shape, maxB.shape, scale.shape, offset.shape)
|
|
norm1 = maxB/127
|
|
C4 = (out/dyna)*norm1+offset
|
|
|
|
|
|
B1 = torch.nn.Parameter(B.clone())
|
|
B2 = torch.nn.Parameter(B.clone())
|
|
B3 = torch.nn.Parameter(B.clone())
|
|
B4 = torch.nn.Parameter(B.clone())
|
|
|
|
|
|
C1 = torch.matmul(A, B1)
|
|
C2 = bnb.matmul_cublas(A, B2, None, 'linear')
|
|
C3 = bnb.matmul_cublas(A, B3, None, 'zeropoint')
|
|
C4 = bnb.matmul_cublas(A, B4, None, 'vector-zeropoint')
|
|
|
|
err1 = torch.abs(C1-C2).mean().item()
|
|
err2 = torch.abs(C1-C3).mean().item()
|
|
err3 = torch.abs(C1-C4).mean().item()
|
|
print(err1, err2, err3)
|
|
#assert err1 > err2
|
|
|
|
loss1 = C1.mean()
|
|
loss2 = C2.mean()
|
|
loss3 = C3.mean()
|
|
loss4 = C4.mean()
|
|
|
|
loss1.backward()
|
|
loss2.backward()
|
|
loss3.backward()
|
|
loss4.backward()
|
|
|
|
print(B.grad)
|
|
print(B1.grad)
|
|
print(B2.grad)
|
|
print(B3.grad)
|
|
print(B4.grad)
|
|
err1 = torch.abs(B1.grad-B2.grad).mean().item()
|
|
err2 = torch.abs(B1.grad-B3.grad).mean().item()
|
|
err3 = torch.abs(B1.grad-B4.grad).mean().item()
|
|
print(err1, err2, err3)
|
|
|
|
|
|
|
|
|
|
def test_zp():
|
|
def quant_zp(x):
|
|
dtype = x.dtype
|
|
x = x.float()
|
|
dyna = x.max() - x.min()
|
|
if dyna == 0: dyna = 1
|
|
qx = 254./dyna
|
|
minx = x.min()
|
|
#zpx = torch.round(minx* qx)
|
|
#zpx = 127 - torch.round(x.max()* qx)
|
|
zpx = torch.round(x.min()* qx) - 127
|
|
x = (qx*x) + zpx
|
|
return x, qx, zpx
|
|
batch = 2
|
|
seq = 512
|
|
model = 1024
|
|
hidden = 4*model
|
|
A = torch.randn(batch*seq, model, device='cuda').half()*0.1
|
|
B = torch.randn(model, hidden, device='cuda').half()*0.1
|
|
|
|
|
|
C0 = torch.matmul(A, B)
|
|
|
|
|
|
#A, SA = F.vectorwise_quant(A, quant_type='linear')
|
|
#B, SB = F.vectorwise_quant(B, quant_type='linear')
|
|
A = A.float()
|
|
B = B.float()
|
|
|
|
C1 = torch.matmul(A, B)
|
|
C3 = bnb.matmul(A.half(), B.t().contiguous().half())
|
|
|
|
zp = 1
|
|
#C2 = torch.matmul(A-zp, B)
|
|
#C2 += B.sum(0).view(1, -1)*zp
|
|
C2 = torch.matmul(A, B-zp)
|
|
C2 -= A.sum(1).view(-1, 1)*zp
|
|
|
|
ca, cqa, cza = quant_zp(A)
|
|
print(ca.min(), ca.max())
|
|
print((ca-cza).min(), (ca-cza).max())
|
|
|
|
zp = 1
|
|
scale = 2.0
|
|
C5 = torch.matmul((A*scale)-zp, B)
|
|
C5 += B.sum(0)*zp
|
|
C5 /= scale
|
|
|
|
CA, qa, zpa = quant_zp(A)
|
|
C4 = torch.matmul(CA, B)
|
|
C4 -= B.sum(0)*zpa
|
|
C4 /= qa
|
|
|
|
zpb = 1
|
|
zpa = 1
|
|
qa = 2
|
|
qb = 2
|
|
C6 = torch.matmul((A*qa)+zpa, (B*qb)+zpb)
|
|
C6 -= (qb*B.sum(0).view(1, -1)*zpa) + (qa*A.sum(1).view(-1, 1)*zpb)
|
|
C6 -= zpa*zpb*A.shape[1]
|
|
C6 /= qa*qb
|
|
|
|
CA, qa, zpa = quant_zp(A)
|
|
CB, qb, zpb = quant_zp(B)
|
|
C7 = torch.matmul(CA, CB)
|
|
C7 -= (qb*B.sum(0).view(1, -1)*zpa) + (qa*A.sum(1).view(-1, 1)*zpb)
|
|
C7 -= zpa*zpb*A.shape[1]
|
|
C7 /= qa*qb
|
|
|
|
print('')
|
|
#print(C0.flatten()[:10])
|
|
print(C1.flatten()[:10])
|
|
print(C2.flatten()[:10])
|
|
print(C3.flatten()[:10])
|
|
print(C5.flatten()[:10])
|
|
print(C6.flatten()[:10])
|
|
print(C7.flatten()[:10])
|
|
err1 = torch.abs(C1-C2).mean().item()
|
|
err2 = torch.abs(C1-C3).mean().item()
|
|
err3 = torch.abs(C1-C4).mean().item()
|
|
err4 = torch.abs(C1-C5).mean().item()
|
|
err5 = torch.abs(C1-C6).mean().item()
|
|
err6 = torch.abs(C1-C7).mean().item()
|
|
print(err1, err2, err3, err4, err5, err6)
|
|
|
|
|
|
|
|
def test_extract_outliers():
|
|
shapeA = (128, 128)
|
|
idx = torch.randint(0, shapeA[1], size=(10,)).int()
|
|
A = torch.randint(-128, 127, size=shapeA, device='cuda').to(torch.int8)
|
|
outliers1 = A[:, idx.long()]
|
|
|
|
CA, SA = F.transform(A, 'col_turing')
|
|
|
|
outliers2 = F.extract_outliers(CA, SA, idx)
|
|
|
|
assert outliers2.shape[0] == shapeA[0]
|
|
assert outliers2.shape[1] == idx.numel()
|
|
|
|
|
|
|
|
torch.testing.assert_allclose(outliers1, outliers2)
|