import pytest import math import random import time import torch import bitsandbytes as bnb import einops from itertools import product from bitsandbytes import functional as F torch.set_printoptions(precision=4, sci_mode=False, linewidth=120, edgeitems=20, threshold=10000) k = 20 def assert_all_approx_close(a, b, rtol, atol, count): idx = torch.isclose(a, b, rtol, atol) sumval = (idx==0).sum().item() if sumval > count: print(f'Too many values not close: assert {sumval} < {count}') torch.testing.assert_allclose(a, b, rtol, atol) class FFN(torch.nn.Module): def __init__(self, input_features, hidden_size, bias=True): super(FFN, self).__init__() self.fc1 = torch.nn.Linear(input_features, hidden_size, bias=bias) self.fc2 = torch.nn.Linear(hidden_size, input_features, bias=bias) with torch.no_grad(): torch.nn.init.xavier_uniform_(self.fc1.weight) torch.nn.init.xavier_uniform_(self.fc2.weight) def forward(self, x): x = torch.relu(self.fc1(x)) x = self.fc2(x) return x class Timer(object): def __init__(self): self.starts = {} self.ends = {} self.agg = {} def tick(self, name='default'): if name not in self.starts: self.starts[name] = torch.cuda.Event(enable_timing=True) self.ends[name] = torch.cuda.Event(enable_timing=True) self.starts[name].record() else: ms = self.tock(name, evict=True, print_ms=False) def tock(self, name='default', evict=True, print_ms=True): if name in self.ends: self.ends[name].record() torch.cuda.synchronize() ms = self.starts[name].elapsed_time(self.ends[name]) if name not in self.agg: self.agg[name] = 0.0 self.agg[name] += ms if evict: self.starts.pop(name) self.ends.pop(name) if print_ms and name in self.agg: print('{0} took: {1:.5f}s'.format(name, self.agg[name]/1000.0)) return self.agg[name] def reset(self): self.starts = {} self.ends = {} self.agg = {} print('Resetting benchmark data') def setup(): pass def teardown(): pass @pytest.mark.parametrize("dtype", [torch.float32, torch.float16], ids=['float', 'half']) def test_estimate_quantiles(dtype): A = torch.rand(1024, 1024, device='cuda') A = A.to(dtype) code = F.estimate_quantiles(A) percs = torch.linspace(1/512, 511/512, 256, device=A.device) torch.testing.assert_allclose(percs, code, atol=1e-3, rtol=1e-2) A = torch.randn(1024, 1024, device='cuda') A = A.to(dtype) code = F.estimate_quantiles(A) quantiles = torch.quantile(A.float(), percs) diff = torch.abs(code-quantiles) assert (diff > 5e-02).sum().item() == 0 def test_quantile_quantization(): for i in range(100): A1 = torch.randn(1024, 1024, device='cuda') code = F.estimate_quantiles(A1) C = F.quantize_no_absmax(A1, code) A2 = F.dequantize_no_absmax(C, code) diff = torch.abs(A1-A2).mean().item() assert diff < 0.0075 A1 = torch.rand(1024, 1024, device='cuda') code = F.estimate_quantiles(A1) C = F.quantize_no_absmax(A1, code) A2 = F.dequantize_no_absmax(C, code) diff = torch.abs(A1-A2).mean().item() torch.testing.assert_allclose(A1, A2, atol=5e-3, rtol=0) assert diff < 0.001 def test_dynamic_quantization(): diffs = [] reldiffs = [] for i in range(100): A1 = torch.randn(1024, 1024, device='cuda') C, S = F.quantize(A1) A2 = F.dequantize(C, S) diff = torch.abs(A1-A2) reldiff = diff/torch.abs(A1+1e-8) diffs.append(diff.mean().item()) reldiffs.append(reldiff.mean().item()) assert diff.mean().item() < 0.0135 #print(sum(diffs)/len(diffs)) #print(sum(reldiffs)/len(reldiffs)) for i in range(100): A1 = torch.rand(1024, 1024, device='cuda') C, S = F.quantize(A1) A2 = F.dequantize(C, S) diff = torch.abs(A1-A2).mean().item() torch.testing.assert_allclose(A1, A2, atol=1e-2, rtol=0) assert diff < 0.004 def test_dynamic_blockwise_quantization(): diffs = [] reldiffs = [] for i in range(100): A1 = torch.randn(1024, 1024, device='cuda') C, S = F.quantize_blockwise(A1) A2 = F.dequantize_blockwise(C, S) diff = torch.abs(A1-A2) reldiff = diff/torch.abs(A1+1e-8) diffs.append(diff.mean().item()) reldiffs.append(reldiff.mean().item()) assert diffs[-1] < 0.011 #print(sum(diffs)/len(diffs)) #print(sum(reldiffs)/len(reldiffs)) diffs = [] for i in range(100): A1 = torch.rand(1024, 1024, device='cuda') C, S = F.quantize_blockwise(A1) A2 = F.dequantize_blockwise(C, S) diff = torch.abs(A1-A2).mean().item() assert diff < 0.0033 diffs.append(diff) torch.testing.assert_allclose(A1, A2, atol=1e-2, rtol=0) #print(sum(diffs)/len(diffs)) def test_dynamic_blockwise_stochastic_quantization(): diffs = [] reldiffs = [] rand = torch.rand(1024).cuda() for i in range(100): A1 = torch.randn(1024, 1024, device='cuda') C1, S1 = F.quantize_blockwise(A1, rand=rand) C2, S2 = F.quantize_blockwise(A1) # a maximunm distance of quantized values of 1 torch.testing.assert_allclose(C1, C2, atol=1, rtol=0) fraction_smaller = (C1C2).float().sum()/C1.numel() torch.testing.assert_allclose(fraction_larger, fraction_smaller, atol=0.01, rtol=0) @pytest.mark.parametrize("gtype", [torch.float32, torch.float16], ids=['float', 'half']) def test_percentile_clipping(gtype): gnorm_vec1 = torch.zeros(100, device='cuda') gnorm_vec2 = torch.zeros(100, device='cuda') n = 4 step = 0 percentile=5 for i in range(k): step += 1 g = torch.randn(n, n, dtype=gtype, device='cuda') gnorm1, clip2, gnorm_scale = F.percentile_clipping(g, gnorm_vec2, step, percentile=percentile) assert gnorm_scale == 1.0 if gnorm1 < clip2 else clip2/gnorm1 gnorm2 = torch.norm(g.float()) if step == 1: gnorm_vec1[:] = gnorm2 else: gnorm_vec1[step % 100] = gnorm2 vals, idx = torch.sort(gnorm_vec1) clip1 = vals[percentile] torch.testing.assert_allclose(gnorm_vec1, torch.sqrt(gnorm_vec2)) torch.testing.assert_allclose(clip1, clip2) torch.testing.assert_allclose(gnorm1, gnorm2) def quant(x): max1 = torch.abs(x).max() x = torch.round(x/max1*127) return max1, x.to(torch.int8) def dequant(c, maxC): return c.float()*(maxC/127) def mm_dequant(maxA, maxB, C): return C.float()*(maxA/127)*(maxB/127) def quant_multi(x, dim): max1 = torch.amax(torch.abs(x), dim=dim, keepdim=True) max1[max1==0] = 1.0 x = torch.round(x/max1*127) return max1, x.to(torch.int8) def quant_multi_chunk(x, dim, chunk_size=32): if dim==1: x_chunked = einops.rearrange(x, '(c a) b -> c a b', c=chunk_size) max1 = torch.amax(torch.abs(x_chunked), dim=dim+1, keepdim=True) max1 = torch.tile(max1, (1, 1, x.shape[1])) max1 = max1.view(x.shape) elif dim==0: x_chunked = einops.rearrange(x, 'a (b c) -> a b c', c=chunk_size) max1 = torch.amax(torch.abs(x_chunked), dim=dim, keepdim=True) max1 = torch.tile(max1, (x.shape[0], 1, 1)) max1 = max1.view(x.shape) max1[max1==0] = 1.0 x = torch.round(x/max1*127) return max1, x.to(torch.int8) def quant_minmax(A): minA = A.min() maxA = A.max() def mean(xx): return sum(xx)/float(len(xx)) #dim1 = torch.randint(1,1024*4, size=(4,)).tolist() #dim2 = torch.randint(1,1024*4, size=(4,)).tolist() dim1 = [1024*2] dim2 = [1024*16] methods = [(lambda x, dim: quant(x), lambda x, dim: quant(x), dequant, dequant, mm_dequant)] methods.append((quant_multi, quant_multi, dequant, dequant, mm_dequant)) #methods.append((lambda x: quant_multi_chunk(x, dim=-1), lambda x: quant_multi_chunk(x, dim=0), dequant, dequant, mm_dequant)) method_names = ['linear', 'vectorwise'] batched = [False, True] values = list(product(dim1,dim2, methods, batched)) values_names = list(product(dim1,dim2, method_names, batched)) names = ['dim1_{0}_dim2_{1}_quant_{2}_batched_{3}'.format(*vals) for vals in values_names] @pytest.mark.parametrize("dim1, dim2, quant_methods, batched", values, ids=names) def test_approx_igemm(dim1, dim2, quant_methods, batched): dim1 = dim1 - (dim1 % 32) dim2 = dim2 - (dim2 % 32) errors = [] relerrors = [] print('') for i in range(5): if batched: A = torch.normal(0, 0.5, size=(32, dim1, dim2//32), device='cuda') B = torch.normal(0, 0.5, size=(32, dim2//32, dim1), device='cuda') maxA, Ac = quant_methods[0](A, 2) maxB, Bc = quant_methods[1](B, 1) else: A = torch.normal(0, 0.5, size=(dim1, dim2), device='cuda') B = torch.normal(0, 0.5, size=(dim2, dim1), device='cuda') maxA, Ac = quant_methods[0](A, 1) maxB, Bc = quant_methods[1](B, 0) torch.testing.assert_allclose(quant_methods[2](maxA, Ac), A, atol=0.025, rtol=0.05) if batched: out2 = torch.bmm(A, B) C = torch.bmm(Ac.float(), Bc.float()) else: out2 = torch.mm(A, B) C = F.igemm(Ac, Bc) out = quant_methods[4](maxA, maxB, C) std = out2.std() out/= std out2/= std err = torch.abs(out-out2) relerr = err/torch.abs(out2) errors.append(err.mean().item()) relerrors.append(relerr.mean().item()) print(mean(errors)) print(mean(relerrors)) def test_stable_embedding(): layer = bnb.nn.StableEmbedding(1024, 1024) layer.reset_parameters() n = 2 hidden_dim = torch.randint(32,256, size=(n,)).tolist() batch_dim = torch.randint(16,256, size=(n,)).tolist() seq_dim = torch.randint(16,256, size=(n,)).tolist() transpose = [(False, False), (False, True), (True, False), (True, True)] values = list(product(hidden_dim,batch_dim, transpose, seq_dim)) names = ['hidden_dim_{0}_batch_dim_{1},transpose_{2}_seq_dim_{3}'.format(*vals) for vals in values] @pytest.mark.parametrize("hidden_dim, batch_dim, transpose, seq_dim", values, ids=names) def test_igemm(hidden_dim, batch_dim, transpose, seq_dim): hidden_dim = hidden_dim - (hidden_dim % 32) batch_dim = batch_dim - (batch_dim % 16) seq_dim = seq_dim - (seq_dim % 16) for i in range(k): shapeA = (batch_dim, hidden_dim) if not transpose[0] else (hidden_dim, batch_dim) shapeB = ((32*random.randint(1, 4), hidden_dim) if transpose[1] else (hidden_dim, 32*random.randint(1, 4))) 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.matmul(A.float(), B.float()) out = F.igemm(A, B) elif not transpose[0] and transpose[1]: out2 = torch.matmul(A.float(), B.t().float()) out = F.igemm(A, B.t()) elif transpose[0] and not transpose[1]: out2 = torch.matmul(A.t().float(), B.float()) out = F.igemm(A.t(), B) elif transpose[0] and transpose[1]: out2 = torch.matmul(A.t().float(), B.t().float()) out = F.igemm(A.t(), B.t()) torch.testing.assert_allclose(out.float(), out2) for i in range(k): shapeA = (batch_dim, seq_dim, hidden_dim) shapeB = ((32*random.randint(1, 4), hidden_dim) if transpose[1] else (hidden_dim, 32*random.randint(1, 4))) 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.matmul(A.float(), B.float()) out = F.igemm(A, B) elif not transpose[0] and transpose[1]: out2 = torch.matmul(A.float(), B.t().float()) out = F.igemm(A, B.t()) torch.testing.assert_allclose(out.float(), out2) n = 3 seq_dim = torch.randint(32,512, size=(n,)).tolist() hidden_dim = torch.randint(32,1024*4, size=(n,)).tolist() batch_dim = torch.randint(2,16, size=(n,)).tolist() values = list(product(seq_dim,hidden_dim,batch_dim)) names = ['seq_dim{0}_hidden_dim{1}_batch_dim{2}'.format(*vals) for vals in values] @pytest.mark.parametrize("seq_dim, hidden_dim, batch_dim", values, ids=names) def test_dim3_igemm(seq_dim, hidden_dim, batch_dim): seq_dim = seq_dim - (seq_dim % 32) hidden_dim = hidden_dim - (hidden_dim % 32) batch_dim = batch_dim - (batch_dim % 2) for i in range(25): A = torch.randint(-128, 127, size=(batch_dim, seq_dim, hidden_dim), device='cuda').to(torch.int8) B = torch.randint(-128, 127, size=(batch_dim, seq_dim, 1024), device='cuda').to(torch.int8) out2 = torch.einsum('bsi, bso->io', A.float(), B.float()) iout = torch.empty(A.shape[2], B.shape[2], dtype=torch.int32, device=A.device) out = F.igemm(A, B, out=iout) torch.testing.assert_allclose(out.float(), out2) n = 2 seq_dim = torch.randint(32,512, size=(n,)).tolist() hidden_dim = torch.randint(32,1024*4, size=(n,)).tolist() batch_dim = torch.randint(2,16, size=(n,)).tolist() transpose = [False, True] values = list(product(seq_dim,hidden_dim,batch_dim, transpose)) names = ['seq_dim={0}_hidden_dim={1}_batch_dim={2}_transpose{3}'.format(*vals) for vals in values] @pytest.mark.parametrize("seq_dim, hidden_dim, batch_dim, transpose", values, ids=names) def test_minmax_igemm(seq_dim, hidden_dim, batch_dim, transpose): def min_max(x): maxA = torch.amax(x, dim=2, keepdim=True) minA = torch.amin(x, dim=2, keepdim=True) scale = (maxA-minA)/2.0 return (127*(x-minA-scale)/scale).to(torch.int8), minA, scale seq_dim = seq_dim - (seq_dim % 16) hidden_dim = hidden_dim - (hidden_dim % 16) batch_dim = batch_dim - (batch_dim % 2) errs = [] relerrs = [] errs2 = [] relerrs2 = [] for i in range(k): A = torch.normal(0.0, 0.5, size=(batch_dim, seq_dim, hidden_dim), device='cuda') if transpose: B = torch.normal(0, 0.5, size=(256, hidden_dim), device='cuda') else: B = torch.normal(0, 0.5, size=(hidden_dim, 256), device='cuda') Ac, minA, scale = min_max(A) if transpose: maxB, Bc = quant_multi(B, dim=(1 if transpose else 0)) 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) @pytest.mark.skip("Row scale has some bugs for ampere") 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) @pytest.mark.skip("Row scale has some bugs for ampere") 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() print(formatB) 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(): for i in range(k): shapeA = (4096, 4096*4) idx = torch.unique(torch.randint(0, shapeA[1], size=(10,)).int()).cuda() #idx = torch.Tensor([0]).int().cuda() 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) CA, SA = F.transform(A, 'col_ampere') 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)