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import pytest
import torch
import bitsandbytes as bnb
from itertools import product
n = 1
k = 25
dim1 = torch.randint(16,64, size=(n,)).tolist()
dim2 = torch.randint(32,96, size=(n,)).tolist()
dim3 = torch.randint(32,96, size=(n,)).tolist()
dim4 = torch.randint(32,96, size=(n,)).tolist()
funcs = [(torch.bmm, bnb.bmm_cublas), (torch.matmul, bnb.matmul_cublas)]
str_funcs = ['bmm', 'matmul']
req_grad = [(False, False), (True, False), (True, True), (False, True)]
req_grad_str = ['FF', 'TF', 'TT', 'FT']
transpose = [(False, False), (False, True), (True, True), (True, False)]
str_transpose = ['FF', 'FT', 'TT', 'TF']
dtype = [torch.float32, torch.float16]
values = list(product(dim1,dim2,dim3,dim4,funcs, dtype, req_grad, transpose))
str_values = list(product(dim1,dim2,dim3,dim4,str_funcs, dtype, req_grad_str, str_transpose))
names = ['dim1_{0}_dim2_{1}_dim3_{2}_dim4_{3}_func_{4}_dtype_{5}_requires_grad_{6}_transpose_{7}'.format(*vals) for vals in str_values]
@pytest.mark.parametrize("dim1, dim2, dim3, dim4, funcs, dtype, req_grad, transpose", values, ids=names)
def test_matmul(dim1, dim2, dim3, dim4, funcs, dtype, req_grad, transpose):
dim2 = dim2 - (dim2 % 16)
dim3 = dim3 - (dim3 % 16)
dim4 = dim4 - (dim4 % 16)
for i in range(k):
# normal multiply
if funcs[0] in [torch.mm, torch.matmul]:
dimA = (dim2, dim3) if not transpose[0] else (dim3, dim2)
dimB = (dim3, dim4) if not transpose[1] else (dim4, dim3)
A = torch.randn(size=dimA, device='cuda', requires_grad=req_grad[0])
B = torch.randn(size=dimB, device='cuda', requires_grad=req_grad[1])
target = torch.randn(size=(dim2, dim4), device='cuda', requires_grad=req_grad[1])
torch.nn.init.xavier_uniform_(B)
if not transpose[0] and not transpose[1]:
out_torch = funcs[0](A, B)
out_bnb = funcs[1](A, B)
elif not transpose[0] and transpose[1]:
out_torch = funcs[0](A, B.t())
out_bnb = funcs[1](A, B.t())
elif transpose[0] and not transpose[1]:
out_torch = funcs[0](A.t(), B)
out_bnb = funcs[1](A.t(), B)
elif transpose[0] and transpose[1]:
out_torch = funcs[0](A.t(), B.t())
out_bnb = funcs[1](A.t(), B.t())
n = out_bnb.numel()
idx = torch.isclose(out_bnb, out_torch, atol=0.01, rtol=0.1)
assert (idx==0).sum().item() < n*0.0175
idx = torch.isclose(out_bnb, out_torch, atol=0.035, rtol=0.2)
assert (idx==0).sum().item() < n*0.001
if any(req_grad):
out_bnb.data.copy_(out_torch)
torch.cuda.synchronize()
loss_bnb = torch.nn.functional.mse_loss(out_bnb, target).mean()
loss_bnb.backward()
gradA1 = A.grad
gradB1 = B.grad
A.grad = None
B.grad = None
loss_torch = torch.nn.functional.mse_loss(out_torch, target).mean()
loss_torch.backward()
gradA2 = A.grad
gradB2 = B.grad
A.grad = None
B.grad = None
if req_grad[0]:
torch.testing.assert_allclose(gradA1, gradA2, atol=0.015, rtol=0.1)
if req_grad[1]:
n = gradB1.numel()
idx = torch.isclose(gradB1, gradB2, atol=0.06, rtol=0.3)
assert (idx==0).sum().item() < n*0.1
idx = torch.isclose(gradB1, gradB2, atol=0.10, rtol=0.3)
assert (idx==0).sum().item() < n*0.02
torch.testing.assert_allclose(gradB1, gradB2, atol=0.18, rtol=0.3)
# batched matrix multiply
if funcs[0] in [torch.bmm, torch.matmul]:
A = torch.randn(size=(dim1, dim2, dim3), device='cuda', requires_grad=req_grad[0])
B = torch.randn(size=(dim1, dim3, dim4), device='cuda', requires_grad=req_grad[1])
target = torch.randn(size=(dim1, dim2, dim4), device='cuda', requires_grad=req_grad[1])
torch.nn.init.xavier_uniform_(B)
out_torch = funcs[0](A, B)
out_bnb = funcs[1](A, B)
n = out_bnb.numel()
idx = torch.isclose(out_bnb, out_torch, atol=0.01, rtol=0.1)
assert (idx==0).sum().item() < n*0.01
torch.testing.assert_allclose(out_bnb, out_torch, atol=0.027, rtol=0.2)
if any(req_grad):
out_bnb.data.copy_(out_torch)
torch.cuda.synchronize()
loss_bnb = torch.nn.functional.mse_loss(out_bnb, target).mean()
loss_bnb.backward()
gradA1 = A.grad
gradB1 = B.grad
A.grad = None
B.grad = None
loss_torch = torch.nn.functional.mse_loss(out_torch, target).mean()
loss_torch.backward()
gradA2 = A.grad
gradB2 = B.grad
A.grad = None
B.grad = None
if req_grad[0]:
torch.testing.assert_allclose(gradA1, gradA2, atol=0.015, rtol=0.1)
if req_grad[1]:
n = gradB1.numel()
idx = torch.isclose(gradB1, gradB2, atol=0.06, rtol=0.3)
assert (idx==0).sum().item() < n*0.1
idx = torch.isclose(gradB1, gradB2, atol=0.10, rtol=0.3)
assert (idx==0).sum().item() < n*0.02
if funcs[0] in [torch.matmul]:
dim1 = dim1 - (dim1 % 16)
A = torch.randn(size=(dim1, dim2, dim3), device='cuda', requires_grad=req_grad[0])
dimB = (dim4, dim3) if transpose[1] else (dim3, dim4)
B = torch.randn(size=dimB, device='cuda', requires_grad=req_grad[1])
target = torch.randn(size=(dim1, dim2, dim4), device='cuda', requires_grad=req_grad[1])
torch.nn.init.xavier_uniform_(B)
if transpose[1]:
out_torch = funcs[0](A, B.t())
out_bnb = funcs[1](A, B.t())
else:
out_torch = funcs[0](A, B)
out_bnb = funcs[1](A, B)
n = out_bnb.numel()
idx = torch.isclose(out_bnb, out_torch, atol=0.01, rtol=0.1)
assert (idx==0).sum().item() < n*0.0175
idx = torch.isclose(out_bnb, out_torch, atol=0.035, rtol=0.2)
assert (idx==0).sum().item() < n*0.001
if any(req_grad):
out_bnb.data.copy_(out_torch)
torch.cuda.synchronize()
loss_bnb = torch.nn.functional.mse_loss(out_bnb, target).mean()
loss_bnb.backward()
gradA1 = A.grad
gradB1 = B.grad
A.grad = None
B.grad = None
loss_torch = torch.nn.functional.mse_loss(out_torch, target).mean()
loss_torch.backward()
gradA2 = A.grad
gradB2 = B.grad
A.grad = None
B.grad = None
if req_grad[0]:
torch.testing.assert_allclose(gradA1, gradA2, atol=0.015, rtol=0.1)
if req_grad[1]:
n = gradB1.numel()
idx = torch.isclose(gradB1, gradB2, atol=0.06, rtol=0.3)
assert (idx==0).sum().item() < n*0.1
idx = torch.isclose(gradB1, gradB2, atol=0.10, rtol=0.3)
assert (idx==0).sum().item() < n*0.02
n = 1
k = 3
dim1 = torch.randint(16,64, size=(n,)).tolist()
dim2 = torch.randint(32,96, size=(n,)).tolist()
dim3 = torch.randint(32,96, size=(n,)).tolist()
dim4 = torch.randint(32,96, size=(n,)).tolist()
#dim1 = (17,)
#dim2 = (7,)
#dim3 = (37,)
#dim4 = (23,)
decomp = [0.0, 6.0]
funcs = [(torch.matmul, bnb.matmul)]
str_funcs = ['matmul']
req_grad = [(False, False), (True, False), (True, True), (False, True)]
req_grad_str = ['FF', 'TF', 'TT', 'FT']
transpose = [(False, True), (False, False)]
str_transpose = ['NT', 'NN']
dtype = [torch.float16]
has_fp16_weights = [True, False]
values = list(product(dim1,dim2,dim3,dim4,funcs, dtype, req_grad, transpose, decomp, has_fp16_weights))
str_values = list(product(dim1,dim2,dim3,dim4,str_funcs, dtype, req_grad_str, str_transpose, decomp, has_fp16_weights))
names = ['dim1_{0}_dim2_{1}_dim3_{2}_dim4_{3}_func_{4}_dtype_{5}_requires_grad_{6}_transpose_{7}_decomp_{8}_has_fp16_weights_{9}'.format(*vals) for vals in str_values]
@pytest.mark.parametrize("dim1, dim2, dim3, dim4, funcs, dtype, req_grad, transpose, decomp, has_fp16_weights", values, ids=names)
def test_matmullt(dim1, dim2, dim3, dim4, funcs, dtype, req_grad, transpose, decomp, has_fp16_weights):
dimA = (dim2, dim3) if not transpose[0] else (dim3, dim2)
dimB = (dim3, dim4) if not transpose[1] else (dim4, dim3)
outlier_dim = torch.randint(0, dimA[1], size=(dimA[1]//8,), device='cuda')
for i in range(k):
# normal multiply
if funcs[0] in [torch.mm, torch.matmul]:
A = torch.randn(size=dimA, device='cuda', requires_grad=req_grad[0], dtype=dtype)
if decomp == 6.0:
with torch.no_grad():
A[:, outlier_dim] = 6.0
B = torch.randn(size=dimB, device='cuda', requires_grad=req_grad[1], dtype=dtype)
target = torch.randn(size=(dim2, dim4), device='cuda', requires_grad=req_grad[1], dtype=dtype)
torch.nn.init.xavier_uniform_(B)
B2 = B.clone()
state = bnb.MatmulLtState()
state.threshold = decomp
state.has_fp16_weights = has_fp16_weights
if not has_fp16_weights:
if not transpose[0] and not transpose[1]: B2 = B2.t().contiguous()
state.CB, CBt, state.SCB, SCBt, coo_tensorB = bnb.functional.double_quant(B2)
B2 = state.CB
if not transpose[0] and transpose[1]:
out_torch = funcs[0](A, B.t())
out_bnb = funcs[1](A, B2, state=state)
elif not transpose[0] and not transpose[1]:
out_torch = funcs[0](A, B)
out_bnb = funcs[1](A, B2.t(), state=state)
n = out_bnb.numel()
err = torch.abs(out_bnb-out_torch).mean().item()
#print(f'abs error {err:.4f}')
idx = torch.isclose(out_bnb, out_torch, atol=0.01, rtol=0.1)
assert (idx==0).sum().item() < n*0.0175
idx = torch.isclose(out_bnb, out_torch, atol=0.035, rtol=0.2)
assert (idx==0).sum().item() < n*0.001
if has_fp16_weights:
if any(req_grad):
out_bnb.data.copy_(out_torch)
torch.cuda.synchronize()
loss_bnb = torch.nn.functional.mse_loss(out_bnb, target).mean()
loss_bnb.backward()
gradA1 = A.grad
gradB1 = B.grad
A.grad = None
B.grad = None
loss_torch = torch.nn.functional.mse_loss(out_torch, target).mean()
loss_torch.backward()
gradA2 = A.grad
gradB2 = B.grad
A.grad = None
B.grad = None
if req_grad[0]:
torch.testing.assert_allclose(gradA1, gradA2, atol=0.015, rtol=0.1)
if req_grad[1]:
n = gradB1.numel()
assert torch.abs(gradB1).sum() > 0.0
assert torch.abs(gradB2).sum() > 0.0
idx = torch.isclose(gradB1, gradB2, atol=0.06, rtol=0.3)
assert (idx==0).sum().item() < n*0.1
idx = torch.isclose(gradB1, gradB2, atol=0.10, rtol=0.3)
assert (idx==0).sum().item() < n*0.02
torch.testing.assert_allclose(gradB1, gradB2, atol=0.18, rtol=0.3)
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