diff options
Diffstat (limited to 'tests')
| -rw-r--r-- | tests/test_optim.py | 8 |
1 files changed, 6 insertions, 2 deletions
diff --git a/tests/test_optim.py b/tests/test_optim.py index fc2456f..ff0734b 100644 --- a/tests/test_optim.py +++ b/tests/test_optim.py @@ -39,6 +39,7 @@ str2optimizers['momentum'] = (lambda pxx: torch.optim.SGD(pxx, 0.01, 0.9), lambd str2optimizers['lars'] = (lambda pxx: bnb.optim.PytorchLARS(pxx, 0.01, 0.9), lambda pxx: bnb.optim.LARS(pxx, 0.01, 0.9)) str2optimizers['lamb'] = (lambda pxx: apex.optimizers.FusedLAMB(pxx, weight_decay=0.0, max_grad_norm=10000.0, eps=1e-8, use_nvlamb=True), bnb.optim.LAMB) str2optimizers['rmsprop'] = (lambda pxx: torch.optim.RMSprop(pxx, 0.01, 0.9), lambda pxx: bnb.optim.RMSprop(pxx, 0.01, 0.9, block_wise=False)) +str2optimizers['adagrad'] = (lambda pxx: torch.optim.Adagrad(pxx, 0.01), lambda pxx: bnb.optim.Adagrad(pxx, 0.01, block_wise=False)) str2optimizers['adam8bit'] = (torch.optim.Adam, lambda pxx: bnb.optim.Adam8bit(pxx, block_wise=False)) str2optimizers['momentum8bit'] = (lambda pxx: torch.optim.SGD(pxx, 0.01, 0.9), lambda pxx: bnb.optim.SGD8bit(pxx, 0.01, 0.9, block_wise=False)) str2optimizers['rmsprop8bit'] = (lambda pxx: torch.optim.RMSprop(pxx, 0.01, 0.9), lambda pxx: bnb.optim.RMSprop8bit(pxx, 0.01, 0.9, block_wise=False)) @@ -48,6 +49,7 @@ str2optimizers['lars8bit'] = (lambda pxx: bnb.optim.PytorchLARS(pxx, 0.01, 0.9), str2optimizers['adam8bit_blockwise'] = (torch.optim.Adam, lambda pxx: bnb.optim.Adam8bit(pxx, block_wise=True)) str2optimizers['momentum8bit_blockwise'] = (lambda pxx: torch.optim.SGD(pxx, 0.01, 0.9), lambda pxx: bnb.optim.SGD8bit(pxx, 0.01, 0.9, block_wise=True)) str2optimizers['rmsprop8bit_blockwise'] = (lambda pxx: torch.optim.RMSprop(pxx, 0.01, 0.9), lambda pxx: bnb.optim.RMSprop8bit(pxx, 0.01, 0.9, block_wise=True)) +str2optimizers['adagrad8bit_blockwise'] = (lambda pxx: torch.optim.Adagrad(pxx, 0.01), lambda pxx: bnb.optim.Adagrad8bit(pxx, 0.01, block_wise=True)) str2statenames = {} str2statenames['adam'] = [('exp_avg', 'state1'), ('exp_avg_sq', 'state2')] @@ -55,6 +57,7 @@ str2statenames['momentum'] = [('momentum_buffer', 'state1')] str2statenames['lars'] = [('momentum_buffer', 'state1')] str2statenames['lamb'] = [('exp_avg', 'state1'), ('exp_avg_sq', 'state2')] str2statenames['rmsprop'] = [('square_avg', 'state1')] +str2statenames['adagrad'] = [('sum', 'state1')] str2statenames['adam8bit'] = [('exp_avg', 'state1', 'qmap1', 'max1'), ('exp_avg_sq', 'state2', 'qmap2', 'max2')] str2statenames['lamb8bit'] = [('exp_avg', 'state1', 'qmap1', 'max1'), ('exp_avg_sq', 'state2', 'qmap2', 'max2')] str2statenames['adam8bit_blockwise'] = [('exp_avg', 'state1', 'qmap1', 'absmax1'), ('exp_avg_sq', 'state2', 'qmap2', 'absmax2')] @@ -63,11 +66,12 @@ str2statenames['momentum8bit_blockwise'] = [('momentum_buffer', 'state1', 'qmap1 str2statenames['lars8bit'] = [('momentum_buffer', 'state1', 'qmap1', 'max1')] str2statenames['rmsprop8bit'] = [('square_avg', 'state1', 'qmap1', 'max1')] str2statenames['rmsprop8bit_blockwise'] = [('square_avg', 'state1', 'qmap1', 'absmax1')] +str2statenames['adagrad8bit_blockwise'] = [('sum', 'state1', 'qmap1', 'absmax1')] dim1 = [1024] dim2 = [32, 1024, 4097, 1] gtype = [torch.float32, torch.float16] -optimizer_names = ['adam', 'momentum', 'rmsprop', 'lars', 'lamb'] +optimizer_names = ['adam', 'momentum', 'rmsprop', 'lars', 'lamb', 'adagrad'] values = list(product(dim1,dim2, gtype, optimizer_names)) names = ['dim1_{0}_dim2_{1}_gtype_{2}_optim_{3}'.format(*vals) for vals in values] @pytest.mark.parametrize("dim1, dim2, gtype, optim_name", values, ids=names) @@ -197,7 +201,7 @@ def test_global_config(dim1, dim2, gtype): dim1 = [1024] dim2 = [32, 1024, 4097] gtype = [torch.float32, torch.float16] -optimizer_names = ['adam8bit', 'momentum8bit', 'rmsprop8bit', 'adam8bit_blockwise', 'lamb8bit', 'lars8bit', 'momentum8bit_blockwise', 'rmsprop8bit_blockwise'] +optimizer_names = ['adam8bit', 'momentum8bit', 'rmsprop8bit', 'adam8bit_blockwise', 'lamb8bit', 'lars8bit', 'momentum8bit_blockwise', 'rmsprop8bit_blockwise', 'adagrad8bit_blockwise'] values = list(product(dim1,dim2, gtype, optimizer_names)) names = ['dim1_{0}_dim2_{1}_gtype_{2}_optim_{3}'.format(*vals) for vals in values] @pytest.mark.parametrize("dim1, dim2, gtype, optim_name", values, ids=names) |