We have moved float8_experimental to pytorch/ao
import float8_experimentalis nowimport torchao.float8
This is an early version of a library for accelerating training with float8 in native PyTorch according to the recipes laid out in https://arxiv.org/pdf/2209.05433.pdf. The codebase strives to stay small, easily hackable, debuggable with native PyTorch tooling, and composable with key systems such as autograd, torch.compile and distributed. With torch.compile on, initial results show throughput speedups of up to 1.2x on small scale (8 GPUs) LLaMa pretraining jobs.
pip install . # Optionally install editable pip install -e . # Optionally Install dev tooling pip install -e ".[dev]"We provide two per-tensor scaling strategies: dynamic and delayed. See https://arxiv.org/pdf/2209.05433.pdf, Section 4.3 for more details. These strategies are configurable separately for activations (input), weights (weight) and gradients (grad_output).
This is the most accurate recipe as every tensor is scaled dynamically.
from float8_experimental import ( convert_to_float8_training, precompute_float8_dynamic_scale_for_fsdp, ) # create model m = Model(...) # optional: filter modules from being eligible for float8 conversion def module_filter_fn(mod: torch.nn.Module, fqn: str): # don't convert the output module if fqn == "output": return False # don't convert linear modules with weight dimensions not divisible by 16 if isinstance(mod, torch.nn.Linear): if mod.in_features % 16 != 0 or mod.out_features % 16 != 0: return False return True # convert all `torch.nn.Linear` modules to `Float8Linear` convert_to_float8_training(m, module_filter_fn=module_filter_fn) # optional: use FSDP model = FSDP(model, use_orig_params=True) # optional: enable torch.compile for improved performance m = torch.compile(m) # toy training loop for _ in range(N_ITER): optimizer.zero_grad() y = m(x) y.sum().backward() optimizer.step() # specific to fsdp2 + dynamic scaling, when fp8 all-gather is turned on # this method is optional but is highly recommended for performance # it calcuclates scales for all parameters in a single all-reduce precompute_float8_dynamic_scale_for_fsdp(model)This is theoretically the most performant recipe as it minimizes memory reads.
from float8_experimental import ( convert_to_float8_training, sync_float8_amax_and_scale_history, ScalingType, ) # create model m = Model(...) # optional: configure for compatibility with FSDP. Note that workarounds # gated with config.enable_amax_init and # config.enable_pre_and_post_forward are needed for # autocast + compile + FSDP + float8 to work from float8_experimental import Float8LinearConfig, ScalingType, CastConfig config = Float8LinearConfig( enable_amax_init = False, # only needed for autocast + compile + FSDP + float8 delayed enable_pre_and_post_forward, False # only needed for autocast + compile + FSDP + float8 delayed cast_config_input=CastConfig(scaling_type=ScalingType.DELAYED), cast_config_weight=CastConfig(scaling_type=ScalingType.DELAYED), cast_config_grad_output=CastConfig(scaling_type=ScalingType.DELAYED), ) # convert all `torch.nn.Linear` modules to `Float8Linear`, specifying scaling # type convert_to_float8_training( m, config=config, ) # optional: use FSDP model = FSDP(model, use_orig_params=True) # optional: enable torch.compile for improved performance m = torch.compile(m) # toy training loop for _ in range(N_ITER): optimizer.zero_grad() y = m(x) y.sum().backward() # specific to float8 with delayed scaling: separate step to sync scales/amaxes # in the future, this may move to a context manager sync_float8_amax_and_scale_history(model) optimizer.step()We compose with the DTensor based distributed APIs, such as FSDP, TP and SP. Please see the torchtitan repository for e2e examples on using float8_experimental in a distributed setting.
# run single-GPU unit tests pytest test/test_base.py # run single-GPU compile tests pytest test/test_compile.py # run single-GPU numerics integration tests pytest test/test_numerics_integration.py # run a two-GPU integration test on FSDP ./test/test_fsdp.sh # run integration tests on the DTensor TP/SP integration ./test/test_dtensor.sh # run integration tests on the FSDP2 integration python test/test_fsdp2/test_fsdp2.py # run all of these tests ./test/test_everything.sh# benchmark the torch._scaled_mm function on LLaMa 2 70B shapes ./benchmarks/bench_matmul.py # benchmark fw/bw of `Linear` and `Float8Linear` on LLaMa 2 70B shapes # make sure to turn on torch.compile to get the best performance ./benchmarks/bench_linear_float8.py -o ../tmp/test.txt --compilePyTorch has a BSD 3-Clause License, as found in the LICENSE file.