basic torchao fp8 mixed precision training

_quarto.yml

@@ -268,6 +268,8 @@ website:
             - docs/batch_vs_grad.qmd
             - docs/dataset_preprocessing.qmd
             - docs/multipack.qmd
+            - docs/mixed_precision.qmd
+            - docs/gradient_accumulation.qmd
 
         - section: "Advanced Features"
           contents:

docs/mixed_precision.qmd

@@ -0,0 +1,149 @@
+---
+title: "Mixed Precision Training"
+format:
+  html:
+    toc: true
+    toc-depth: 3
+    number-sections: true
+    code-tools: true
+execute:
+  enabled: false
+---
+
+Mixed precision training uses lower precision data types to reduce memory usage and increase training speed while maintaining model quality. Axolotl supports several mixed precision formats:
+
+- **FP16** - Half precision 16-bit (Pascal generation+)
+- **BF16** - Brain Float 16-bit (Ampere generation+)
+- **FP8** - 8-bit floating point (Hopper generation+)
+
+## FP16 Mixed Precision {#sec-fp16}
+
+### Overview {#sec-fp16-overview}
+
+FP16 is the traditional half-precision format, supported on older GPUs but can be less numerically stable than BF16.
+
+### Configuration {#sec-fp16-config}
+
+```{.yaml}
+fp16: true
+```
+
+### FP16 Considerations {#sec-fp16-considerations}
+
+- May require gradient scaling to prevent underflow
+- Less numerically stable than BF16
+- Can cause training instability with some model architectures
+- Consider using BF16 if your hardware supports it
+
+## BF16 Mixed Precision {#sec-bf16}
+
+### Overview {#sec-bf16-overview}
+
+BF16 (Brain Float 16) offers better numerical stability than FP16 and is the recommended mixed precision format for modern GPUs. It provides the same dynamic range as FP32 while using half the memory.
+
+### Configuration {#sec-bf16-config}
+
+```{.yaml}
+# Automatic BF16 detection (recommended)
+bf16: auto
+
+# Or explicitly enable
+bf16: true
+
+# For evaluation with BF16
+bf16: full  # Equivalent to bf16_full_eval in the HF trainer
+```
+
+## FP8 Mixed Precision {#sec-fp8}
+
+::: {.callout-note}
+FP8 support is experimental and requires compatible hardware (H100, H200) and recent PyTorch versions with TorchAO.
+:::
+
+### What is FP8? {#sec-fp8-overview}
+
+FP8 (8-bit floating point) can provide significant time savings compared to FP16/BF16 while maintaining training stability. Axolotl's implementation uses PyTorch's TorchAO library with "tensorwise" scaling strategy.
+
+### Requirements {#sec-fp8-software}
+
+- Hopper+ GPUs (H100/H200)
+- PyTorch 2.7+ (+ compatible TorchAO version)
+- CUDA 12.4+
+
+### Configuration {#sec-fp8-config}
+
+Add to your YAML config:
+
+```{.yaml}
+# Enable FP8 mixed precision
+fp8: true
+
+# Optional: Enable FP8 for FSDP all-gather operations
+fp8_enable_fsdp_float8_all_gather: true
+
+# Enable torch.compile (almost always necessary for FP8 speedups)
+torch_compile: true
+```
+
+::: {.callout-important}
+**torch.compile is critical for FP8 performance**
+
+FP8 training requires `torch_compile: true` to see meaningful speedups. Without compilation, FP8 may actually be slower and use more memory than FP16/BF16.
+:::
+
+### Advanced FP8 Configs {#sec-fp8-advanced}
+
+For [FSDP](multi-gpu.qmd#sec-fsdp) (Fully Sharded Data Parallel) training:
+
+```{.yaml}
+fp8: true
+fp8_enable_fsdp_float8_all_gather: true
+
+torch_compile: true
+
+# FSDP configuration
+fsdp_version: 2
+fsdp_config:
+  offload_params: false
+  cpu_ram_efficient_loading: true
+  auto_wrap_policy: TRANSFORMER_BASED_WRAP
+  transformer_layer_cls_to_wrap: LlamaDecoderLayer
+  state_dict_type: FULL_STATE_DICT
+  reshard_after_forward: true
+```
+
+## Best Practices {#sec-best-practices}
+
+### Choosing Precision Format {#sec-choosing-format}
+
+- **Start with automatic detection**: `bf16: auto`
+- **For Hopper+ (H100/H200)**: Try FP8 + torch.compile for maximum speed
+- **For Ampere (A100/RTX 30/40)**: Use BF16
+- **For older Pascal/Turing GPUs**: Use FP16 with caution
+- **For very old or unsupported GPUs**: Use FP32
+
+### Validation and Testing {#sec-validation}
+
+Always validate your mixed precision setup:
+
+- **Start with a small dataset** to verify stability
+- **Monitor loss curves** for irregularities
+- **Compare with FP32 baseline** when possible
+- **Test evaluation metrics** match expectations
+
+### FP8 Particulars {#sec-fp8-details}
+
+- Use cases
+  - Single GPU training
+  - Multi GPU training with FSDP2 or Deepspeed
+- Speedups
+  - Please refer to the [TorchAO FP8 training benchmarks](https://github.com/pytorch/ao/tree/main/torchao/float8#rowwise-scaling) for expected matmul speedups for different (M, K, N) settings
+  - Concrete number for LLaMA 3 8B training can be found [here](https://github.com/pytorch/ao/tree/main/torchao/float8#training-benchmarks)
+- Known issues:
+  - FP8 + DDP + `torch.compile` (causes [error](https://gist.github.com/djsaunde/0c1664c32e44a64d31b5e01b4aafe5c4))
+  - FP8 + FSDP2 + `torch.compile` + FSDP2 activation checkpointing tends to be _slower_ than the BF16 equivalent training
+  - Flash Attention 2 does not play nicely with `torch.compile`
+
+See `examples/llama-3/3b-fp8-fsdp2.yaml` for an optimized example config. Enabling FP8 mixed precision + FP8 all-gather training results in ~10% faster iterations per second vs. BF16 for a relatively small (3B param) model
+
+For more information on multi-GPU training, see our [Multi-GPU guide](multi-gpu.qmd).

examples/llama-3/3b-fp8-fsdp2.yaml

@@ -0,0 +1,76 @@
+base_model: meta-llama/Llama-3.2-3B
+# Automatically upload checkpoint and final model to HF
+# hub_model_id: username/custom_model_name
+
+load_in_8bit: false
+load_in_4bit: false
+strict: false
+
+plugins:
+  - axolotl.integrations.liger.LigerPlugin
+
+liger_rope: true
+liger_rms_norm: true
+liger_glu_activation: true
+liger_layer_norm: true
+liger_fused_linear_cross_entropy: true
+
+datasets:
+  - path: yahma/alpaca-cleaned
+    type: alpaca
+
+output_dir: ./outputs/fp8_out/
+
+sample_packing: true
+pad_to_sequence_len: true
+sequence_len: 512
+
+flex_attention: true
+flex_attn_compile_kwargs:
+  dynamic: false
+  mode: max-autotune-no-cudagraphs
+
+torch_compile: true
+
+wandb_project:
+wandb_entity:
+wandb_watch:
+wandb_name:
+wandb_log_model:
+
+gradient_accumulation_steps: 1
+micro_batch_size: 16
+num_epochs: 1
+optimizer: adamw_torch_fused
+
+cosine_constant_lr_ratio: 0
+cosine_min_lr_ratio: 1.0
+learning_rate: 2e-5
+save_only_model: true
+
+fp8: true
+fp8_enable_fsdp_float8_all_gather: true
+
+resume_from_checkpoint:
+logging_steps: 1
+
+evals_per_epoch: 1
+saves_per_epoch: 1
+
+warmup_steps: 10
+weight_decay: 0.0
+
+fsdp_version: 2
+fsdp_config:
+  offload_params: false
+  auto_wrap_policy: TRANSFORMER_BASED_WRAP
+  transformer_layer_cls_to_wrap: LlamaDecoderLayer
+  state_dict_type: FULL_STATE_DICT
+  sharding_strategy: FULL_SHARD
+  reshard_after_forward: true
+  activation_checkpointing: false
+
+special_tokens:
+  pad_token: <|end_of_text|>
+
+# save_first_step: true  # uncomment this to validate checkpoint saving works with your config

src/axolotl/core/trainers/base.py

@@ -7,7 +7,7 @@
 import os
 from collections import defaultdict
 from functools import partial, wraps
-from typing import Callable, Literal, Optional
+from typing import Any, Callable, Literal, Optional
 
 import datasets
 import torch
@@ -522,15 +522,25 @@ def create_accelerator_and_postprocess(self):
 
         return res
 
+    # pylint: disable=unused-argument
     def additional_accelerator_args(
-        self, fp8=None, **kwargs
-    ):  # pylint: disable=unused-argument
+        self, fp8: bool = False, enable_fsdp_float8_all_gather: bool = False, **kwargs
+    ) -> dict[str, Any]:
         ret_kwargs = {}
         if fp8:
             from accelerate.utils import AORecipeKwargs
+            from torchao.float8 import Float8LinearConfig
+
+            # By default, Float8LinearConfig is instantiated using the "tensorwise"
+            # scaling strategy. See more details here:
+            # https://github.com/pytorch/ao/tree/main/torchao/float8.
+            config = Float8LinearConfig(
+                enable_fsdp_float8_all_gather=enable_fsdp_float8_all_gather,
+                force_recompute_fp8_weight_in_bwd=enable_fsdp_float8_all_gather is True,
+            )
 
             ret_kwargs["mixed_precision"] = "fp8"
-            ret_kwargs["kwargs_handlers"] = [AORecipeKwargs()]
+            ret_kwargs["kwargs_handlers"] = [AORecipeKwargs(config=config)]  # type: ignore
             os.environ["ACCELERATE_MIXED_PRECISION"] = "fp8"
 
         return ret_kwargs

src/axolotl/loaders/patch_manager.py

@@ -154,7 +154,9 @@ def _apply_fp8_patches(self):
                 patch_create_accelerate_code_for_fp8,
             )
 
-            patch_create_accelerate_code_for_fp8()
+            patch_create_accelerate_code_for_fp8(
+                self.cfg.fp8_enable_fsdp_float8_all_gather
+            )
 
     def _apply_flash_attention_peft_patches(self):
         """Apply patches for Flash Attention with PEFT."""

src/axolotl/monkeypatch/trainer_accelerator_args.py

@@ -18,7 +18,7 @@
 
 PATCHED_TRAINER_CODE = """
     if hasattr(self, "additional_accelerator_args"):
-        additional_args = self.additional_accelerator_args(fp8=True, **args)
+        additional_args = self.additional_accelerator_args(fp8=True, enable_fsdp_float8_all_gather={enable_fsdp_float8_all_gather}, **args)
         if additional_args:
             args.update(additional_args)
 
@@ -38,9 +38,9 @@ def check_create_accelerate_code_is_patchable() -> bool:
     return ORIGINAL_TRAINER_CODE in create_code
 
 
-def patch_create_accelerate_code_for_fp8():
+def patch_create_accelerate_code_for_fp8(enable_fsdp_float8_all_gather: bool):
     """
-    monkeypatch create_accelerator_and_postprocess so it checks for additional kwargs
+    Monkeypatch create_accelerator_and_postprocess so it checks for additional kwargs.
     """
 
     try:
@@ -54,7 +54,10 @@ def patch_create_accelerate_code_for_fp8():
     if ORIGINAL_TRAINER_CODE not in create_code:
         return
 
-    create_code = create_code.replace(ORIGINAL_TRAINER_CODE, PATCHED_TRAINER_CODE)
+    patched_trainer_code = PATCHED_TRAINER_CODE.format(
+        enable_fsdp_float8_all_gather=enable_fsdp_float8_all_gather
+    )
+    create_code = create_code.replace(ORIGINAL_TRAINER_CODE, patched_trainer_code)
     create_code = create_code.replace(
         "def create_accelerator_and_postprocess(",
         "def fixed_create_accelerator_and_postprocess(",

src/axolotl/utils/schemas/config.py

@@ -343,7 +343,20 @@ class AxolotlInputConfig(
     fp16: bool | None = Field(
         default=None, json_schema_extra={"description": "Use CUDA fp16"}
     )
-    fp8: bool | None = None
+    fp8: bool | None = Field(
+        default=None,
+        json_schema_extra={
+            "description": "Enable FP8 mixed precision training using TorchAO. Best "
+            "used in combination with torch.compile."
+        },
+    )
+    fp8_enable_fsdp_float8_all_gather: bool | None = Field(
+        default=None,
+        json_schema_extra={
+            "description": "Enable FSDP float8 all-gather optimization for FP8 training. Can "
+            "improve training speed by 10-15% when FSDP is enabled."
+        },
+    )
     bfloat16: bool | None = Field(
         default=None,
         json_schema_extra={

src/axolotl/utils/schemas/validation.py

@@ -360,6 +360,36 @@ def check_fft_possible_bad_config(self):
             # RuntimeError: expected mat1 and mat2 to have the same dtype, but got: float != c10::Half
         return self
 
+    @model_validator(mode="before")
+    @classmethod
+    def check_fp8_config(cls, data):
+        if data.get("fp8") and not data.get("torch_compile"):
+            LOG.warning(
+                "torch_compile is strongly recommended for FP8 training in order to "
+                "see speed improvements. Please consider setting `torch_compile: "
+                "true` in your config."
+            )
+        if data.get("fp8") and (
+            data.get("fsdp_config", {}).get("activation_checkpointing", False) is True
+            or data.get("fsdp_config", {}).get("fsdp_activation_checkpointing", False)
+            is True
+        ):
+            LOG.warning(
+                "FP8 + FSDP2 + activation checkpointing may be slower than BF16 "
+                "training. Please considering setting `activation_checkpointing: false` "
+                "in your FSDP config."
+            )
+        if (
+            data.get("fp8_enable_fsdp_float8_all_gather")
+            and not data.get("fsdp_version", None) == 2
+        ):
+            raise ValueError(
+                "fp8_enable_fsdp_float8_all_gather requires FSDP2 (fsdp_version: 2) "
+                "to be used."
+            )
+
+        return data
+
     @model_validator(mode="before")
     @classmethod
     def check_use_reentrant_mismatch(cls, data):