Update float8nocompile readme (#1693)

Author: danielvegamyhre

torchao/prototype/float8nocompile/README.md

@@ -1,3 +1,72 @@
-# Work in progress
+# float8nocompile
 
-A prototype version of Float8Linear which is performant without `torch.compile`.
+
+A prototype API for high performance eager mode float8 training that uses handwritten Triton kernels for quantization.
+
+### Usage
+
+Prepare your model for high performance eager mode float8 training with a single conversion function: `convert_to_float8_nocompile_training` ([source](https://github.com/pytorch/ao/blob/32a51eca14257bbaafd3671a5349189e30c65e2b/torchao/prototype/float8nocompile/float8nocompile_linear_utils.py#L24)).
+
+This function will replace nn.Linear layers with Float8NoCompileLinear layers in-place, which uses **dynamic, tensorwise scaling**
+to perform all matmuls in the linear layer forward and backward pass as FP8 GEMMs.
+
+**Example**:
+
+```python
+from torchao.prototype.float8nocompile.float8nocompile_linear_utils import (
+    convert_to_float8_nocompile_training,
+)
+
+# define your model, data loaders, etc
+...
+
+# convert specified `torch.nn.Linear` modules to `Float8Linear`
+convert_to_float8_nocompile_training(model)
+
+# training loop
+for i in range(num_epochs):
+    ...
+```
+
+### Performance benchmarks
+
+Performance benchmarking was done via [experimental integration into torchtitan](https://github.com/pytorch/torchtitan/pull/778).
+
+The results indicate a solid 6-10% tokens/sec speedup with relatively flat memory (+/- 1% peak memory) compared the bf16 eager baseline.
+
+# Performance Comparison of Different Configurations on 8 H100s
+
+## No AC (seq len 4096) - 8 H100s
+
+| Configuration                                    | Tokens/sec | Peak memory (GB) | Tokens/sec Δ | Peak memory Δ |
+|-------------------------------------------------|------------|------------------|--------------|---------------|
+| bfloat16, eager                                 | 5339.0     | 53.12            | 0%           | 0.00%         |
+| float8nocompile prototype                       | 5871.4     | 52.7             | 9.97%        | -0.79%        |
+| float8 + torch.compile                          | 6667.6     | 46.64            | 24.88%       | -12.20%       |
+
+---
+
+## Selective per layer AC (AC every 2nd layer, seq len 4096) - 8 H100s
+
+| Configuration                                    | Tokens/sec | Peak memory (GB) | Tokens/sec Δ | Peak memory Δ |
+|-------------------------------------------------|------------|------------------|--------------|---------------|
+| bfloat16, eager                                 | 4882.4     | 40.6             | 0%           | 0.00%         |
+| float8nocompile prototype                       | 5302.0     | 40.97            | 8.59%        | 0.91%         |
+| float8 + torch.compile                          | 6199.6     | 37.38            | 26.98%       | -7.93%        |
+
+---
+
+## Full AC (seq len 4096) - 8 H100s
+
+| Configuration                                    | Tokens/sec | Peak memory (GB) | Tokens/sec Δ | Peak memory Δ |
+|-------------------------------------------------|------------|------------------|--------------|---------------|
+| bfloat16, eager                                 | 4502.0     | 28.07            | 0%           | 0.00%         |
+| float8nocompile prototype                       | 4773.4     | 28.07            | 6.03%        | 0.00%         |
+| float8 + torch.compile                          | 5775.2     | 28.03            | 28.28%       | -0.14%        |
+
+
+## Numerical accuracy
+
+Numerical accuracy has been verified via unit tests as well as manually verifying that the training loss curves maintain fidelity with the loss curves for bf16 eager and production float8 + torch.compile:
+
+![loss curves](float8nocompile_loss_curves.png "Loss curves")