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Commit 9df0c4a3 authored by wenjh's avatar wenjh
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docs/features/low_precision_training/fp8_current_scaling/img/fp8_cast_process.svg 0 → 100644
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docs/features/low_precision_training/fp8_current_scaling/img/fp8_current_scaling_all_gather.svg 0 → 100644
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docs/features/low_precision_training/fp8_current_scaling/img/fp8_formats.svg 0 → 100644
View file @ 9df0c4a3
<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 900 280">
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<rect x="439" y="105" width="18" height="30" class="mantissa-bit"/>
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docs/features/low_precision_training/fp8_current_scaling/img/fp8_scaling_concept.svg 0 → 100644
View file @ 9df0c4a3
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@import url("../_static/css/diagram-colors.css");
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<!-- FP8 range bracket -->
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docs/features/low_precision_training/fp8_current_scaling/jax_current_scaling_example.py 0 → 100644
View file @ 9df0c4a3
# Copyright (c) 2022-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# See LICENSE for license information.
# START_CURRENT_SCALING_EXAMPLE
import jax
import jax.numpy as jnp
import transformer_engine.jax as te
from transformer_engine.jax.flax import DenseGeneral
from transformer_engine.common.recipe import Float8CurrentScaling, Format
# Create FP8 Current Scaling recipe
# Available formats:
# - Format.HYBRID (default) -- E4M3 for forward pass, E5M2 for backward pass
# - Format.E4M3 -- E4M3 for both forward and backward pass
recipe = Float8CurrentScaling(fp8_format=Format.HYBRID)
with te.autocast(enabled=True, recipe=recipe):
# Create and initialize layer
layer = DenseGeneral(features=1024)
key = jax.random.PRNGKey(0)
x = jax.random.normal(key, (32, 128, 1024), dtype=jnp.bfloat16)
var_collect = layer.init(key, x)
# Forward and backward pass
def loss_fn(var_collect):
output = layer.apply(var_collect, x)
return output.sum()
loss, grads = jax.value_and_grad(loss_fn)(var_collect)
# END_CURRENT_SCALING_EXAMPLE
docs/features/low_precision_training/fp8_current_scaling/pytorch_current_scaling_example.py 0 → 100644
View file @ 9df0c4a3
# Copyright (c) 2022-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# See LICENSE for license information.
# START_CURRENT_SCALING_EXAMPLE
import torch
import transformer_engine.pytorch as te
from transformer_engine.common.recipe import Float8CurrentScaling, Format
# Create FP8 Current Scaling recipe
# Available formats:
# - Format.HYBRID (default) -- E4M3 for forward pass, E5M2 for backward pass
# - Format.E4M3 -- E4M3 for both forward and backward pass
recipe = Float8CurrentScaling(fp8_format=Format.HYBRID)
# Create a simple linear layer with bfloat16 parameters
layer = te.Linear(1024, 1024, params_dtype=torch.bfloat16)
# Forward and backward pass
inp = torch.randn(32, 128, 1024, dtype=torch.bfloat16, device="cuda")
with te.autocast(enabled=True, recipe=recipe):
output = layer(inp)
loss = output.sum()
loss.backward()
# END_CURRENT_SCALING_EXAMPLE
docs/features/low_precision_training/fp8_delayed_scaling/fp8_delayed_scaling.rst 0 → 100644
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..
Copyright (c) 2022-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
See LICENSE for license information.
FP8 Delayed Scaling
===================================
FP8 Delayed Scaling recipe estimates scaling factors from historical amax values rather than computing them
for each tensor. Compared to Current Scaling recipe,
this reduces tensor reads per quantization from two to one,
improving memory efficiency.
Both this and :doc:`FP8 Current Scaling <../fp8_current_scaling/fp8_current_scaling>` recipe use
the same FP8 formats (E4M3/E5M2) with one FP32 scaling factor per tensor.
Reading the FP8 Current Scaling documentation first is recommended.
Quantization with delayed scaling factors
-----------------------------------------
FP8 Current Scaling requires two tensor reads per quantization: one to compute amax,
one to cast. FP8 Delayed Scaling eliminates the first read by predicting the scaling factor
from historical amax values - hence *delayed* (using past values) versus *current* (using present values).
The quantization process works as follows:
1. **Compute scaling factor from history** (no tensor read needed):
The scaling factor is derived from stored ``amax_history`` using the formula:
``scaling_factor = FP8_MAX / amax``
where ``amax`` is computed from history using either ``max`` (maximum over window, default) or ``most_recent`` algorithm.
2. **Quantize the tensor** (one tensor read):
Apply the scaling factor and cast to FP8. Values exceeding FP8 range are clipped.
3. **Update history**:
Record the actual amax from this quantization for future iterations.
Each module maintains an ``amax_history`` tensor of configurable length (``amax_history_len``)
for each quantized tensor.
.. raw:: html
:file: img/scaling_comparison.svg
*Figure 1. Comparison of FP8 Current Scaling and FP8 Delayed Scaling quantization processes.*
Amax History Management
-----------------------
The ``amax_history`` buffer acts as a sliding window of recent amax values.
Position 0 serves as a staging area for the current amax, while positions 1 to N-1
store the history from oldest to newest. Each quantization writes the observed amax
to position 0, and after the pass completes, the history is rotated:
.. code-block:: text
Before rotation: [amax_N, amax_1, amax_2, ..., amax_N-1] (amax_N = current, amax_1 = oldest)
After rotation: [0, amax_2, ..., amax_N-1, amax_N] (amax_1 dropped, amax_N appended)
The scaling factor is computed **before** the rotation, so it uses all ``amax_history_len`` values.
Position 0 serves as a staging area — it is zeroed after the scale update, ready for the next iteration's amax.
The implementation differs between PyTorch and JAX:
.. tabs::
.. tab:: PyTorch
Each module creates two ``amax_history`` tensors, initialized to zero:
- Forward: shape ``(amax_history_len, num_gemms * 3)`` — three FP8 tensors per GEMM (input, weight, output)
- Backward: shape ``(amax_history_len, num_gemms * 2)`` — two FP8 tensors per GEMM (grad_output, grad_input)
When the autocast context exits, a single CUDA kernel processes all tensors at once —
performing amax reduction across GPUs and history rotation. This batched approach
minimizes kernel launch overhead compared to updating each tensor separately.
.. tab:: JAX
Each quantizer maintains its own ``amax_history`` with shape ``(amax_history_len,)``
and updates independently.
Here's how to use FP8 Delayed Scaling in PyTorch and JAX:
.. tabs::
.. tab:: PyTorch
.. raw:: html
<div style="background: #f0f4f8; border-left: 3px solid #5c7cfa; padding: 6px 12px; font-size: 13px; color: #495057; margin-bottom: 0; border-radius: 4px 4px 0 0;">
Requires SM89 (Ada) or later
</div>
.. literalinclude:: pytorch_delayed_scaling_example.py
:language: python
:start-after: # START_DELAYED_SCALING_EXAMPLE
:end-before: # END_DELAYED_SCALING_EXAMPLE
.. tab:: JAX
.. raw:: html
<div style="background: #f0f4f8; border-left: 3px solid #5c7cfa; padding: 6px 12px; font-size: 13px; color: #495057; margin-bottom: 0; border-radius: 4px 4px 0 0;">
Requires SM89 (Ada) or later
</div>
.. literalinclude:: jax_delayed_scaling_example.py
:language: python
:start-after: # START_DELAYED_SCALING_EXAMPLE
:end-before: # END_DELAYED_SCALING_EXAMPLE
Distributed Training
--------------------
FP8 Delayed Scaling uses the same data formats as FP8 Current Scaling - quantized all-gather is supported.
However, amax reduction works slightly differently in different frameworks.
.. tabs::
.. tab:: PyTorch
Amax reduction is controlled by two parameters:
- ``reduce_amax`` in recipe: enables/disables reduction (required for SP and CP)
- ``amax_reduction_group`` in ``autocast``: specifies the process group for reduction
We recommend reducing amax across all GPUs where the tensor is sharded,
including data parallel ranks.
.. literalinclude:: pytorch_delayed_scaling_distributed_example.py
:language: python
:start-after: # START_AMAX_REDUCTION_EXAMPLE
:end-before: # END_AMAX_REDUCTION_EXAMPLE
In data parallel training, some modules may not execute on certain ranks
(e.g., MoE experts that receive no tokens). This is handled as follows:
- **First iteration**: All modules must execute on all ranks to register
their ``amax_history`` tensors in the global buffer. Mismatched registration
would cause the ``all_reduce`` to hang due to different tensor sizes across ranks.
- **Subsequent iterations**: The ``autocast`` context must be entered and exited
on all ranks (this triggers the collective reduction). Individual modules can be
skipped - if no rank executes a module, its history is not rotated and scale
remains unchanged.
.. tab:: JAX
Amax reduction is always enabled and managed automatically.
Reduction scope: all parallelism axes except pipeline parallelism (TP, SP, DP/FSDP).
.. literalinclude:: jax_delayed_scaling_distributed_example.py
:language: python
:start-after: # START_AMAX_REDUCTION_EXAMPLE
:end-before: # END_AMAX_REDUCTION_EXAMPLE
Supported devices
-----------------
Ada and later (SM 8.9+)
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<text x="820" y="162" class="small-text" style="text-anchor: start;">read amax</text>
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<text x="565" y="175" class="small-text" style="text-anchor: end;">update amax</text>
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<text x="750" y="325" class="text">FP8 Tensor</text>
</svg>
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# Copyright (c) 2022-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# See LICENSE for license information.
# START_AMAX_REDUCTION_EXAMPLE
import transformer_engine.jax as te
from transformer_engine.common.recipe import DelayedScaling
# Amax reduction scope is managed internally
recipe = DelayedScaling(reduce_amax=True) # Must be True in JAX
with te.autocast(enabled=True, recipe=recipe):
output = layer.apply(params, inp)
# END_AMAX_REDUCTION_EXAMPLE
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# Copyright (c) 2022-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# See LICENSE for license information.
from transformer_engine.jax.quantize import get_device_compute_capability
# Requires Ada (SM89) or newer for FP8 support
assert get_device_compute_capability() >= 89, "This example requires SM89 (Ada) or newer"
# START_DELAYED_SCALING_EXAMPLE
import jax
import jax.numpy as jnp
import transformer_engine.jax as te
from transformer_engine.jax.flax import DenseGeneral
from transformer_engine.common.recipe import DelayedScaling
# Create FP8 Delayed Scaling recipe
recipe = DelayedScaling(
margin=0, # Margin for scaling factor computation (default: 0)
amax_history_len=1024, # Length of amax history window (default: 1024)
amax_compute_algo="max", # How to compute amax from history (default: "max")
)
with te.autocast(enabled=True, recipe=recipe):
# Initialize layer and data
layer = DenseGeneral(features=1024)
key = jax.random.PRNGKey(0)
x = jax.random.normal(key, (32, 128, 1024), dtype=jnp.bfloat16)
var_collect = layer.init(key, x)
# Forward and backward pass
def loss_fn(var_collect):
output = layer.apply(var_collect, x)
return output.sum()
loss, grads = jax.value_and_grad(loss_fn)(var_collect)
# END_DELAYED_SCALING_EXAMPLE
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# Copyright (c) 2022-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# See LICENSE for license information.
# START_AMAX_REDUCTION_EXAMPLE
import torch.distributed as dist
import transformer_engine.pytorch as te
from transformer_engine.common.recipe import DelayedScaling
# Create process group for amax reduction (e.g., all 8 GPUs)
amax_reduction_group = dist.new_group(ranks=[0, 1, 2, 3, 4, 5, 6, 7])
recipe = DelayedScaling(reduce_amax=True)
with te.autocast(recipe=recipe, amax_reduction_group=amax_reduction_group):
output = model(inp)
# END_AMAX_REDUCTION_EXAMPLE
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# Copyright (c) 2022-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# See LICENSE for license information.
import torch
# Requires Ada (SM89) or newer for FP8 support
assert torch.cuda.get_device_capability()[0] >= 9 or (
torch.cuda.get_device_capability()[0] == 8 and torch.cuda.get_device_capability()[1] >= 9
), "This example requires SM89 (Ada) or newer"
# START_DELAYED_SCALING_EXAMPLE
import torch
import transformer_engine.pytorch as te
from transformer_engine.common.recipe import DelayedScaling
# Create FP8 Delayed Scaling recipe
recipe = DelayedScaling(
margin=0, # Margin for scaling factor computation (default: 0)
amax_history_len=1024, # Length of amax history window (default: 1024)
amax_compute_algo="max", # How to compute amax from history (default: "max")
)
# Create a linear layer with bfloat16 parameters
layer = te.Linear(1024, 1024, params_dtype=torch.bfloat16)
# Forward and backward pass
inp = torch.randn(32, 128, 1024, dtype=torch.bfloat16, device="cuda")
with te.autocast(enabled=True, recipe=recipe):
output = layer(inp)
loss = output.sum()
loss.backward()
# END_DELAYED_SCALING_EXAMPLE
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..
Copyright (c) 2022-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
See LICENSE for license information.
Low precision training
===================================
.. toctree::
introduction/introduction.rst
performance_considerations/performance_considerations.rst
fp8_current_scaling/fp8_current_scaling.rst
fp8_delayed_scaling/fp8_delayed_scaling.rst
fp8_blockwise_scaling/fp8_blockwise_scaling.rst
mxfp8/mxfp8.rst
nvfp4/nvfp4.rst
\ No newline at end of file
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# Copyright (c) 2022-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# See LICENSE for license information.
from transformer_engine.jax.quantize import get_device_compute_capability
# Requires Ada (SM89) or newer for FP8 support
assert get_device_compute_capability() >= 89, "This example requires SM89 (Ada) or newer"
# START_AUTOCAST_BASIC
import jax
import jax.numpy as jnp
import transformer_engine.jax as te
from transformer_engine.jax.flax import TransformerLayer
from transformer_engine.common.recipe import DelayedScaling, Format
# Set up recipe
recipe = DelayedScaling()
# Model initialization must happen inside autocast
with te.autocast(enabled=True, recipe=recipe):
layer = TransformerLayer(
hidden_size=1024,
mlp_hidden_size=4096,
num_attention_heads=16,
)
init_key, dropout_key = jax.random.split(jax.random.PRNGKey(0))
x = jax.random.normal(init_key, (32, 128, 1024), dtype=jnp.bfloat16)
var_collect = layer.init({"params": init_key, "dropout": dropout_key}, x)
# Forward and backward pass (both inside autocast for JAX)
def loss_fn(var_collect):
output = layer.apply(var_collect, x, rngs={"dropout": dropout_key})
return output.sum()
loss, grads = jax.value_and_grad(loss_fn)(var_collect)
# END_AUTOCAST_BASIC
# START_AUTOCAST_SEQUENTIAL
encoder_recipe = DelayedScaling(fp8_format=Format.E4M3)
decoder_recipe = DelayedScaling(fp8_format=Format.HYBRID)
with te.autocast(enabled=True, recipe=encoder_recipe):
encoder = TransformerLayer(hidden_size=1024, mlp_hidden_size=4096, num_attention_heads=16)
encoder_var_collect = encoder.init({"params": init_key, "dropout": dropout_key}, x)
hidden = encoder.apply(encoder_var_collect, x, rngs={"dropout": dropout_key})
with te.autocast(enabled=True, recipe=decoder_recipe):
decoder = TransformerLayer(hidden_size=1024, mlp_hidden_size=4096, num_attention_heads=16)
decoder_var_collect = decoder.init({"params": init_key, "dropout": dropout_key}, hidden)
output = decoder.apply(decoder_var_collect, hidden, rngs={"dropout": dropout_key})
# END_AUTOCAST_SEQUENTIAL
# START_AUTOCAST_NESTED
outer_recipe = DelayedScaling(fp8_format=Format.E4M3)
inner_recipe = DelayedScaling(fp8_format=Format.HYBRID)
with te.autocast(enabled=True, recipe=outer_recipe):
# layer1 uses outer_recipe
layer1 = TransformerLayer(hidden_size=1024, mlp_hidden_size=4096, num_attention_heads=16)
var_collect1 = layer1.init({"params": init_key, "dropout": dropout_key}, x)
hidden = layer1.apply(var_collect1, x, rngs={"dropout": dropout_key})
with te.autocast(enabled=True, recipe=inner_recipe):
# layer2 uses inner_recipe (overrides outer)
layer2 = TransformerLayer(hidden_size=1024, mlp_hidden_size=4096, num_attention_heads=16)
var_collect2 = layer2.init({"params": init_key, "dropout": dropout_key}, hidden)
hidden = layer2.apply(var_collect2, hidden, rngs={"dropout": dropout_key})
# layer3 uses outer_recipe again
layer3 = TransformerLayer(hidden_size=1024, mlp_hidden_size=4096, num_attention_heads=16)
var_collect3 = layer3.init({"params": init_key, "dropout": dropout_key}, hidden)
output = layer3.apply(var_collect3, hidden, rngs={"dropout": dropout_key})
# END_AUTOCAST_NESTED
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# Copyright (c) 2022-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# See LICENSE for license information.
import torch
# Requires Ada (SM89) or newer for FP8 support
assert torch.cuda.get_device_capability()[0] >= 9 or (
torch.cuda.get_device_capability()[0] == 8 and torch.cuda.get_device_capability()[1] >= 9
), "This example requires SM89 (Ada) or newer"
# START_AUTOCAST_BASIC
import torch
import transformer_engine.pytorch as te
from transformer_engine.common.recipe import DelayedScaling, Format
recipe = DelayedScaling()
layer = te.Linear(1024, 1024)
inp = torch.randn(32, 1024, dtype=torch.float32, device="cuda")
with te.autocast(enabled=True, recipe=recipe):
output = layer(inp)
# .backward() is called outside of autocast
loss = output.sum()
loss.backward()
# END_AUTOCAST_BASIC
# START_AUTOCAST_SEQUENTIAL
encoder_recipe = DelayedScaling(fp8_format=Format.E4M3)
decoder_recipe = DelayedScaling(fp8_format=Format.HYBRID)
encoder = te.Linear(1024, 1024)
decoder = te.Linear(1024, 1024)
with te.autocast(enabled=True, recipe=encoder_recipe):
hidden = encoder(inp)
with te.autocast(enabled=True, recipe=decoder_recipe):
output = decoder(hidden)
# END_AUTOCAST_SEQUENTIAL
# START_AUTOCAST_NESTED
outer_recipe = DelayedScaling(fp8_format=Format.E4M3)
inner_recipe = DelayedScaling(fp8_format=Format.HYBRID)
layer1 = te.Linear(1024, 1024)
layer2 = te.Linear(1024, 1024)
layer3 = te.Linear(1024, 1024)
with te.autocast(enabled=True, recipe=outer_recipe):
# layer1 uses outer_recipe
x = layer1(inp)
with te.autocast(enabled=True, recipe=inner_recipe):
# layer2 uses inner_recipe (overrides outer)
x = layer2(x)
# layer3 uses outer_recipe again
output = layer3(x)
# END_AUTOCAST_NESTED
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# Copyright (c) 2022-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# See LICENSE for license information.
# START_BF16_FP16_TRAINING
import jax
import jax.numpy as jnp
from transformer_engine.jax.flax import TransformerLayer
def run_forward_backward(params_dtype, compute_dtype):
# Create TransformerLayer
layer = TransformerLayer(
hidden_size=1024,
mlp_hidden_size=4096,
num_attention_heads=16,
dtype=params_dtype,
)
# Initialize parameters and optimizer
init_key, dropout_key = jax.random.split(jax.random.PRNGKey(0))
x = jax.random.normal(init_key, (32, 128, 1024), dtype=compute_dtype)
var_collect = layer.init({"params": init_key, "dropout": dropout_key}, x)
# Forward and backward pass
def loss_fn(var_collect):
output = layer.apply(var_collect, x, rngs={"dropout": dropout_key})
assert output.dtype == compute_dtype
return output.sum()
loss, grads = jax.value_and_grad(loss_fn)(var_collect)
run_forward_backward(jnp.float32, jnp.float32) # high precision training
run_forward_backward(jnp.float32, jnp.bfloat16) # bfloat16 training with master weights in FP32
run_forward_backward(jnp.bfloat16, jnp.bfloat16) # bfloat16 training with weights in BF16
# END_BF16_FP16_TRAINING
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# Copyright (c) 2022-2026, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
#
# See LICENSE for license information.
# START_BF16_FP16_TRAINING
import torch
import transformer_engine.pytorch as te
from contextlib import nullcontext
def run_forward_backward(params_dtype, autocast_precision, grad_scaler_enabled):
if grad_scaler_enabled:
grad_scaler = torch.amp.GradScaler("cuda")
layer = te.TransformerLayer(
hidden_size=1024,
ffn_hidden_size=4096,
num_attention_heads=16,
params_dtype=params_dtype,
)
x = torch.randn(32, 128, 1024, dtype=params_dtype, device="cuda")
autocast_ctx = (
torch.autocast(device_type="cuda", dtype=autocast_precision)
if autocast_precision is not None
else nullcontext()
)
with autocast_ctx:
output = layer(x)
assert (
output.dtype == autocast_precision if autocast_precision is not None else params_dtype
)
loss = output.sum()
if grad_scaler_enabled:
grad_scaler.scale(loss).backward()
else:
loss.backward()
run_forward_backward(torch.float32, torch.float32, False) # high precision training
run_forward_backward(
torch.float32, torch.bfloat16, False
) # bfloat16 training with master weights in FP32
run_forward_backward(
torch.float32, torch.float16, True
) # fp16 training with master weights in FP32, needs loss scaling
run_forward_backward(
torch.bfloat16, torch.bfloat16, False
) # bfloat16 training with weights in BF16
# END_BF16_FP16_TRAINING
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<text x="310" y="160" class="text">Input</text>
<!-- Forward: Arrow from Input to GEMM -->
<path d="M 350 155 L 400 170" stroke="#616161" stroke-width="2" fill="none" marker-end="url(#arrowhead)"/>
<text x="370" y="145" class="text" style="font-size: 10px; font-weight: bold;">N</text>
<!-- Forward: Weights High Precision -->
<rect x="30" y="195" width="100" height="50" class="hp" rx="6"/>
<text x="80" y="225" class="text">Weight</text>
<!-- Forward: Arrow -->
<path d="M 130 220 L 155 220" stroke="#616161" stroke-width="2" fill="none" marker-end="url(#arrowhead)"/>
<!-- Forward: Quantize Weights -->
<rect x="155" y="195" width="90" height="50" class="quantize" rx="6"/>
<text x="200" y="225" class="text">Quantize</text>
<!-- Forward: Arrow to Weight -->
<path d="M 245 220 L 270 220" stroke="#616161" stroke-width="2" fill="none" marker-end="url(#arrowhead)"/>
<!-- Forward: Arrow to Weight^T (going down) -->
<path d="M 245 235 L 270 270" stroke="#616161" stroke-width="2" fill="none" marker-end="url(#arrowhead)"/>
<!-- Forward: Weights FP8 -->
<rect x="270" y="195" width="80" height="50" class="fp8" rx="6"/>
<text x="310" y="225" class="text">Weight</text>
<!-- Forward: Weight^T FP8 (bottom, saved for backward) -->
<rect x="270" y="255" width="80" height="50" class="fp8" rx="6"/>
<text x="310" y="285" class="text">Weight<tspan baseline-shift="super" style="font-size: 9px;">T</tspan></text>
<!-- Forward: Arrow from Weight to GEMM -->
<path d="M 350 220 L 400 200" stroke="#616161" stroke-width="2" fill="none" marker-end="url(#arrowhead)"/>
<text x="370" y="230" class="text" style="font-size: 10px; font-weight: bold;">T</text>
<!-- Forward: GEMM -->
<rect x="400" y="160" width="130" height="50" class="gemm" rx="6"/>
<text x="465" y="180" class="text" style="font-weight: 600;">FP8 GEMM</text>
<text x="465" y="200" class="text" style="font-size: 11px;">(TN)</text>
<!-- Forward: Arrow -->
<path d="M 530 185 L 580 185" stroke="#616161" stroke-width="2" fill="none" marker-end="url(#arrowhead)"/>
<!-- Forward: Output -->
<rect x="580" y="160" width="110" height="50" class="hp" rx="6"/>
<text x="635" y="190" class="text">Output</text>
<!-- Divider Line -->
<line x1="30" y1="310" x2="820" y2="310" stroke="#ddd" stroke-width="2"/>
<!-- Backward Pass Section -->
<text x="425" y="345" class="section-title" style="fill: #c62828;">Backward Pass</text>
<!-- Backward: Weight^T (from forward, top input to GEMM1) -->
<rect x="495" y="355" width="80" height="50" class="fp8" rx="6"/>
<text x="535" y="385" class="text">Weight<tspan baseline-shift="super" style="font-size: 9px;">T</tspan></text>
<!-- Backward: Output gradient High Precision -->
<rect x="30" y="480" width="130" height="50" class="hp" rx="6"/>
<text x="95" y="510" class="text">Output grad.</text>
<!-- Backward: Arrow -->
<path d="M 160 505 L 180 505" stroke="#616161" stroke-width="2" fill="none" marker-end="url(#arrowhead)"/>
<!-- Backward: Quantize Output gradient -->
<rect x="180" y="480" width="90" height="50" class="quantize" rx="6"/>
<text x="225" y="510" class="text">Quantize</text>
<!-- Backward: Arrow to Output grad (going up) -->
<path d="M 270 490 L 290 465" stroke="#616161" stroke-width="2" fill="none" marker-end="url(#arrowhead)"/>
<!-- Backward: Arrow to Output grad^T (going down) -->
<path d="M 270 520 L 290 545" stroke="#616161" stroke-width="2" fill="none" marker-end="url(#arrowhead)"/>
<!-- Backward: Output gradient FP8 (for input gradient) -->
<rect x="290" y="440" width="110" height="50" class="fp8" rx="6"/>
<text x="345" y="470" class="text">Output grad.</text>
<!-- Backward: Output gradient^T FP8 (for weight gradient) -->
<rect x="290" y="520" width="110" height="50" class="fp8" rx="6"/>
<text x="345" y="550" class="text">Output grad.<tspan baseline-shift="super" style="font-size: 9px;">T</tspan></text>
<!-- Backward: GEMM 1 (for input gradient) -->
<rect x="470" y="440" width="130" height="50" class="gemm" rx="6"/>
<text x="535" y="460" class="text" style="font-weight: 600;">FP8 GEMM</text>
<text x="535" y="480" class="text" style="font-size: 11px;">(TN)</text>
<!-- Backward: Input gradient -->
<rect x="640" y="440" width="130" height="50" class="hp" rx="6"/>
<text x="705" y="470" class="text">Input grad.</text>
<!-- Backward: GEMM 2 (for weight gradient) -->
<rect x="470" y="520" width="130" height="50" class="gemm" rx="6"/>
<text x="535" y="540" class="text" style="font-weight: 600;">FP8 GEMM</text>
<text x="535" y="560" class="text" style="font-size: 11px;">(TN)</text>
<!-- Backward: Weight gradient -->
<rect x="640" y="520" width="130" height="50" class="hp" rx="6"/>
<text x="705" y="550" class="text">Weight grad.</text>
<!-- Backward: Input^T (from forward, bottom input to GEMM2) -->
<rect x="495" y="605" width="80" height="50" class="fp8" rx="6"/>
<text x="535" y="635" class="text">Input<tspan baseline-shift="super" style="font-size: 9px;">T</tspan></text>
<!-- Backward: Arrows -->
<!-- Output gradient FP8 to top GEMM -->
<path d="M 400 465 L 470 465" stroke="#616161" stroke-width="2" fill="none" marker-end="url(#arrowhead)"/>
<text x="430" y="457" class="text" style="font-size: 10px; font-weight: bold;">N</text>
<!-- Weight^T to top GEMM -->
<path d="M 535 405 L 535 440" stroke="#616161" stroke-width="2" fill="none" marker-end="url(#arrowhead)"/>
<text x="543" y="427" class="text" style="font-size: 10px; font-weight: bold;">T</text>
<!-- Top GEMM to input gradient -->
<path d="M 600 465 L 640 465" stroke="#616161" stroke-width="2" fill="none" marker-end="url(#arrowhead)"/>
<!-- Output gradient^T FP8 to bottom GEMM -->
<path d="M 400 545 L 470 545" stroke="#616161" stroke-width="2" fill="none" marker-end="url(#arrowhead)"/>
<text x="430" y="537" class="text" style="font-size: 10px; font-weight: bold;">N</text>
<!-- Input^T to bottom GEMM -->
<path d="M 535 605 L 535 570" stroke="#616161" stroke-width="2" fill="none" marker-end="url(#arrowhead)"/>
<text x="543" y="597" class="text" style="font-size: 10px; font-weight: bold;">T</text>
<!-- Bottom GEMM to weight gradient -->
<path d="M 600 545 L 640 545" stroke="#616161" stroke-width="2" fill="none" marker-end="url(#arrowhead)"/>
<!-- Legend -->
<g transform="translate(30, 680)">
<!-- Higher Precision -->
<rect x="0" y="0" width="80" height="40" rx="5" class="hp"/>
<text x="95" y="23" class="text" style="text-anchor: start;">Higher Precision (FP32/BF16/FP16)</text>
<!-- Lower Precision -->
<rect x="380" y="0" width="80" height="40" rx="5" class="fp8"/>
<text x="475" y="23" class="text" style="text-anchor: start;">Lower Precision (FP8, MXFP8 etc.)</text>
</g>
</svg>
docs/features/low_precision_training/introduction/img/fp_formats_comparison.svg 0 → 100644
View file @ 9df0c4a3
<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 900 210">
<defs>
<style>
@import url("../_static/css/diagram-colors.css");
.sign-bit { fill: #9db4d0; stroke: #333; stroke-width: 1; }
.exponent-bit { fill: #d9a066; stroke: #333; stroke-width: 1; }
.mantissa-bit { fill: #a8d99c; stroke: #333; stroke-width: 1; }
.bit-text { fill: #000; text-anchor: middle; dominant-baseline: middle; font-size: 16px; }
.header-text { fill: #555; font-weight: normal; text-anchor: middle; font-size: 18px; }
.value-text { fill: #333; font-size: 18px; }
.format-label { fill: #333; font-size: 20px; font-weight: bold; text-anchor: middle; dominant-baseline: middle; }
</style>
</defs>
<!-- Header labels - centered -->
<text x="79" y="18" class="header-text">sign</text>
<text x="173" y="18" class="header-text">exponent</text>
<text x="530" y="18" class="header-text">mantissa</text>
<!-- FP32 Format (32 bits: 1 + 8 + 23) -->
<text x="30" y="60" class="format-label">FP32</text>
<!-- Sign bit (1) -->
<rect x="70" y="45" width="18" height="30" class="sign-bit"/>
<text x="79" y="60" class="bit-text">0</text>
<!-- Exponent bits (8) -->
<rect x="93" y="45" width="18" height="30" class="exponent-bit"/>
<text x="102" y="60" class="bit-text">0</text>
<rect x="116" y="45" width="18" height="30" class="exponent-bit"/>
<text x="125" y="60" class="bit-text">1</text>
<rect x="139" y="45" width="18" height="30" class="exponent-bit"/>
<text x="148" y="60" class="bit-text">1</text>
<rect x="162" y="45" width="18" height="30" class="exponent-bit"/>
<text x="171" y="60" class="bit-text">1</text>
<rect x="185" y="45" width="18" height="30" class="exponent-bit"/>
<text x="194" y="60" class="bit-text">1</text>
<rect x="208" y="45" width="18" height="30" class="exponent-bit"/>
<text x="217" y="60" class="bit-text">1</text>
<rect x="231" y="45" width="18" height="30" class="exponent-bit"/>
<text x="240" y="60" class="bit-text">0</text>
<rect x="254" y="45" width="18" height="30" class="exponent-bit"/>
<text x="263" y="60" class="bit-text">1</text>
<!-- Mantissa bits (23) -->
<rect x="277" y="45" width="18" height="30" class="mantissa-bit"/>
<text x="286" y="60" class="bit-text">1</text>
<rect x="300" y="45" width="18" height="30" class="mantissa-bit"/>
<text x="309" y="60" class="bit-text">0</text>
<rect x="323" y="45" width="18" height="30" class="mantissa-bit"/>
<text x="332" y="60" class="bit-text">0</text>
<rect x="346" y="45" width="18" height="30" class="mantissa-bit"/>
<text x="355" y="60" class="bit-text">1</text>
<rect x="369" y="45" width="18" height="30" class="mantissa-bit"/>
<text x="378" y="60" class="bit-text">0</text>
<rect x="392" y="45" width="18" height="30" class="mantissa-bit"/>
<text x="401" y="60" class="bit-text">1</text>
<rect x="415" y="45" width="18" height="30" class="mantissa-bit"/>
<text x="424" y="60" class="bit-text">0</text>
<rect x="438" y="45" width="18" height="30" class="mantissa-bit"/>
<text x="447" y="60" class="bit-text">0</text>
<rect x="461" y="45" width="18" height="30" class="mantissa-bit"/>
<text x="470" y="60" class="bit-text">1</text>
<rect x="484" y="45" width="18" height="30" class="mantissa-bit"/>
<text x="493" y="60" class="bit-text">0</text>
<rect x="507" y="45" width="18" height="30" class="mantissa-bit"/>
<text x="516" y="60" class="bit-text">1</text>
<rect x="530" y="45" width="18" height="30" class="mantissa-bit"/>
<text x="539" y="60" class="bit-text">0</text>
<rect x="553" y="45" width="18" height="30" class="mantissa-bit"/>
<text x="562" y="60" class="bit-text">1</text>
<rect x="576" y="45" width="18" height="30" class="mantissa-bit"/>
<text x="585" y="60" class="bit-text">1</text>
<rect x="599" y="45" width="18" height="30" class="mantissa-bit"/>
<text x="608" y="60" class="bit-text">1</text>
<rect x="622" y="45" width="18" height="30" class="mantissa-bit"/>
<text x="631" y="60" class="bit-text">1</text>
<rect x="645" y="45" width="18" height="30" class="mantissa-bit"/>
<text x="654" y="60" class="bit-text">0</text>
<rect x="668" y="45" width="18" height="30" class="mantissa-bit"/>
<text x="677" y="60" class="bit-text">1</text>
<rect x="691" y="45" width="18" height="30" class="mantissa-bit"/>
<text x="700" y="60" class="bit-text">0</text>
<rect x="714" y="45" width="18" height="30" class="mantissa-bit"/>
<text x="723" y="60" class="bit-text">1</text>
<rect x="737" y="45" width="18" height="30" class="mantissa-bit"/>
<text x="746" y="60" class="bit-text">0</text>
<rect x="760" y="45" width="18" height="30" class="mantissa-bit"/>
<text x="769" y="60" class="bit-text">0</text>
<rect x="783" y="45" width="18" height="30" class="mantissa-bit"/>
<text x="792" y="60" class="bit-text">0</text>
<text x="820" y="60" class="value-text">= 0.3952</text>
<!-- BF16 Format (16 bits: 1 + 8 + 7) -->
<text x="30" y="120" class="format-label">BF16</text>
<!-- Sign bit (1) -->
<rect x="70" y="105" width="18" height="30" class="sign-bit"/>
<text x="79" y="120" class="bit-text">0</text>
<!-- Exponent bits (8) -->
<rect x="93" y="105" width="18" height="30" class="exponent-bit"/>
<text x="102" y="120" class="bit-text">0</text>
<rect x="116" y="105" width="18" height="30" class="exponent-bit"/>
<text x="125" y="120" class="bit-text">1</text>
<rect x="139" y="105" width="18" height="30" class="exponent-bit"/>
<text x="148" y="120" class="bit-text">1</text>
<rect x="162" y="105" width="18" height="30" class="exponent-bit"/>
<text x="171" y="120" class="bit-text">1</text>
<rect x="185" y="105" width="18" height="30" class="exponent-bit"/>
<text x="194" y="120" class="bit-text">1</text>
<rect x="208" y="105" width="18" height="30" class="exponent-bit"/>
<text x="217" y="120" class="bit-text">1</text>
<rect x="231" y="105" width="18" height="30" class="exponent-bit"/>
<text x="240" y="120" class="bit-text">0</text>
<rect x="254" y="105" width="18" height="30" class="exponent-bit"/>
<text x="263" y="120" class="bit-text">1</text>
<!-- Mantissa bits (7) -->
<rect x="277" y="105" width="18" height="30" class="mantissa-bit"/>
<text x="286" y="120" class="bit-text">1</text>
<rect x="300" y="105" width="18" height="30" class="mantissa-bit"/>
<text x="309" y="120" class="bit-text">0</text>
<rect x="323" y="105" width="18" height="30" class="mantissa-bit"/>
<text x="332" y="120" class="bit-text">0</text>
<rect x="346" y="105" width="18" height="30" class="mantissa-bit"/>
<text x="355" y="120" class="bit-text">1</text>
<rect x="369" y="105" width="18" height="30" class="mantissa-bit"/>
<text x="378" y="120" class="bit-text">0</text>
<rect x="392" y="105" width="18" height="30" class="mantissa-bit"/>
<text x="401" y="120" class="bit-text">1</text>
<rect x="415" y="105" width="18" height="30" class="mantissa-bit"/>
<text x="424" y="120" class="bit-text">0</text>
<text x="820" y="120" class="value-text">≈ 0.3945</text>
<!-- FP16 Format (16 bits: 1 + 5 + 10) -->
<text x="30" y="180" class="format-label">FP16</text>
<!-- Sign bit (1) -->
<rect x="70" y="165" width="18" height="30" class="sign-bit"/>
<text x="79" y="180" class="bit-text">0</text>
<!-- Exponent bits (5) -->
<rect x="93" y="165" width="18" height="30" class="exponent-bit"/>
<text x="102" y="180" class="bit-text">0</text>
<rect x="116" y="165" width="18" height="30" class="exponent-bit"/>
<text x="125" y="180" class="bit-text">1</text>
<rect x="139" y="165" width="18" height="30" class="exponent-bit"/>
<text x="148" y="180" class="bit-text">1</text>
<rect x="162" y="165" width="18" height="30" class="exponent-bit"/>
<text x="171" y="180" class="bit-text">0</text>
<rect x="185" y="165" width="18" height="30" class="exponent-bit"/>
<text x="194" y="180" class="bit-text">1</text>
<!-- Mantissa bits (10) -->
<rect x="208" y="165" width="18" height="30" class="mantissa-bit"/>
<text x="217" y="180" class="bit-text">1</text>
<rect x="231" y="165" width="18" height="30" class="mantissa-bit"/>
<text x="240" y="180" class="bit-text">0</text>
<rect x="254" y="165" width="18" height="30" class="mantissa-bit"/>
<text x="263" y="180" class="bit-text">0</text>
<rect x="277" y="165" width="18" height="30" class="mantissa-bit"/>
<text x="286" y="180" class="bit-text">1</text>
<rect x="300" y="165" width="18" height="30" class="mantissa-bit"/>
<text x="309" y="180" class="bit-text">0</text>
<rect x="323" y="165" width="18" height="30" class="mantissa-bit"/>
<text x="332" y="180" class="bit-text">1</text>
<rect x="346" y="165" width="18" height="30" class="mantissa-bit"/>
<text x="355" y="180" class="bit-text">0</text>
<rect x="369" y="165" width="18" height="30" class="mantissa-bit"/>
<text x="378" y="180" class="bit-text">0</text>
<rect x="392" y="165" width="18" height="30" class="mantissa-bit"/>
<text x="401" y="180" class="bit-text">1</text>
<rect x="415" y="165" width="18" height="30" class="mantissa-bit"/>
<text x="424" y="180" class="bit-text">0</text>
<text x="820" y="180" class="value-text">≈ 0.3950</text>
</svg>
docs/features/low_precision_training/introduction/img/master_weights_approaches.svg 0 → 100644
View file @ 9df0c4a3
<?xml version="1.0" encoding="UTF-8" standalone="no"?>
<svg xmlns="http://www.w3.org/2000/svg" viewBox="0 0 1050 580" width="1050" height="580">
<defs>
<style>
@import url("../_static/css/diagram-colors.css");
.arrow { stroke: #616161; stroke-width: 2; fill: none; marker-end: url(#arrowhead); }
.column-title { font-family: 'Segoe UI', Arial, sans-serif; font-size: 14px; font-weight: 600; text-anchor: middle; fill: #424242; }
.divider { stroke: #bdbdbd; stroke-width: 1.5; stroke-dasharray: 8,6; }
</style>
<marker id="arrowhead" markerWidth="6" markerHeight="6" refX="5" refY="2" orient="auto">
<polygon points="0 0, 6 2, 0 4" fill="#616161" />
</marker>
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<!-- Title -->
<text x="525" y="30" class="title">Master Weights Storage Approaches</text>
<!-- Vertical dividers (dashed lines) -->
<line x1="350" y1="50" x2="350" y2="560" class="divider"/>
<line x1="700" y1="50" x2="700" y2="560" class="divider"/>
<!-- Column 1: Low Precision Only -->
<text x="175" y="75" class="column-title">Low Precision Weights</text>
<text x="175" y="93" class="small-text">(no master weights)</text>
<!-- Model box -->
<rect x="60" y="145" width="230" height="90" rx="6" fill="#f5f5f5" stroke="#9e9e9e" stroke-width="1.5"/>
<text x="175" y="168" class="label">Model</text>
<rect x="80" y="183" width="190" height="40" class="hp" rx="4"/>
<text x="175" y="208" class="text">Weights (BF16/FP16)</text>
<!-- Arrow down -->
<path d="M 175 235 L 175 300" class="arrow"/>
<!-- Computation -->
<rect x="90" y="300" width="170" height="50" class="gemm" rx="6"/>
<text x="175" y="330" class="text">Forward/Backward</text>
<!-- Arrow down -->
<path d="M 175 350 L 175 415" class="arrow"/>
<!-- Optimizer box -->
<rect x="60" y="415" width="230" height="90" rx="6" fill="#f5f5f5" stroke="#9e9e9e" stroke-width="1.5"/>
<text x="175" y="438" class="label">Optimizer</text>
<rect x="80" y="453" width="190" height="40" class="fp32" rx="4"/>
<text x="175" y="478" class="text">State (FP32)</text>
<!-- Column 2: Master Weights in Model -->
<text x="525" y="75" class="column-title">Master Weights in Model</text>
<!-- Model box -->
<rect x="410" y="145" width="230" height="90" rx="6" fill="#f5f5f5" stroke="#9e9e9e" stroke-width="1.5"/>
<text x="525" y="168" class="label">Model</text>
<rect x="430" y="183" width="190" height="40" class="fp32" rx="4"/>
<text x="525" y="208" class="text">Weights (FP32)</text>
<!-- Arrow down with cast -->
<path d="M 525 235 L 525 300" class="arrow"/>
<rect x="465" y="255" width="120" height="26" class="quantize" rx="4"/>
<text x="525" y="273" class="small-text">cast to BF16/FP16</text>
<!-- Computation -->
<rect x="440" y="300" width="170" height="50" class="gemm" rx="6"/>
<text x="525" y="330" class="text">Forward/Backward</text>
<!-- Arrow down -->
<path d="M 525 350 L 525 415" class="arrow"/>
<!-- Optimizer box -->
<rect x="410" y="415" width="230" height="90" rx="6" fill="#f5f5f5" stroke="#9e9e9e" stroke-width="1.5"/>
<text x="525" y="438" class="label">Optimizer</text>
<rect x="430" y="453" width="190" height="40" class="fp32" rx="4"/>
<text x="525" y="478" class="text">State (FP32)</text>
<!-- Column 3: Master Weights in Optimizer -->
<text x="875" y="75" class="column-title">Master Weights in Optimizer</text>
<!-- Cast box above Model -->
<rect x="815" y="105" width="120" height="26" class="quantize" rx="4"/>
<text x="875" y="123" class="small-text">cast to BF16/FP16</text>
<!-- Arrow from cast to Model -->
<path d="M 875 131 L 875 145" class="arrow"/>
<!-- Model box -->
<rect x="760" y="145" width="230" height="90" rx="6" fill="#f5f5f5" stroke="#9e9e9e" stroke-width="1.5"/>
<text x="875" y="168" class="label">Model</text>
<rect x="780" y="183" width="190" height="40" class="hp" rx="4"/>
<text x="875" y="208" class="text">Weights (BF16/FP16)</text>
<!-- Arrow down -->
<path d="M 875 235 L 875 300" class="arrow"/>
<!-- Computation -->
<rect x="790" y="300" width="170" height="50" class="gemm" rx="6"/>
<text x="875" y="330" class="text">Forward/Backward</text>
<!-- Arrow down -->
<path d="M 875 350 L 875 415" class="arrow"/>
<!-- Optimizer box with State and Master -->
<rect x="760" y="415" width="230" height="140" rx="6" fill="#f5f5f5" stroke="#9e9e9e" stroke-width="1.5"/>
<text x="875" y="438" class="label">Optimizer</text>
<rect x="780" y="453" width="190" height="40" class="fp32" rx="4"/>
<text x="875" y="478" class="text">State (FP32)</text>
<rect x="780" y="503" width="190" height="40" class="fp32" rx="4"/>
<text x="875" y="528" class="text">Master (FP32)</text>
<!-- Arrow from Master to cast -->
<path d="M 970 523 L 1010 523 L 1010 118 L 935 118" class="arrow"/>
</svg>
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