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Update docs in executorch, remove capture_pre_autograd_graph references #6613

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Nov 1, 2024
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Update docs in executorch, remove capture_pre_autograd_graph references #6613

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4 changes: 2 additions & 2 deletions backends/apple/coreml/README.md
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Original file line number Diff line number Diff line change
Expand Up @@ -65,7 +65,7 @@ To quantize a Program in a Core ML favored way, the client may utilize **CoreMLQ
import torch
import executorch.exir

from torch._export import capture_pre_autograd_graph
from torch.export import export_for_training
from torch.ao.quantization.quantize_pt2e import (
convert_pt2e,
prepare_pt2e,
Expand Down Expand Up @@ -93,7 +93,7 @@ class Model(torch.nn.Module):
source_model = Model()
example_inputs = (torch.randn((1, 3, 256, 256)), )

pre_autograd_aten_dialect = capture_pre_autograd_graph(model, example_inputs)
pre_autograd_aten_dialect = export_for_training(model, example_inputs).module()

quantization_config = LinearQuantizerConfig.from_dict(
{
Expand Down
11 changes: 5 additions & 6 deletions docs/source/llm/getting-started.md
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Expand Up @@ -144,8 +144,7 @@ import torch

from executorch.exir import EdgeCompileConfig, to_edge
from torch.nn.attention import sdpa_kernel, SDPBackend
from torch._export import capture_pre_autograd_graph
from torch.export import export
from torch.export import export, export_for_training

from model import GPT

Expand All @@ -170,7 +169,7 @@ dynamic_shape = (
# Trace the model, converting it to a portable intermediate representation.
# The torch.no_grad() call tells PyTorch to exclude training-specific logic.
with torch.nn.attention.sdpa_kernel([SDPBackend.MATH]), torch.no_grad():
m = capture_pre_autograd_graph(model, example_inputs, dynamic_shapes=dynamic_shape)
m = export_for_training(model, example_inputs, dynamic_shapes=dynamic_shape).module()
traced_model = export(m, example_inputs, dynamic_shapes=dynamic_shape)

# Convert the model into a runnable ExecuTorch program.
Expand Down Expand Up @@ -462,7 +461,7 @@ from executorch.exir import EdgeCompileConfig, to_edge
import torch
from torch.export import export
from torch.nn.attention import sdpa_kernel, SDPBackend
from torch._export import capture_pre_autograd_graph
from torch.export import export_for_training

from model import GPT

Expand All @@ -489,7 +488,7 @@ dynamic_shape = (
# Trace the model, converting it to a portable intermediate representation.
# The torch.no_grad() call tells PyTorch to exclude training-specific logic.
with torch.nn.attention.sdpa_kernel([SDPBackend.MATH]), torch.no_grad():
m = capture_pre_autograd_graph(model, example_inputs, dynamic_shapes=dynamic_shape)
m = export_for_training(model, example_inputs, dynamic_shapes=dynamic_shape).module()
traced_model = export(m, example_inputs, dynamic_shapes=dynamic_shape)

# Convert the model into a runnable ExecuTorch program.
Expand Down Expand Up @@ -635,7 +634,7 @@ xnnpack_quant_config = get_symmetric_quantization_config(
xnnpack_quantizer = XNNPACKQuantizer()
xnnpack_quantizer.set_global(xnnpack_quant_config)

m = capture_pre_autograd_graph(model, example_inputs)
m = export_for_training(model, example_inputs).module()

# Annotate the model for quantization. This prepares the model for calibration.
m = prepare_pt2e(m, xnnpack_quantizer)
Expand Down
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