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[Doc] Fix doc build error in e2e_opt_model.py #17319

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Merged
tqchen merged 1 commit into from
Sep 2, 2024
Merged

[Doc] Fix doc build error in e2e_opt_model.py #17319

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63 changes: 31 additions & 32 deletions docs/how_to/tutorials/e2e_opt_model.py
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Original file line number Diff line number Diff line change
Expand Up @@ -32,7 +32,6 @@
# PyTorch.

import os
import sys
import numpy as np
import torch
from torch import fx
Expand Down Expand Up @@ -101,39 +100,39 @@

# Skip running in CI environment
IS_IN_CI = os.getenv("CI", "") == "true"
if IS_IN_CI:
sys.exit(0)

with target:
mod = tvm.ir.transform.Sequential(
[
# Convert BatchNorm into a sequence of simpler ops for fusion
relax.transform.DecomposeOpsForInference(),
# Canonicalize the bindings
relax.transform.CanonicalizeBindings(),
# Run default optimization pipeline
relax.get_pipeline("zero"),
# Tune the model and store the log to database
relax.transform.MetaScheduleTuneIRMod({}, work_dir, TOTAL_TRIALS),
# Apply the database
relax.transform.MetaScheduleApplyDatabase(work_dir),
]
)(mod)

# Only show the main function
mod["main"].show()
if not IS_IN_CI:
with target:
mod = tvm.ir.transform.Sequential(
[
# Convert BatchNorm into a sequence of simpler ops for fusion
relax.transform.DecomposeOpsForInference(),
# Canonicalize the bindings
relax.transform.CanonicalizeBindings(),
# Run default optimization pipeline
relax.get_pipeline("zero"),
# Tune the model and store the log to database
relax.transform.MetaScheduleTuneIRMod({}, work_dir, TOTAL_TRIALS),
# Apply the database
relax.transform.MetaScheduleApplyDatabase(work_dir),
]
)(mod)

# Only show the main function
mod["main"].show()

######################################################################
# Build and Deploy
# ----------------
# Finally, we build the optimized model and deploy it to the target device.

ex = relax.build(mod, target="cuda")
dev = tvm.device("cuda", 0)
vm = relax.VirtualMachine(ex, dev)
# Need to allocate data and params on GPU device
gpu_data = tvm.nd.array(np.random.rand(1, 3, 224, 224).astype("float32"), dev)
gpu_params = [tvm.nd.array(p, dev) for p in params["main"]]
gpu_out = vm["main"](gpu_data, *gpu_params).numpy()

print(gpu_out.shape)
# We skip this step in the CI environment.

if not IS_IN_CI:
ex = relax.build(mod, target="cuda")
dev = tvm.device("cuda", 0)
vm = relax.VirtualMachine(ex, dev)
# Need to allocate data and params on GPU device
gpu_data = tvm.nd.array(np.random.rand(1, 3, 224, 224).astype("float32"), dev)
gpu_params = [tvm.nd.array(p, dev) for p in params["main"]]
gpu_out = vm["main"](gpu_data, *gpu_params).numpy()

print(gpu_out.shape)