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Group bulk write requests by device list. It avoids setting devices repeatedly for each request. #59

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Group bulk write requests by device list. It avoids setting devices repeatedly for each request. #59

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91 changes: 70 additions & 21 deletions pathwaysutils/persistence/helper.py
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Original file line number Diff line number Diff line change
Expand Up @@ -168,35 +168,52 @@ def get_read_request(
dtype: np.dtype,
shape: Sequence[int],
sharding: jax.sharding.Sharding,
devices: Sequence[jax.Device],
devices: Sequence[jax.Device] | None,
timeout: datetime.timedelta,
return_dict: bool = False,
) -> Union[str, dict[str, Any]]:
"""Returns a string representation of the plugin program which reads the given array from the given location into the provided sharding."""
if not isinstance(devices, np.ndarray):
devices = np.array(devices)

timeout_seconds, timeout_fractional_seconds = divmod(
timeout.total_seconds(), 1
)
timeout_nanoseconds = timeout_fractional_seconds * 1e9
d = {
"persistenceReadRequest": {
"b64_location": string_to_base64(location_path),
"shape": get_shape_info(dtype, shape),
"b64_name": string_to_base64(name),
"b64_hlo_sharding_string": get_hlo_sharding_string(
sharding, len(shape)
),
"devices": {
"device_ids": [device.id for device in devices.flatten()]
},
"timeout": {
"seconds": int(timeout_seconds),
"nanos": int(timeout_nanoseconds),
},
}
}

if devices is None:
d = {
"persistenceReadRequest": {
"b64_location": string_to_base64(location_path),
"shape": get_shape_info(dtype, shape),
"b64_name": string_to_base64(name),
"b64_hlo_sharding_string": get_hlo_sharding_string(
sharding, len(shape)
),
"timeout": {
"seconds": int(timeout_seconds),
"nanos": int(timeout_nanoseconds),
},
}
}
else:
if not isinstance(devices, np.ndarray):
devices = np.array(devices)

d = {
"persistenceReadRequest": {
"b64_location": string_to_base64(location_path),
"shape": get_shape_info(dtype, shape),
"b64_name": string_to_base64(name),
"b64_hlo_sharding_string": get_hlo_sharding_string(
sharding, len(shape)
),
"devices": {
"device_ids": [device.id for device in devices.flatten()]
},
"timeout": {
"seconds": int(timeout_seconds),
"nanos": int(timeout_nanoseconds),
},
}
}

if return_dict:
return d
Expand Down Expand Up @@ -224,6 +241,38 @@ def get_bulk_read_request(
)


def get_bulk_read_request_per_device_list(
location_path: str,
names: Sequence[str],
dtypes: Sequence[np.dtype],
shapes: Sequence[Sequence[int]],
shardings: Sequence[jax.sharding.Sharding],
devices: Sequence[jax.Device],
timeout: datetime.timedelta,
) -> str:
"""Returns a string representation of a bulk read request, reads multiple arrays with one call."""
read_requests = [
get_read_request(
location_path, name, dtype, shape, sharding, None, timeout, True
)["persistenceReadRequest"]
for name, dtype, shape, sharding in zip(names, dtypes, shapes, shardings)
]

if not isinstance(devices, np.ndarray):
devices = np.array(devices)

return json.dumps({
"bulk_persistence_read_request": {
"read_requests_per_device_list": {
"device_list": {
"device_ids": [device.id for device in devices.flatten()]
},
"read_requests": read_requests,
}
}
})


def write_one_array(
location: str,
name: str,
Expand Down