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Record min/max of integral tensor in ET #191

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Record min/max of integral tensor in ET #191

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16 changes: 16 additions & 0 deletions et_replay/execution_trace.py
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Original file line number Diff line number Diff line change
Expand Up @@ -128,6 +128,7 @@ def __init__(
comm_args: _CommArgs | None = None,
input_strides: list[Any] | None = None,
output_strides: list[Any] | None = None,
tensor_range: str | None = None,
):
self.name: str = name
self.parent_id: int = parent_id
Expand Down Expand Up @@ -156,6 +157,11 @@ def __init__(
self.output_shapes: list[Any] = output_shapes
self.output_strides: list[Any] | None = output_strides
self.commArgs: _CommArgs | None = comm_args
self.tensor_range = json.loads(tensor_range) if tensor_range else None
if self.tensor_range is not None:
self.tensor_range = {
int(index): min_max for index, min_max in self.tensor_range.items()
}

def get_inputs(self) -> Iterable:
return zip(self.input_types, self.inputs, self.input_shapes)
Expand Down Expand Up @@ -293,6 +299,12 @@ def get_input_tensor_strides(self) -> list[tuple] | None:
else:
return self.get_tensor_strides(self.get_inputs(), self.input_strides)

def get_input_tensor_range(self, tensor_index) -> tuple | None:
if self.tensor_range is None or tensor_index not in self.tensor_range:
return None
else:
return self.tensor_range[tensor_index]

def get_output_tensor_strides(self) -> list[tuple] | None:
if self.output_strides is None:
return None
Expand Down Expand Up @@ -404,6 +416,7 @@ def schema_chakra(self) -> tuple[int, int, int]:
"tid": int,
"kernel_backend": str,
"kernel_file": str,
"tensor_range": str,
}

@classmethod
Expand Down Expand Up @@ -477,6 +490,7 @@ def _create_node_v1_0_2_chakra_0_0_4(pid, x: dict[str, Any]) -> Node:
tid,
kernel_backend,
kernel_file,
_,
) = ExecutionTrace._read_attrs(x)

comm_attrs = (
Expand Down Expand Up @@ -520,6 +534,7 @@ def _create_node_v1_1_1_chakra_0_0_4(pid, x: dict[str, Any]) -> Node:
tid,
kernel_backend,
kernel_file,
tensor_range,
) = ExecutionTrace._read_attrs(x)

comm_attrs = (
Expand Down Expand Up @@ -551,6 +566,7 @@ def _create_node_v1_1_1_chakra_0_0_4(pid, x: dict[str, Any]) -> Node:
comm_attrs,
x["inputs"]["strides"],
x["outputs"]["strides"],
tensor_range,
)

def get_nodes(self, clean: bool = False):
Expand Down
34 changes: 32 additions & 2 deletions et_replay/tools/et_replay.py
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Original file line number Diff line number Diff line change
Expand Up @@ -662,6 +662,7 @@ def add_comm_tensor_registry(tensor_strides, tensors):
shape,
dtype,
strides,
node.get_input_tensor_range(idx),
)
self.tensor_registry_permanent[replay_t_id] = tensor
except KeyError:
Expand Down Expand Up @@ -724,6 +725,7 @@ def allocate_comp_tensors(self, node): # noqa: C901
shape,
dtype,
strides,
node.get_input_tensor_range(idx),
)
self.tensor_registry_permanent[replay_t_id] = tensor

Expand Down Expand Up @@ -1178,11 +1180,40 @@ def get_tensor_from_storage(
shape,
data_type,
strides,
tensor_range,
):
assert storage_id in self.tensor_storage_map
tensor_data = self.tensor_storage_map[storage_id]
if device not in tensor_data[1]:
if data_type in [torch.half, torch.float32, torch.float64, torch.bfloat16]:
if (
data_type
in [
torch.int8,
torch.int16,
torch.int32,
torch.int64,
torch.uint8,
torch.uint16,
torch.uint32,
torch.uint64,
torch.int,
torch.long,
]
and tensor_range is not None
):
storage_tensor = torch.randint(
tensor_range[0],
tensor_range[1] + 1,
(tensor_data[0] // elem_bytes,),
dtype=data_type,
device=device,
)
elif data_type in [
torch.half,
torch.float32,
torch.float64,
torch.bfloat16,
]:
storage_tensor = torch.rand(
(tensor_data[0] // elem_bytes), dtype=data_type, device=device
)
Expand Down Expand Up @@ -1369,7 +1400,6 @@ def run_op(self, node, iter, cnt): # noqa: C901
inputs, msg = self.get_data(node, True)
if msg != "":
return False, msg

# TODO: why need this hack?
# Workaround to eliminate the "strides() called on undefined Tensor" error.
if node.name == "aten::convolution_backward":
Expand Down
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