10000 [PT2][Optimus][fp8 compuation quantizatoin] Add fallback logic by mengluy0125 · Pull Request #153430 · pytorch/pytorch · GitHub
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[PT2][Optimus][fp8 compuation quantizatoin] Add fallback logic #153430

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[PT2][Optimus][fp8 compuation quantizatoin] Add fallback logic #153430

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42 changes: 26 additions & 16 deletions torch/_functorch/partitioners.py
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Original file line number Diff line number Diff line change
Expand Up @@ -517,11 +517,21 @@ def calculate_range(dtype: torch.dtype) -> tuple:
# 8-bit floating-point format with e5m2 layout
min_val = -57344.0
max_val = 57344.0
if dtype == torch.float8_e4m3fn:
min_val = -448
max_val = 448
else:
raise ValueError(f"Unsupported dtype: {dtype}")
return min_val, max_val


def has_overflow(node: torch.fx.Node, max_val: float) -> bool:
abs = torch.ops.aten.abs.default(node.meta["val"])
amex = torch.ops.aten.amax.default(abs.meta["val"], [-1], True)

return amex.item() > max_val


def quantize_activation_fw(graph: torch.fx.Graph) -> None:
output = graph.find_nodes(op="output")[0]
fwd_outputs = output.args[0]
Expand All @@ -532,23 +542,10 @@ def quantize_activation_fw(graph: torch.fx.Graph) -> None:
for node in fwd_outputs:
# check if the activation node is the node saved for quantization
if node.meta.get("saved_for_quantization", False):
# case: use scaling
if torch._inductor.config.post_grad_fusion_options[
# use non-scaling in default, fallback to scaling when overflow
if not torch._inductor.config.post_grad_fusion_options[
"activation_quantization_aten_pass"
].get("use_scaling", False):
# calculating the scale
scale_node = calculate_quantization_scaling(
graph, node, clamp_max, 1e-12
)
# converting to fp8
quant_node = perform_quantization(
graph, node, scale_node, quant_type, clamp_min, clamp_max
)
if not is_sym_node(scale_node):
tensor_scale_nodes.append(scale_node)
else:
sym_scale_nodes.append(scale_node)
else:
].get("use_scaling", False) and not has_overflow(node, clamp_max):
# case: do not use scaling
with graph.inserting_after(node):
quant_node = graph.call_function(
Expand All @@ -564,6 +561,19 @@ def quantize_activation_fw(graph: torch.fx.Graph) -> None:
quant_node.meta["tensor_meta"] = extract_tensor_metadata(
quant_node.meta["val"]
)
else:
# calculating the scale
scale_node = calculate_quantization_scaling(
graph, node, clamp_max, 1e-12
)
# converting to fp8
quant_node = perform_quantization(
graph, node, scale_node, quant_type, clamp_min, clamp_max
)
if not is_sym_node(scale_node):
tensor_scale_nodes.append(scale_node)
else:
sym_scale_nodes.append(scale_node)
node_to_quant[node] = quant_node
# only update the return node args, and remain all other users unchanged
output_updated_args = [
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