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[PyTorch] Explicitly specify quantized tensor usages needed for linear op backward #1646

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Merged
timmoon10 merged 1 commit into from
Apr 9, 2025
Merged

[PyTorch] Explicitly specify quantized tensor usages needed for linear op backward #1646

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9 changes: 6 additions & 3 deletions tests/pytorch/test_fusible_ops.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1420,15 +1420,17 @@ def test_activation(
test_device=device,
test_is_fp8=quantized_compute,
)
if quantized_compute:
with torch.no_grad():
x_test = x_test.dequantize().requires_grad_()
dy_ref, dy_test = make_reference_and_test_tensors(
out_shape,
test_dtype=dtype,
test_device=device,
test_is_fp8=quantized_compute,
requires_grad=False,
)
if quantized_compute:
with torch.no_grad():
x_test = x_test.dequantize().requires_grad_()
dy_test = dy_test.dequantize()

# Plain PyTorch implementation
y_ref: torch.Tensor
Expand Down Expand Up @@ -1459,6 +1461,7 @@ def test_activation(
swiglu=te_ops.SwiGLU,
)[activation]
forward = te_ops.Sequential(
te_ops.Quantize(forward=False, backward=quantized_compute),
make_op(),
te_ops.Quantize(forward=quantized_compute, backward=False),
)
Expand Down
32 changes: 19 additions & 13 deletions transformer_engine/pytorch/ops/basic/basic_linear.py
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Original file line number Diff line number Diff line change
Expand Up @@ -523,7 +523,7 @@ def _functional_forward(

# Configure input tensor for backward pass
if own_quantized_x_local:
x_local.update_usage(rowwise_usage=False)
x_local.update_usage(rowwise_usage=False, columnwise_usage=True)

# Detach input tensor if needed
# Note: PyTorch autograd produces esoteric errors if we save
Expand Down Expand Up @@ -679,7 +679,9 @@ def _functional_backward(
quantizer=input_quantizer,
)
else:
if not isinstance(x_local, QuantizedTensor):
if isinstance(x_local, QuantizedTensor):
x_local.update_usage(columnwise_usage=True)
else:
x_local = input_quantizer(x_local)
x = x_local
else:
Expand All @@ -706,15 +708,19 @@ def _functional_backward(
raise ValueError("Weight tensor is required to compute input grad")
w = weight
w_is_quantized = isinstance(w, QuantizedTensor)
if with_quantized_compute and not w_is_quantized:
if weight_quantizer is None:
raise ValueError("Missing quantizer for weight tensor")
weight_quantizer.set_usage(columnwise=True)
w = weight_quantizer(w)
elif not with_quantized_compute and w_is_quantized:
w = w.dequantize()
if not with_quantized_compute and w.dtype != dtype:
w = w.to(dtype=dtype)
if with_quantized_compute:
if w_is_quantized:
w.update_usage(columnwise_usage=True)
else:
if weight_quantizer is None:
raise ValueError("Missing quantizer for weight tensor")
weight_quantizer.set_usage(columnwise=True)
w = weight_quantizer(w)
else:
if w_is_quantized:
w = w.dequantize(dtype=dtype)
elif w.dtype != dtype:
w = w.to(dtype=dtype)

# Synchronize tensor-parallel communication
_wait_async(dy_async)
Expand Down Expand Up @@ -867,8 +873,8 @@ def op_forward(
# Configure quantizers
# Note: We cache the quantized input for backward pass,
# but discard the quantized weights.
input_quantizer.set_usage(columnwise=weight_requires_grad)
weight_quantizer.set_usage(columnwise=False)
input_quantizer.set_usage(rowwise=True, columnwise=weight_requires_grad)
weight_quantizer.set_usage(rowwise=True, columnwise=False)

# Get autocast dtype if needed
dtype = None
Expand Down