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[PyTorch] Fix CP A2A F16 when NVTE_FP8_DPA_BWD=1 #2917

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Merged
cyanguwa merged 1 commit into from
Apr 23, 2026
Merged

[PyTorch] Fix CP A2A F16 when NVTE_FP8_DPA_BWD=1 #2917

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28 changes: 14 additions & 14 deletions transformer_engine/pytorch/attention/dot_product_attention/context_parallel.py
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Original file line number Diff line number Diff line change
Expand Up @@ -1469,7 +1469,8 @@ def forward(
fwd_nominal_dtype = q.dtype
is_input_fp8 = isinstance(q, QuantizedTensorStorage)
is_output_fp8 = fp8_output
is_bwd_fp8 = int(os.getenv("NVTE_FP8_DPA_BWD", "1"))
_use_fp8_dpa_bwd = bool(int(os.getenv("NVTE_FP8_DPA_BWD", "1")))
is_bwd_fp8 = fp8 and _use_fp8_dpa_bwd
# recipe passed in through autocast or set by NVTE_DPA_FP8_RECIPE;
# may be different from fp8_meta["recipe"]
fp8_recipe = FP8GlobalStateManager.get_fp8_recipe()
Expand Down Expand Up @@ -2063,20 +2064,17 @@ def forward(
# prepare for return and ctx saves
out_fp8 = None
out_f16 = out.to(fwd_nominal_dtype)
if fp8 and (
is_output_fp8
or (
is_bwd_fp8
and not (fp8_recipe.float8_current_scaling() and _dpa_fp8_cs_o_in_f16)
and not fp8_recipe.mxfp8()
)
if (fp8 and is_output_fp8) or (
is_bwd_fp8
and not (fp8_recipe.float8_current_scaling() and _dpa_fp8_cs_o_in_f16)
and not fp8_recipe.mxfp8()
):
out_fp8 = O_quantizer(out_f16)
out_ret = out_fp8 if (fp8 and is_output_fp8) else out_f16

ctx.layer_number = layer_number
ctx.fp8_recipe = fp8_recipe
ctx.fp8 = fp8 and is_bwd_fp8
ctx.fp8 = is_bwd_fp8

kv_fp8 = None
kv = p2p_comm_buffers[-1]
Expand Down Expand Up @@ -3063,7 +3061,8 @@ def forward(
), "q, k, v must be of the same class, e.g. torch.Tensor or QuantizedTensorStorage."
is_input_fp8 = isinstance(q, QuantizedTensorStorage)
is_output_fp8 = fp8_output
is_bwd_fp8 = int(os.getenv("NVTE_FP8_DPA_BWD", "1"))
_use_fp8_dpa_bwd = bool(int(os.getenv("NVTE_FP8_DPA_BWD", "1")))
is_bwd_fp8 = fp8 and _use_fp8_dpa_bwd
fp8_recipe = FP8GlobalStateManager.get_fp8_recipe()
if fp8_meta is not None and fp8_meta.get("local_recipes", None) is not None:
fp8_recipe = fp8_meta["local_recipes"][0]
Expand Down Expand Up @@ -3306,12 +3305,12 @@ def forward(
or (fp8_recipe.float8_current_scaling() and not _dpa_fp8_cs_o_in_f16)
)
)
if fp8 and (is_output_fp8 or bwd_requires_o_fp8):
if (fp8 and is_output_fp8) or bwd_requires_o_fp8:
out_fp8 = O_quantizer(out_f16)
out_ret = out_fp8 if is_output_fp8 else out_f16

# save tensors for backward
ctx.fp8 = fp8 and is_bwd_fp8
ctx.fp8 = is_bwd_fp8
ctx.fp8_recipe = fp8_recipe
fp8_tensors = (None, None, None, None)
f16_tensors = (None, None, None, None)
Expand Down Expand Up @@ -3931,7 +3930,8 @@ def forward(
), "q, k, v must be of the same class, e.g. torch.Tensor or QuantizedTensorStorage."
is_input_fp8 = isinstance(q, QuantizedTensorStorage)
is_output_fp8 = fp8_output
is_bwd_fp8 = int(os.getenv("NVTE_FP8_DPA_BWD", "1"))
_use_fp8_dpa_bwd = bool(int(os.getenv("NVTE_FP8_DPA_BWD", "1")))
is_bwd_fp8 = fp8 and _use_fp8_dpa_bwd
# recipe passed in through autocast or set by NVTE_DPA_FP8_RECIPE;
# may be different from fp8_meta["recipe"]
fp8_recipe = FP8GlobalStateManager.get_fp8_recipe()
Expand Down Expand Up @@ -4161,7 +4161,7 @@ def forward(
ctx.orig_o_shape = orig_o_shape

# save tensors for backward
ctx.fp8 = fp8 and is_bwd_fp8
ctx.fp8 = is_bwd_fp8
fp8_tensors = (None, None, None, None)
f16_tensors = (None, None, None, None)
if is_training:
Expand Down
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