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override dgrad precision #1

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9 changes: 5 additions & 4 deletions transformer_engine/common/recipe.py
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Original file line number Diff line number Diff line change
Expand Up @@ -133,7 +133,8 @@ def scaling_factor_compute(amax: Tensor,

def __post_init__(self) -> None:
assert self.fp8_format != Format.E5M2, "Pure E5M2 training is not supported."
assert self.override_linear_precision in (
(False, False, False),
(False, False, True),
), "Only wgrad GEMM override is currently supported."
assert self.override_linear_precision[0] == False, "Fprop GEMM Override is disabled"
#assert self.override_linear_precision in (
# (False, False, False),
# (False, False, True),
#), "Only wgrad GEMM override is currently supported."
154 changes: 99 additions & 55 deletions transformer_engine/pytorch/module.py
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Expand Up @@ -906,20 +906,31 @@ def backward(
ctx.fp8_meta["recipe"], fprop_tensor=False
)

# DGRAD: Evaluated unconditionally to feed into Linear backward
dgrad = fp8_gemm(
weight_t_fp8,
fwd_scale_inverses,
tex.FP8FwdTensors.GEMM1_WEIGHT,
fp8_dtype_forward,
grad_output_c,
ctx.fp8_meta["scaling_bwd"].scale_inv,
tex.FP8BwdTensors.GRAD_OUTPUT1,
fp8_dtype_backward,
ctx.activation_dtype,
get_workspace(),
use_split_accumulator=_2X_ACC_DGRAD,
)
if not ctx.fp8_meta["recipe"].override_linear_precision.dgrad:
# DGRAD: Evaluated unconditionally to feed into Linear backward
dgrad = fp8_gemm(
weight_t_fp8,
fwd_scale_inverses,
tex.FP8FwdTensors.GEMM1_WEIGHT,
fp8_dtype_forward,
grad_output_c,
ctx.fp8_meta["scaling_bwd"].scale_inv,
tex.FP8BwdTensors.GRAD_OUTPUT1,
fp8_dtype_backward,
ctx.activation_dtype,
get_workspace(),
use_split_accumulator=_2X_ACC_DGRAD,
)
else:
# Is weight already in bf16?
dgrad, _, _ = gemm(
weight,
grad_output,
ctx.activation_dtype,
get_workspace(),
layout="NN",
grad=True,
)
else:
# DGRAD: Evaluated unconditionally to feed into Linear backward
dgrad, _, _ = gemm(
Expand Down Expand Up @@ -1598,20 +1609,30 @@ def backward(
ctx.fp8_meta["recipe"], fprop_tensor=False
)

# DGRAD
dgrad = fp8_gemm(
weight_t_fp8,
fwd_scale_inverses,
tex.FP8FwdTensors.GEMM1_WEIGHT,
fp8_dtype_forward,
grad_output_c,
ctx.fp8_meta["scaling_bwd"].scale_inv,
tex.FP8BwdTensors.GRAD_OUTPUT1,
fp8_dtype_backward,
ctx.activation_dtype,
get_workspace(),
use_split_accumulator=_2X_ACC_DGRAD,
)
if not ctx.fp8_meta["recipe"].override_linear_precision.dgrad:
# DGRAD
dgrad = fp8_gemm(
weight_t_fp8,
fwd_scale_inverses,
tex.FP8FwdTensors.GEMM1_WEIGHT,
fp8_dtype_forward,
grad_output_c,
ctx.fp8_meta["scaling_bwd"].scale_inv,
tex.FP8BwdTensors.GRAD_OUTPUT1,
fp8_dtype_backward,
ctx.activation_dtype,
get_workspace(),
use_split_accumulator=_2X_ACC_DGRAD,
)
else:
dgrad, _, _ = gemm(
weight,
grad_output,
ctx.activation_dtype,
get_workspace(),
layout="NN",
grad=True,
)
else:
# DGRAD
dgrad, _, _ = gemm(
Expand Down Expand Up @@ -2321,21 +2342,33 @@ def backward(
fp8_dtype_backward = get_fp8_te_dtype(
ctx.fp8_meta["recipe"], fprop_tensor=False
)
if not ctx.fp8_meta["recipe"].override_linear_precision.dgrad:
# FC2 DGRAD; Unconditional
fc2_dgrad = fp8_gemm(
fc2_weight_t_fp8,
fwd_scale_inverses,
tex.FP8FwdTensors.GEMM2_WEIGHT,
fp8_dtype_forward,
grad_output_c,
ctx.fp8_meta["scaling_bwd"].scale_inv,
tex.FP8BwdTensors.GRAD_OUTPUT1,
fp8_dtype_backward,
ctx.activation_dtype,
get_workspace(),
use_split_accumulator=_2X_ACC_DGRAD,
)
else:
fc2_dgrad, _, _ = gemm(
fc2_weight,
grad_output,
ctx.activation_dtype,
get_workspace(),
layout="NN",
gelu=not ctx.bias_gelu_nvfusion,
grad=True,
gelu_input=fc1_out,
)

# FC2 DGRAD; Unconditional
fc2_dgrad = fp8_gemm(
fc2_weight_t_fp8,
fwd_scale_inverses,
tex.FP8FwdTensors.GEMM2_WEIGHT,
fp8_dtype_forward,
grad_output_c,
ctx.fp8_meta["scaling_bwd"].scale_inv,
tex.FP8BwdTensors.GRAD_OUTPUT1,
fp8_dtype_backward,
ctx.activation_dtype,
get_workspace(),
use_split_accumulator=_2X_ACC_DGRAD,
)

# FC2 WGRAD
if not ctx.fp8_meta["recipe"].override_linear_precision.wgrad:
Expand Down Expand Up @@ -2402,19 +2435,30 @@ def backward(
dgelu_t = None

# FC1 DGRAD: Unconditional
fc1_dgrad = fp8_gemm(
fc1_weight_t_fp8,
fwd_scale_inverses,
tex.FP8FwdTensors.GEMM1_WEIGHT,
fp8_dtype_forward,
dgelu,
ctx.fp8_meta["scaling_bwd"].scale_inv,
tex.FP8BwdTensors.GRAD_OUTPUT2,
fp8_dtype_backward,
ctx.activation_dtype,
get_workspace(),
use_split_accumulator=_2X_ACC_DGRAD,
)
if not ctx.fp8_meta["recipe"].override_linear_precision.dgrad:
fc1_dgrad = fp8_gemm(
fc1_weight_t_fp8,
fwd_scale_inverses,
tex.FP8FwdTensors.GEMM1_WEIGHT,
fp8_dtype_forward,
dgelu,
ctx.fp8_meta["scaling_bwd"].scale_inv,
tex.FP8BwdTensors.GRAD_OUTPUT2,
fp8_dtype_backward,
ctx.activation_dtype,
get_workspace(),
use_split_accumulator=_2X_ACC_DGRAD,
)
else:
#NOTE: This will not work if wgrad is in FP8
fc1_dgrad, _, _ = gemm(
fc1_weight,
dgelu_no_fp8,
ctx.activation_dtype,
get_workspace(),
layout="NN",
grad=True,
)
else:
# FC2 DGRAD; Unconditional
fc2_dgrad, _, _ = gemm(
Expand Down