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fprob bf16 #2

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b88d2ec
fprop bf16
anmolgupt Apr 26, 2023
41f6340
fixes for fprop_bf16
anmolgupt Apr 26, 2023
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8 changes: 4 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 +133,7 @@ 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 in (
# (False, False, False),
# (False, False, True),
#), "Only wgrad GEMM override is currently supported."
120 changes: 112 additions & 8 deletions transformer_engine/pytorch/module.py
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Expand Up @@ -695,6 +695,7 @@ def forward(
if fp8:
fp8_dtype_forward = get_fp8_te_dtype(fp8_meta["recipe"], fprop_tensor=True)

if fp8 and not fp8_meta["recipe"].override_linear_precision.fprop:
if not return_layernorm_output:
if is_grad_enabled:
ln_out, mu, rsigma = layernorm_fwd_fp8(
Expand Down Expand Up @@ -753,7 +754,7 @@ def forward(
else:
ln_out_total = ln_out

if fp8:
if fp8 and not fp8_meta["recipe"].override_linear_precision.fprop:
bias_dtype = (
torch.bfloat16
if activation_dtype == torch.float32
Expand Down Expand Up @@ -816,6 +817,32 @@ def forward(
use_bias=use_bias,
)

if fp8:
if update_fp8_weights:
if is_grad_enabled:
fp8_cast_transpose_fused(
weight,
fp8_meta["scaling_fwd"],
tex.FP8FwdTensors.GEMM1_WEIGHT,
fp8_dtype_forward,
cast_out=weight_fp8,
transpose_out=weight_t_fp8,
)
else:
weight_t_fp8 = None
weight_fp8 = cast_to_fp8(
weight,
fp8_meta["scaling_fwd"],
tex.FP8FwdTensors.GEMM1_WEIGHT,
fp8_dtype_forward)

ln_out = cast_to_fp8(
ln_out,
fp8_meta["scaling_fwd"],
tex.FP8FwdTensors.GEMM1_INPUT,
fp8_dtype_forward,
)

if is_grad_enabled:
ctx.save_for_backward(
inputmat,
Expand Down Expand Up @@ -1413,11 +1440,9 @@ def forward(
), "Input and weight dimensions are not compatible for FP8 execution."

update_fp8_weights = is_first_microbatch is None or is_first_microbatch

# Cast for native AMP
inputmat = cast_if_needed(inputmat, activation_dtype)
inputmat_no_fp8 = inputmat

if fp8:
fp8_dtype_forward = get_fp8_te_dtype(fp8_meta["recipe"], fprop_tensor=True)

Expand Down Expand Up @@ -1445,12 +1470,18 @@ def forward(
), None

# Column Parallel Linear
if parallel_mode == "column" and sequence_parallel:
inputmat_total, _ = gather_along_first_dim(inputmat, tp_group)
if not fp8_meta["recipe"].override_linear_precision.fprop:
if parallel_mode == "column" and sequence_parallel:
inputmat_total, _ = gather_along_first_dim(inputmat, tp_group)
else:
inputmat_total = inputmat
else:
inputmat_total = inputmat
if parallel_mode == "column" and sequence_parallel:
inputmat_total, _ = gather_along_first_dim(inputmat_no_fp8, tp_group)
else:
inputmat_total = inputmat_no_fp8

if fp8:
if fp8 and not fp8_meta["recipe"].override_linear_precision.fprop:
bias_dtype = (
torch.bfloat16
if activation_dtype == torch.float32
Expand Down Expand Up @@ -1514,6 +1545,26 @@ def forward(
use_bias=use_bias,
)

if fp8:
if update_fp8_weights:
if is_grad_enabled:
fp8_cast_transpose_fused(
weight,
fp8_meta["scaling_fwd"],
tex.FP8FwdTensors.GEMM1_WEIGHT,
fp8_dtype_forward,
cast_out=weight_fp8,
transpose_out=weight_t_fp8,
)
else:
weight_t_fp8 = None
weight_fp8 = cast_to_fp8(
weight,
fp8_meta["scaling_fwd"],
tex.FP8FwdTensors.GEMM1_WEIGHT,
fp8_dtype_forward,
)

if is_grad_enabled:
fp8_wgrad = fp8 and not fp8_meta["recipe"].override_linear_precision.wgrad
ctx.save_for_backward(
Expand Down Expand Up @@ -2040,6 +2091,8 @@ def forward(
# of an extra fp8 cast.
if fp8:
fp8_dtype_forward = get_fp8_te_dtype(fp8_meta["recipe"], fprop_tensor=True)

if fp8 and not fp8_meta["recipe"].override_linear_precision.fprop:
if not return_layernorm_output:
if is_grad_enabled:
ln_out, mu, rsigma = layernorm_fwd_fp8(
Expand Down Expand Up @@ -2091,7 +2144,7 @@ def forward(
else:
ln_out_total = ln_out

if fp8:
if fp8 and not fp8_meta["recipe"].override_linear_precision.fprop:
bias_dtype = (
torch.bfloat16
if activation_dtype == torch.float32
Expand Down Expand Up @@ -2222,6 +2275,57 @@ def forward(
bias=fc2_bias,
use_bias=use_bias,
)

if fp8:
if update_fp8_weights:
if is_grad_enabled:
fp8_cast_transpose_fused(
fc1_weight,
fp8_meta["scaling_fwd"],
tex.FP8FwdTensors.GEMM1_WEIGHT,
fp8_dtype_forward,
cast_out=fc1_weight_fp8,
transpose_out=fc1_weight_t_fp8,
)

fp8_cast_transpose_fused(
fc2_weight,
fp8_meta["scaling_fwd"],
tex.FP8FwdTensors.GEMM2_WEIGHT,
fp8_dtype_forward,
cast_out=fc2_weight_fp8,
transpose_out=fc2_weight_t_fp8,
)
else:
fc1_weight_t_fp8 = None
fc1_weight_fp8 = cast_to_fp8(
fc1_weight,
fp8_meta["scaling_fwd"],
tex.FP8FwdTensors.GEMM1_WEIGHT,
fp8_dtype_forward,
)
fc2_weight_t_fp8 = None
fc2_weight_fp8 = cast_to_fp8(
fc2_weight,
fp8_meta["scaling_fwd"],
tex.FP8FwdTensors.GEMM2_WEIGHT,
fp8_dtype_forward,
)

ln_out = cast_to_fp8(
ln_out,
fp8_meta["scaling_fwd"],
tex.FP8FwdTensors.GEMM1_INPUT,
fp8_dtype_forward,
)

gelu_out = cast_to_fp8(
gelu_out,
fp8_meta["scaling_fwd"],
tex.FP8FwdTensors.GEMM2_INPUT,
fp8_dtype_forward,
)

if is_grad_enabled:
ctx.save_for_backward(
inputmat,
Expand Down