Support arbitrary output dtypes in PyT GEMM functions#75

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Merged
ksivaman merged 4 commits into from
Feb 23, 2023
Merged

Support arbitrary output dtypes in PyT GEMM functions #75

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c1bc953
Deprecate fp32_output option for PyT linear layers
timmoon10 Feb 15, 2023
3ab330a
Remove deprecated options
timmoon10 Feb 16, 2023
b6baff1
Merge branch 'main' into bf16-wgrad-bugfix
timmoon10 Feb 22, 2023
bfc386b
Merge branch 'main' into bf16-wgrad-bugfix
ksivaman Feb 23, 2023
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1 change: 0 additions & 1 deletion tests/test_onnx_export.py
View file
Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -371,7 +371,6 @@ def forward(self, inp, weight):
get_workspace(),
bias=self.bias,
use_bias=self.use_bias,
fp32_output=(self.precision==torch.float32),
use_split_accumulator=False)
return ret

Expand Down
28 changes: 14 additions & 14 deletions transformer_engine/pytorch/cpp_extensions.py
View file
Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -26,9 +26,8 @@ def fp8_gemm(
fp8_meta_tensor: tex.FP8TensorMeta = None,
bias: Optional[torch.Tensor] = None,
use_bias: bool = False,
fp32_output: bool = False,
use_split_accumulator: bool = False,
D_dtype: tex.DType = None,
D_dtype: Optional[tex.DType] = None,
) -> torch.Tensor:
"""TN layout GEMM with fp8 inputs."""

Expand All@@ -41,15 +40,14 @@ def fp8_gemm(
out = torch.empty(
B.shape[0],
A.shape[0],
dtype=torch.float32 if fp32_output else out_dtype,
dtype=out_dtype,
device="cuda",
)
return_output = True

out_dtype = tex.DType.kFloat32 if fp32_output else TE_DType[out_dtype]
out_dtype = TE_DType[out.dtype] if D_dtype is None else D_dtype
# Use bfloat16 as default bias_dtype
bias_dtype = tex.DType.kBFloat16 if bias is None else TE_DType[bias.dtype]
out_dtype = D_dtype if D_dtype is not None else out_dtype

_ = torch.ops.tex_ts.te_gemm_ts(
A,
Expand DownExpand Up@@ -94,7 +92,6 @@ def gemm(
out: Optional[torch.Tensor] = None,
bias: Optional[torch.Tensor] = None,
use_bias: bool = False,
fp32_output: bool = False,
) -> Tuple[Union[torch.Tensor, None], ...]:
"""Non FP8 GEMM."""

Expand All@@ -104,16 +101,12 @@ def gemm(
empty_tensor = torch.Tensor()
fp8_index = -1 # dummy index

input_dtype = TE_DType[dtype]
output_dtype = tex.DType.kFloat32 if fp32_output else input_dtype
bias_dtype = output_dtype if bias is None else TE_DType[bias.dtype]

return_output = False
if out is None:
out = torch.empty(
B.shape[1] if transb else B.shape[0],
A.shape[0] if transa else A.shape[1],
dtype=torch.float32 if fp32_output else dtype,
dtype=dtype,
device="cuda",
)
return_output = True
Expand All@@ -124,14 +117,21 @@ def gemm(
gelu_input = empty_tensor

if grad and use_bias:
grad_bias = torch.empty(
B.shape[1], dtype=torch.float32 if fp32_output else dtype, device="cuda"
)
grad_bias = torch.empty(B.shape[1], dtype=out.dtype, device="cuda")
else:
grad_bias = empty_tensor

bias = bias if use_bias else empty_tensor

assert A.dtype == dtype and B.dtype == dtype, \
f'Expected dtype={dtype}, but found A.dtype={A.dtype} and B.dtype={B.dtype}'
input_dtype = TE_DType[dtype]
output_dtype = TE_DType[out.dtype]
if use_bias:
bias_dtype = TE_DType[grad_bias.dtype] if grad else TE_DType[bias.dtype]
else:
bias_dtype = output_dtype

_ = torch.ops.tex_ts.te_gemm_ts(
A,
empty_tensor,
Expand Down
12 changes: 0 additions & 12 deletions transformer_engine/pytorch/module.py
View file
Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -960,7 +960,6 @@ def backward(
ctx.activation_dtype,
get_workspace(),
accumulate=accumulate_wgrad_into_param_main_grad,
fp32_output=ctx.fuse_wgrad_accumulation,
out=weight.main_grad if ctx.fuse_wgrad_accumulation else None,
use_split_accumulator=_2X_ACC_WGRAD,
)
Expand All@@ -980,7 +979,6 @@ def backward(
layout="NT",
grad=True,
accumulate=accumulate_wgrad_into_param_main_grad,
fp32_output=ctx.fuse_wgrad_accumulation,
out=weight.main_grad if ctx.fuse_wgrad_accumulation else None,
)
else:
Expand All@@ -994,7 +992,6 @@ def backward(
grad=True,
use_bias=ctx.use_bias,
accumulate=accumulate_wgrad_into_param_main_grad,
fp32_output=ctx.fuse_wgrad_accumulation,
out=weight.main_grad if ctx.fuse_wgrad_accumulation else None,
)

Expand DownExpand Up@@ -1652,7 +1649,6 @@ def backward(
ctx.activation_dtype,
get_workspace(),
accumulate=accumulate_wgrad_into_param_main_grad,
fp32_output=ctx.fuse_wgrad_accumulation,
out=weight.main_grad if ctx.fuse_wgrad_accumulation else None,
use_split_accumulator=_2X_ACC_WGRAD,
)
Expand All@@ -1665,7 +1661,6 @@ def backward(
layout="NT",
grad=True,
accumulate=accumulate_wgrad_into_param_main_grad,
fp32_output=ctx.fuse_wgrad_accumulation,
out=weight.main_grad if ctx.fuse_wgrad_accumulation else None,
)
else:
Expand All@@ -1679,7 +1674,6 @@ def backward(
grad=True,
use_bias=ctx.use_bias,
accumulate=accumulate_wgrad_into_param_main_grad,
fp32_output=ctx.fuse_wgrad_accumulation,
out=weight.main_grad if ctx.fuse_wgrad_accumulation else None,
)

Expand DownExpand Up@@ -2358,7 +2352,6 @@ def backward(
ctx.activation_dtype,
get_workspace(),
accumulate=accumulate_wgrad_into_param_main_grad,
fp32_output=ctx.fuse_wgrad_accumulation,
out=fc2_weight.main_grad
if ctx.fuse_wgrad_accumulation
else None,
Expand DownExpand Up@@ -2390,7 +2383,6 @@ def backward(
grad=True,
use_bias=ctx.use_bias,
accumulate=accumulate_wgrad_into_param_main_grad,
fp32_output=ctx.fuse_wgrad_accumulation,
out=fc2_weight.main_grad
if ctx.fuse_wgrad_accumulation
else None,
Expand DownExpand Up@@ -2446,7 +2438,6 @@ def backward(
grad=True,
use_bias=ctx.use_bias,
accumulate=accumulate_wgrad_into_param_main_grad,
fp32_output=ctx.fuse_wgrad_accumulation,
out=fc2_weight.main_grad if ctx.fuse_wgrad_accumulation else None,
)

Expand DownExpand Up@@ -2491,7 +2482,6 @@ def backward(
ctx.activation_dtype,
get_workspace(),
accumulate=accumulate_wgrad_into_param_main_grad,
fp32_output=ctx.fuse_wgrad_accumulation,
out=fc1_weight.main_grad
if ctx.fuse_wgrad_accumulation
else None,
Expand All@@ -2513,7 +2503,6 @@ def backward(
layout="NT",
grad=True,
accumulate=accumulate_wgrad_into_param_main_grad,
fp32_output=ctx.fuse_wgrad_accumulation,
out=fc1_weight.main_grad
if ctx.fuse_wgrad_accumulation
else None,
Expand All@@ -2529,7 +2518,6 @@ def backward(
grad=True,
use_bias=not ctx.bias_gelu_nvfusion,
accumulate=accumulate_wgrad_into_param_main_grad,
fp32_output=ctx.fuse_wgrad_accumulation,
out=fc1_weight.main_grad if ctx.fuse_wgrad_accumulation else None,
)

Expand Down
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}
} catch(__e) { console.warn('[Userscript:Add Copy Buttons to Code Blocks]', __e); }
})();
(function(){
try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '

Skip to content
Support arbitrary output dtypes in PyT GEMM functions#75

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Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

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 Merged

ksivaman
merged 4 commits into

from

Feb 23, 2023
 Merged


Support arbitrary output dtypes in PyT GEMM functions
#75
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c1bc953

Deprecate fp32_output option for PyT linear layers


timmoon10 Feb 15, 2023
3ab330a

Remove deprecated options


timmoon10 Feb 16, 2023
b6baff1

Merge branch 'main' into bf16-wgrad-bugfix


timmoon10 Feb 22, 2023
bfc386b

Merge branch 'main' into bf16-wgrad-bugfix


ksivaman Feb 23, 2023

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1 change: 0 additions & 1 deletion
tests/test_onnx_export.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -371,7 +371,6 @@ def forward(self, inp, weight):
 get_workspace(),
 bias=self.bias,
 use_bias=self.use_bias,
 fp32_output=(self.precision==torch.float32),
 use_split_accumulator=False)
 return ret

Expand Down

28 changes: 14 additions & 14 deletions
transformer_engine/pytorch/cpp_extensions.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -26,9 +26,8 @@ def fp8_gemm(
 fp8_meta_tensor: tex.FP8TensorMeta = None,
 bias: Optional[torch.Tensor] = None,
 use_bias: bool = False,
 fp32_output: bool = False,
 use_split_accumulator: bool = False,
 D_dtype: tex.DType = None,
 D_dtype: Optional[tex.DType] = None,
) -> torch.Tensor:
 """TN layout GEMM with fp8 inputs."""

Expand All@@ -41,15 +40,14 @@ def fp8_gemm(
 out = torch.empty(
 B.shape[0],
 A.shape[0],
 dtype=torch.float32 if fp32_output else out_dtype,
 dtype=out_dtype,
 device="cuda",
 )
 return_output = True

 out_dtype = tex.DType.kFloat32 if fp32_output else TE_DType[out_dtype]
 out_dtype = TE_DType[out.dtype] if D_dtype is None else D_dtype
 # Use bfloat16 as default bias_dtype
 bias_dtype = tex.DType.kBFloat16 if bias is None else TE_DType[bias.dtype]
 out_dtype = D_dtype if D_dtype is not None else out_dtype

 _ = torch.ops.tex_ts.te_gemm_ts(
 A,
Expand DownExpand Up@@ -94,7 +92,6 @@ def gemm(
 out: Optional[torch.Tensor] = None,
 bias: Optional[torch.Tensor] = None,
 use_bias: bool = False,
 fp32_output: bool = False,
) -> Tuple[Union[torch.Tensor, None], ...]:
 """Non FP8 GEMM."""

Expand All@@ -104,16 +101,12 @@ def gemm(
 empty_tensor = torch.Tensor()
 fp8_index = -1 # dummy index

 input_dtype = TE_DType[dtype]
 output_dtype = tex.DType.kFloat32 if fp32_output else input_dtype
 bias_dtype = output_dtype if bias is None else TE_DType[bias.dtype]

 return_output = False
 if out is None:
 out = torch.empty(
 B.shape[1] if transb else B.shape[0],
 A.shape[0] if transa else A.shape[1],
 dtype=torch.float32 if fp32_output else dtype,
 dtype=dtype,
 device="cuda",
 )
 return_output = True
Expand All@@ -124,14 +117,21 @@ def gemm(
 gelu_input = empty_tensor

 if grad and use_bias:
 grad_bias = torch.empty(
 B.shape[1], dtype=torch.float32 if fp32_output else dtype, device="cuda"
 )
 grad_bias = torch.empty(B.shape[1], dtype=out.dtype, device="cuda")
 else:
 grad_bias = empty_tensor

 bias = bias if use_bias else empty_tensor

 assert A.dtype == dtype and B.dtype == dtype, \
 f'Expected dtype={dtype}, but found A.dtype={A.dtype} and B.dtype={B.dtype}'
 input_dtype = TE_DType[dtype]
 output_dtype = TE_DType[out.dtype]
 if use_bias:
 bias_dtype = TE_DType[grad_bias.dtype] if grad else TE_DType[bias.dtype]
 else:
 bias_dtype = output_dtype

 _ = torch.ops.tex_ts.te_gemm_ts(
 A,
 empty_tensor,
Expand Down

12 changes: 0 additions & 12 deletions
transformer_engine/pytorch/module.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -960,7 +960,6 @@ def backward(
 ctx.activation_dtype,
 get_workspace(),
 accumulate=accumulate_wgrad_into_param_main_grad,
 fp32_output=ctx.fuse_wgrad_accumulation,
 out=weight.main_grad if ctx.fuse_wgrad_accumulation else None,
 use_split_accumulator=_2X_ACC_WGRAD,
 )
Expand All@@ -980,7 +979,6 @@ def backward(
 layout="NT",
 grad=True,
 accumulate=accumulate_wgrad_into_param_main_grad,
 fp32_output=ctx.fuse_wgrad_accumulation,
 out=weight.main_grad if ctx.fuse_wgrad_accumulation else None,
 )
 else:
Expand All@@ -994,7 +992,6 @@ def backward(
 grad=True,
 use_bias=ctx.use_bias,
 accumulate=accumulate_wgrad_into_param_main_grad,
 fp32_output=ctx.fuse_wgrad_accumulation,
 out=weight.main_grad if ctx.fuse_wgrad_accumulation else None,
 )

Expand DownExpand Up@@ -1652,7 +1649,6 @@ def backward(
 ctx.activation_dtype,
 get_workspace(),
 accumulate=accumulate_wgrad_into_param_main_grad,
 fp32_output=ctx.fuse_wgrad_accumulation,
 out=weight.main_grad if ctx.fuse_wgrad_accumulation else None,
 use_split_accumulator=_2X_ACC_WGRAD,
 )
Expand All@@ -1665,7 +1661,6 @@ def backward(
 layout="NT",
 grad=True,
 accumulate=accumulate_wgrad_into_param_main_grad,
 fp32_output=ctx.fuse_wgrad_accumulation,
 out=weight.main_grad if ctx.fuse_wgrad_accumulation else None,
 )
 else:
Expand All@@ -1679,7 +1674,6 @@ def backward(
 grad=True,
 use_bias=ctx.use_bias,
 accumulate=accumulate_wgrad_into_param_main_grad,
 fp32_output=ctx.fuse_wgrad_accumulation,
 out=weight.main_grad if ctx.fuse_wgrad_accumulation else None,
 )

Expand DownExpand Up@@ -2358,7 +2352,6 @@ def backward(
 ctx.activation_dtype,
 get_workspace(),
 accumulate=accumulate_wgrad_into_param_main_grad,
 fp32_output=ctx.fuse_wgrad_accumulation,
 out=fc2_weight.main_grad
 if ctx.fuse_wgrad_accumulation
 else None,
Expand DownExpand Up@@ -2390,7 +2383,6 @@ def backward(
 grad=True,
 use_bias=ctx.use_bias,
 accumulate=accumulate_wgrad_into_param_main_grad,
 fp32_output=ctx.fuse_wgrad_accumulation,
 out=fc2_weight.main_grad
 if ctx.fuse_wgrad_accumulation
 else None,
Expand DownExpand Up@@ -2446,7 +2438,6 @@ def backward(
 grad=True,
 use_bias=ctx.use_bias,
 accumulate=accumulate_wgrad_into_param_main_grad,
 fp32_output=ctx.fuse_wgrad_accumulation,
 out=fc2_weight.main_grad if ctx.fuse_wgrad_accumulation else None,
 )

Expand DownExpand Up@@ -2491,7 +2482,6 @@ def backward(
 ctx.activation_dtype,
 get_workspace(),
 accumulate=accumulate_wgrad_into_param_main_grad,
 fp32_output=ctx.fuse_wgrad_accumulation,
 out=fc1_weight.main_grad
 if ctx.fuse_wgrad_accumulation
 else None,
Expand All@@ -2513,7 +2503,6 @@ def backward(
 layout="NT",
 grad=True,
 accumulate=accumulate_wgrad_into_param_main_grad,
 fp32_output=ctx.fuse_wgrad_accumulation,
 out=fc1_weight.main_grad
 if ctx.fuse_wgrad_accumulation
 else None,
Expand All@@ -2529,7 +2518,6 @@ def backward(
 grad=True,
 use_bias=not ctx.bias_gelu_nvfusion,
 accumulate=accumulate_wgrad_into_param_main_grad,
 fp32_output=ctx.fuse_wgrad_accumulation,
 out=fc1_weight.main_grad if ctx.fuse_wgrad_accumulation else None,
 )

Expand Down

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Force GitHub README to respect dark mode\n(function() {\n var style = document.createElement('style');\n style.textContent = '\n .markdown-body {\n color-scheme: dark light;\n }\n .markdown-body pre { background: #161b22 !important; }\n .markdown-body code { background: rgba(110, 118, 129, 0.4) !important; }\n .markdown-body table th, .markdown-body table td { border-color: #30363d !important; }\n .markdown-body img { background: #0d1117; }\n .markdown-body blockquote { border-left-color: #8b949e; }\n .markdown-body hr { border-color: #30363d; }\n ';\n document.head.appendChild(style);\n})();", "GitHub Dark Mode README Fix");
}
} catch(__e) { console.warn('[Userscript:GitHub Dark Mode README Fix]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
Support arbitrary output dtypes in PyT GEMM functions#75

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Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

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privacy statement. We’ll occasionally send you account related emails.

Already on GitHub?
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 Merged

ksivaman
merged 4 commits into

from

Feb 23, 2023
 Merged


Support arbitrary output dtypes in PyT GEMM functions
#75
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c1bc953

Deprecate fp32_output option for PyT linear layers


timmoon10 Feb 15, 2023
3ab330a

Remove deprecated options


timmoon10 Feb 16, 2023
b6baff1

Merge branch 'main' into bf16-wgrad-bugfix


timmoon10 Feb 22, 2023
bfc386b

Merge branch 'main' into bf16-wgrad-bugfix


ksivaman Feb 23, 2023

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1 change: 0 additions & 1 deletion
tests/test_onnx_export.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -371,7 +371,6 @@ def forward(self, inp, weight):
 get_workspace(),
 bias=self.bias,
 use_bias=self.use_bias,
 fp32_output=(self.precision==torch.float32),
 use_split_accumulator=False)
 return ret

Expand Down

28 changes: 14 additions & 14 deletions
transformer_engine/pytorch/cpp_extensions.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -26,9 +26,8 @@ def fp8_gemm(
 fp8_meta_tensor: tex.FP8TensorMeta = None,
 bias: Optional[torch.Tensor] = None,
 use_bias: bool = False,
 fp32_output: bool = False,
 use_split_accumulator: bool = False,
 D_dtype: tex.DType = None,
 D_dtype: Optional[tex.DType] = None,
) -> torch.Tensor:
 """TN layout GEMM with fp8 inputs."""

Expand All@@ -41,15 +40,14 @@ def fp8_gemm(
 out = torch.empty(
 B.shape[0],
 A.shape[0],
 dtype=torch.float32 if fp32_output else out_dtype,
 dtype=out_dtype,
 device="cuda",
 )
 return_output = True

 out_dtype = tex.DType.kFloat32 if fp32_output else TE_DType[out_dtype]
 out_dtype = TE_DType[out.dtype] if D_dtype is None else D_dtype
 # Use bfloat16 as default bias_dtype
 bias_dtype = tex.DType.kBFloat16 if bias is None else TE_DType[bias.dtype]
 out_dtype = D_dtype if D_dtype is not None else out_dtype

 _ = torch.ops.tex_ts.te_gemm_ts(
 A,
Expand DownExpand Up@@ -94,7 +92,6 @@ def gemm(
 out: Optional[torch.Tensor] = None,
 bias: Optional[torch.Tensor] = None,
 use_bias: bool = False,
 fp32_output: bool = False,
) -> Tuple[Union[torch.Tensor, None], ...]:
 """Non FP8 GEMM."""

Expand All@@ -104,16 +101,12 @@ def gemm(
 empty_tensor = torch.Tensor()
 fp8_index = -1 # dummy index

 input_dtype = TE_DType[dtype]
 output_dtype = tex.DType.kFloat32 if fp32_output else input_dtype
 bias_dtype = output_dtype if bias is None else TE_DType[bias.dtype]

 return_output = False
 if out is None:
 out = torch.empty(
 B.shape[1] if transb else B.shape[0],
 A.shape[0] if transa else A.shape[1],
 dtype=torch.float32 if fp32_output else dtype,
 dtype=dtype,
 device="cuda",
 )
 return_output = True
Expand All@@ -124,14 +117,21 @@ def gemm(
 gelu_input = empty_tensor

 if grad and use_bias:
 grad_bias = torch.empty(
 B.shape[1], dtype=torch.float32 if fp32_output else dtype, device="cuda"
 )
 grad_bias = torch.empty(B.shape[1], dtype=out.dtype, device="cuda")
 else:
 grad_bias = empty_tensor

 bias = bias if use_bias else empty_tensor

 assert A.dtype == dtype and B.dtype == dtype, \
 f'Expected dtype={dtype}, but found A.dtype={A.dtype} and B.dtype={B.dtype}'
 input_dtype = TE_DType[dtype]
 output_dtype = TE_DType[out.dtype]
 if use_bias:
 bias_dtype = TE_DType[grad_bias.dtype] if grad else TE_DType[bias.dtype]
 else:
 bias_dtype = output_dtype

 _ = torch.ops.tex_ts.te_gemm_ts(
 A,
 empty_tensor,
Expand Down

12 changes: 0 additions & 12 deletions
transformer_engine/pytorch/module.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -960,7 +960,6 @@ def backward(
 ctx.activation_dtype,
 get_workspace(),
 accumulate=accumulate_wgrad_into_param_main_grad,
 fp32_output=ctx.fuse_wgrad_accumulation,
 out=weight.main_grad if ctx.fuse_wgrad_accumulation else None,
 use_split_accumulator=_2X_ACC_WGRAD,
 )
Expand All@@ -980,7 +979,6 @@ def backward(
 layout="NT",
 grad=True,
 accumulate=accumulate_wgrad_into_param_main_grad,
 fp32_output=ctx.fuse_wgrad_accumulation,
 out=weight.main_grad if ctx.fuse_wgrad_accumulation else None,
 )
 else:
Expand All@@ -994,7 +992,6 @@ def backward(
 grad=True,
 use_bias=ctx.use_bias,
 accumulate=accumulate_wgrad_into_param_main_grad,
 fp32_output=ctx.fuse_wgrad_accumulation,
 out=weight.main_grad if ctx.fuse_wgrad_accumulation else None,
 )

Expand DownExpand Up@@ -1652,7 +1649,6 @@ def backward(
 ctx.activation_dtype,
 get_workspace(),
 accumulate=accumulate_wgrad_into_param_main_grad,
 fp32_output=ctx.fuse_wgrad_accumulation,
 out=weight.main_grad if ctx.fuse_wgrad_accumulation else None,
 use_split_accumulator=_2X_ACC_WGRAD,
 )
Expand All@@ -1665,7 +1661,6 @@ def backward(
 layout="NT",
 grad=True,
 accumulate=accumulate_wgrad_into_param_main_grad,
 fp32_output=ctx.fuse_wgrad_accumulation,
 out=weight.main_grad if ctx.fuse_wgrad_accumulation else None,
 )
 else:
Expand All@@ -1679,7 +1674,6 @@ def backward(
 grad=True,
 use_bias=ctx.use_bias,
 accumulate=accumulate_wgrad_into_param_main_grad,
 fp32_output=ctx.fuse_wgrad_accumulation,
 out=weight.main_grad if ctx.fuse_wgrad_accumulation else None,
 )

Expand DownExpand Up@@ -2358,7 +2352,6 @@ def backward(
 ctx.activation_dtype,
 get_workspace(),
 accumulate=accumulate_wgrad_into_param_main_grad,
 fp32_output=ctx.fuse_wgrad_accumulation,
 out=fc2_weight.main_grad
 if ctx.fuse_wgrad_accumulation
 else None,
Expand DownExpand Up@@ -2390,7 +2383,6 @@ def backward(
 grad=True,
 use_bias=ctx.use_bias,
 accumulate=accumulate_wgrad_into_param_main_grad,
 fp32_output=ctx.fuse_wgrad_accumulation,
 out=fc2_weight.main_grad
 if ctx.fuse_wgrad_accumulation
 else None,
Expand DownExpand Up@@ -2446,7 +2438,6 @@ def backward(
 grad=True,
 use_bias=ctx.use_bias,
 accumulate=accumulate_wgrad_into_param_main_grad,
 fp32_output=ctx.fuse_wgrad_accumulation,
 out=fc2_weight.main_grad if ctx.fuse_wgrad_accumulation else None,
 )

Expand DownExpand Up@@ -2491,7 +2482,6 @@ def backward(
 ctx.activation_dtype,
 get_workspace(),
 accumulate=accumulate_wgrad_into_param_main_grad,
 fp32_output=ctx.fuse_wgrad_accumulation,
 out=fc1_weight.main_grad
 if ctx.fuse_wgrad_accumulation
 else None,
Expand All@@ -2513,7 +2503,6 @@ def backward(
 layout="NT",
 grad=True,
 accumulate=accumulate_wgrad_into_param_main_grad,
 fp32_output=ctx.fuse_wgrad_accumulation,
 out=fc1_weight.main_grad
 if ctx.fuse_wgrad_accumulation
 else None,
Expand All@@ -2529,7 +2518,6 @@ def backward(
 grad=True,
 use_bias=not ctx.bias_gelu_nvfusion,
 accumulate=accumulate_wgrad_into_param_main_grad,
 fp32_output=ctx.fuse_wgrad_accumulation,
 out=fc1_weight.main_grad if ctx.fuse_wgrad_accumulation else None,
 )

Expand Down

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Highlight search terms from Google/DuckDuckGo/Bing referrer\n(function() {\n var ref = document.referrer;\n var terms = [];\n \n if (ref.includes('google.com') || ref.includes('duckduckgo.com') || ref.includes('bing.com')) {\n var url = new URL(ref);\n var q = url.searchParams.get('q') || url.searchParams.get('p');\n if (q) {\n terms = q.split(/\\s+/).filter(function(t) { return t.length > 2; });\n }\n }\n \n if (terms.length === 0) return;\n \n var style = document.createElement('style');\n style.textContent = '.userscript-highlight { background: #fbbf24; color: #1a1a2e; padding: 1px 3px; border-radius: 2px; }';\n document.head.appendChild(style);\n \n function highlight(node) {\n if (node.nodeType === 3) { // text node\n var text = node.textContent;\n var found = false;\n terms.forEach(function(term) {\n var regex = new RegExp('(' + term.replace(/[.*+?^${}()|[\\]\\\\]/g, '\\\\') + ')', 'gi');\n if (regex.test(text)) {\n found = true;\n var frag = document.createDocumentFragment();\n var parts = text.split(regex);\n parts.forEach(function(part, i) {\n if (i % 2 === 0) {\n frag.appendChild(document.createTextNode(part));\n } else {\n var span = document.createElement('span');\n span.className = 'userscript-highlight';\n span.textContent = part;\n frag.appendChild(span);\n }\n });\n node.parentNode.replaceChild(frag, node);\n }\n });\n } else if (node.nodeType === 1 && node.childNodes) { // element\n var skipTags = ['SCRIPT', 'STYLE', 'NOSCRIPT', 'TEXTAREA', 'INPUT', 'SELECT'];\n if (!skipTags.includes(node.tagName)) {\n Array.from(node.childNodes).forEach(highlight);\n }\n }\n }\n \n highlight(document.body);\n \n // Re-highlight on dynamic content\n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1 || node.nodeType === 3) highlight(node);\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Highlight Search Terms");
}
} catch(__e) { console.warn('[Userscript:Highlight Search Terms]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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Support arbitrary output dtypes in PyT GEMM functions#75

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 Merged

ksivaman
merged 4 commits into

from

Feb 23, 2023
 Merged


Support arbitrary output dtypes in PyT GEMM functions
#75
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c1bc953

Deprecate fp32_output option for PyT linear layers


timmoon10 Feb 15, 2023
3ab330a

Remove deprecated options


timmoon10 Feb 16, 2023
b6baff1

Merge branch 'main' into bf16-wgrad-bugfix


timmoon10 Feb 22, 2023
bfc386b

Merge branch 'main' into bf16-wgrad-bugfix


ksivaman Feb 23, 2023

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1 change: 0 additions & 1 deletion
tests/test_onnx_export.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -371,7 +371,6 @@ def forward(self, inp, weight):
 get_workspace(),
 bias=self.bias,
 use_bias=self.use_bias,
 fp32_output=(self.precision==torch.float32),
 use_split_accumulator=False)
 return ret

Expand Down

28 changes: 14 additions & 14 deletions
transformer_engine/pytorch/cpp_extensions.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -26,9 +26,8 @@ def fp8_gemm(
 fp8_meta_tensor: tex.FP8TensorMeta = None,
 bias: Optional[torch.Tensor] = None,
 use_bias: bool = False,
 fp32_output: bool = False,
 use_split_accumulator: bool = False,
 D_dtype: tex.DType = None,
 D_dtype: Optional[tex.DType] = None,
) -> torch.Tensor:
 """TN layout GEMM with fp8 inputs."""

Expand All@@ -41,15 +40,14 @@ def fp8_gemm(
 out = torch.empty(
 B.shape[0],
 A.shape[0],
 dtype=torch.float32 if fp32_output else out_dtype,
 dtype=out_dtype,
 device="cuda",
 )
 return_output = True

 out_dtype = tex.DType.kFloat32 if fp32_output else TE_DType[out_dtype]
 out_dtype = TE_DType[out.dtype] if D_dtype is None else D_dtype
 # Use bfloat16 as default bias_dtype
 bias_dtype = tex.DType.kBFloat16 if bias is None else TE_DType[bias.dtype]
 out_dtype = D_dtype if D_dtype is not None else out_dtype

 _ = torch.ops.tex_ts.te_gemm_ts(
 A,
Expand DownExpand Up@@ -94,7 +92,6 @@ def gemm(
 out: Optional[torch.Tensor] = None,
 bias: Optional[torch.Tensor] = None,
 use_bias: bool = False,
 fp32_output: bool = False,
) -> Tuple[Union[torch.Tensor, None], ...]:
 """Non FP8 GEMM."""

Expand All@@ -104,16 +101,12 @@ def gemm(
 empty_tensor = torch.Tensor()
 fp8_index = -1 # dummy index

 input_dtype = TE_DType[dtype]
 output_dtype = tex.DType.kFloat32 if fp32_output else input_dtype
 bias_dtype = output_dtype if bias is None else TE_DType[bias.dtype]

 return_output = False
 if out is None:
 out = torch.empty(
 B.shape[1] if transb else B.shape[0],
 A.shape[0] if transa else A.shape[1],
 dtype=torch.float32 if fp32_output else dtype,
 dtype=dtype,
 device="cuda",
 )
 return_output = True
Expand All@@ -124,14 +117,21 @@ def gemm(
 gelu_input = empty_tensor

 if grad and use_bias:
 grad_bias = torch.empty(
 B.shape[1], dtype=torch.float32 if fp32_output else dtype, device="cuda"
 )
 grad_bias = torch.empty(B.shape[1], dtype=out.dtype, device="cuda")
 else:
 grad_bias = empty_tensor

 bias = bias if use_bias else empty_tensor

 assert A.dtype == dtype and B.dtype == dtype, \
 f'Expected dtype={dtype}, but found A.dtype={A.dtype} and B.dtype={B.dtype}'
 input_dtype = TE_DType[dtype]
 output_dtype = TE_DType[out.dtype]
 if use_bias:
 bias_dtype = TE_DType[grad_bias.dtype] if grad else TE_DType[bias.dtype]
 else:
 bias_dtype = output_dtype

 _ = torch.ops.tex_ts.te_gemm_ts(
 A,
 empty_tensor,
Expand Down

12 changes: 0 additions & 12 deletions
transformer_engine/pytorch/module.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -960,7 +960,6 @@ def backward(
 ctx.activation_dtype,
 get_workspace(),
 accumulate=accumulate_wgrad_into_param_main_grad,
 fp32_output=ctx.fuse_wgrad_accumulation,
 out=weight.main_grad if ctx.fuse_wgrad_accumulation else None,
 use_split_accumulator=_2X_ACC_WGRAD,
 )
Expand All@@ -980,7 +979,6 @@ def backward(
 layout="NT",
 grad=True,
 accumulate=accumulate_wgrad_into_param_main_grad,
 fp32_output=ctx.fuse_wgrad_accumulation,
 out=weight.main_grad if ctx.fuse_wgrad_accumulation else None,
 )
 else:
Expand All@@ -994,7 +992,6 @@ def backward(
 grad=True,
 use_bias=ctx.use_bias,
 accumulate=accumulate_wgrad_into_param_main_grad,
 fp32_output=ctx.fuse_wgrad_accumulation,
 out=weight.main_grad if ctx.fuse_wgrad_accumulation else None,
 )

Expand DownExpand Up@@ -1652,7 +1649,6 @@ def backward(
 ctx.activation_dtype,
 get_workspace(),
 accumulate=accumulate_wgrad_into_param_main_grad,
 fp32_output=ctx.fuse_wgrad_accumulation,
 out=weight.main_grad if ctx.fuse_wgrad_accumulation else None,
 use_split_accumulator=_2X_ACC_WGRAD,
 )
Expand All@@ -1665,7 +1661,6 @@ def backward(
 layout="NT",
 grad=True,
 accumulate=accumulate_wgrad_into_param_main_grad,
 fp32_output=ctx.fuse_wgrad_accumulation,
 out=weight.main_grad if ctx.fuse_wgrad_accumulation else None,
 )
 else:
Expand All@@ -1679,7 +1674,6 @@ def backward(
 grad=True,
 use_bias=ctx.use_bias,
 accumulate=accumulate_wgrad_into_param_main_grad,
 fp32_output=ctx.fuse_wgrad_accumulation,
 out=weight.main_grad if ctx.fuse_wgrad_accumulation else None,
 )

Expand DownExpand Up@@ -2358,7 +2352,6 @@ def backward(
 ctx.activation_dtype,
 get_workspace(),
 accumulate=accumulate_wgrad_into_param_main_grad,
 fp32_output=ctx.fuse_wgrad_accumulation,
 out=fc2_weight.main_grad
 if ctx.fuse_wgrad_accumulation
 else None,
Expand DownExpand Up@@ -2390,7 +2383,6 @@ def backward(
 grad=True,
 use_bias=ctx.use_bias,
 accumulate=accumulate_wgrad_into_param_main_grad,
 fp32_output=ctx.fuse_wgrad_accumulation,
 out=fc2_weight.main_grad
 if ctx.fuse_wgrad_accumulation
 else None,
Expand DownExpand Up@@ -2446,7 +2438,6 @@ def backward(
 grad=True,
 use_bias=ctx.use_bias,
 accumulate=accumulate_wgrad_into_param_main_grad,
 fp32_output=ctx.fuse_wgrad_accumulation,
 out=fc2_weight.main_grad if ctx.fuse_wgrad_accumulation else None,
 )

Expand DownExpand Up@@ -2491,7 +2482,6 @@ def backward(
 ctx.activation_dtype,
 get_workspace(),
 accumulate=accumulate_wgrad_into_param_main_grad,
 fp32_output=ctx.fuse_wgrad_accumulation,
 out=fc1_weight.main_grad
 if ctx.fuse_wgrad_accumulation
 else None,
Expand All@@ -2513,7 +2503,6 @@ def backward(
 layout="NT",
 grad=True,
 accumulate=accumulate_wgrad_into_param_main_grad,
 fp32_output=ctx.fuse_wgrad_accumulation,
 out=fc1_weight.main_grad
 if ctx.fuse_wgrad_accumulation
 else None,
Expand All@@ -2529,7 +2518,6 @@ def backward(
 grad=True,
 use_bias=not ctx.bias_gelu_nvfusion,
 accumulate=accumulate_wgrad_into_param_main_grad,
 fp32_output=ctx.fuse_wgrad_accumulation,
 out=fc1_weight.main_grad if ctx.fuse_wgrad_accumulation else None,
 )

Expand Down

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Strip utm_, fbclid, gclid, etc. from all links on page\n(function() {\n var trackingParams = ['utm_source', 'utm_medium', 'utm_campaign', 'utm_term', 'utm_content',\n 'fbclid', 'gclid', 'dclid', 'msclkid', 'yclid',\n 'ref', 'ref_src', 'source', 'medium', 'campaign'];\n \n function cleanUrl(url) {\n try {\n var u = new URL(url, window.location.origin);\n var changed = false;\n trackingParams.forEach(function(p) {\n if (u.searchParams.has(p)) {\n u.searchParams.delete(p);\n changed = true;\n }\n });\n return changed ? u.toString() : url;\n } catch (e) {\n return url;\n }\n }\n \n function cleanLinks() {\n document.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n \n cleanLinks();\n \n var observer = new MutationObserver(function(mutations) {\n mutations.forEach(function(m) {\n m.addedNodes.forEach(function(node) {\n if (node.nodeType === 1) {\n if (node.tagName === 'A') cleanLinks();\n node.querySelectorAll('a[href]').forEach(function(a) {\n var clean = cleanUrl(a.href);\n if (clean !== a.href) a.href = clean;\n });\n }\n });\n });\n });\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Remove Tracking Parameters from Links");
}
} catch(__e) { console.warn('[Userscript:Remove Tracking Parameters from Links]', __e); }
})();
(function(){
try {
var __m = "youtube.com";
var __re = new RegExp('^' + "youtube\\.com" + '

Skip to content
Support arbitrary output dtypes in PyT GEMM functions#75

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Have a question about this project? Sign up for a free GitHub account to open an issue and contact its maintainers and the community.

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privacy statement. We’ll occasionally send you account related emails.

Already on GitHub?
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to your account

 Merged

ksivaman
merged 4 commits into

from

Feb 23, 2023
 Merged


Support arbitrary output dtypes in PyT GEMM functions
#75
Show file tree
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c1bc953

Deprecate fp32_output option for PyT linear layers


timmoon10 Feb 15, 2023
3ab330a

Remove deprecated options


timmoon10 Feb 16, 2023
b6baff1

Merge branch 'main' into bf16-wgrad-bugfix


timmoon10 Feb 22, 2023
bfc386b

Merge branch 'main' into bf16-wgrad-bugfix


ksivaman Feb 23, 2023

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1 change: 0 additions & 1 deletion
tests/test_onnx_export.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -371,7 +371,6 @@ def forward(self, inp, weight):
 get_workspace(),
 bias=self.bias,
 use_bias=self.use_bias,
 fp32_output=(self.precision==torch.float32),
 use_split_accumulator=False)
 return ret

Expand Down

28 changes: 14 additions & 14 deletions
transformer_engine/pytorch/cpp_extensions.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -26,9 +26,8 @@ def fp8_gemm(
 fp8_meta_tensor: tex.FP8TensorMeta = None,
 bias: Optional[torch.Tensor] = None,
 use_bias: bool = False,
 fp32_output: bool = False,
 use_split_accumulator: bool = False,
 D_dtype: tex.DType = None,
 D_dtype: Optional[tex.DType] = None,
) -> torch.Tensor:
 """TN layout GEMM with fp8 inputs."""

Expand All@@ -41,15 +40,14 @@ def fp8_gemm(
 out = torch.empty(
 B.shape[0],
 A.shape[0],
 dtype=torch.float32 if fp32_output else out_dtype,
 dtype=out_dtype,
 device="cuda",
 )
 return_output = True

 out_dtype = tex.DType.kFloat32 if fp32_output else TE_DType[out_dtype]
 out_dtype = TE_DType[out.dtype] if D_dtype is None else D_dtype
 # Use bfloat16 as default bias_dtype
 bias_dtype = tex.DType.kBFloat16 if bias is None else TE_DType[bias.dtype]
 out_dtype = D_dtype if D_dtype is not None else out_dtype

 _ = torch.ops.tex_ts.te_gemm_ts(
 A,
Expand DownExpand Up@@ -94,7 +92,6 @@ def gemm(
 out: Optional[torch.Tensor] = None,
 bias: Optional[torch.Tensor] = None,
 use_bias: bool = False,
 fp32_output: bool = False,
) -> Tuple[Union[torch.Tensor, None], ...]:
 """Non FP8 GEMM."""

Expand All@@ -104,16 +101,12 @@ def gemm(
 empty_tensor = torch.Tensor()
 fp8_index = -1 # dummy index

 input_dtype = TE_DType[dtype]
 output_dtype = tex.DType.kFloat32 if fp32_output else input_dtype
 bias_dtype = output_dtype if bias is None else TE_DType[bias.dtype]

 return_output = False
 if out is None:
 out = torch.empty(
 B.shape[1] if transb else B.shape[0],
 A.shape[0] if transa else A.shape[1],
 dtype=torch.float32 if fp32_output else dtype,
 dtype=dtype,
 device="cuda",
 )
 return_output = True
Expand All@@ -124,14 +117,21 @@ def gemm(
 gelu_input = empty_tensor

 if grad and use_bias:
 grad_bias = torch.empty(
 B.shape[1], dtype=torch.float32 if fp32_output else dtype, device="cuda"
 )
 grad_bias = torch.empty(B.shape[1], dtype=out.dtype, device="cuda")
 else:
 grad_bias = empty_tensor

 bias = bias if use_bias else empty_tensor

 assert A.dtype == dtype and B.dtype == dtype, \
 f'Expected dtype={dtype}, but found A.dtype={A.dtype} and B.dtype={B.dtype}'
 input_dtype = TE_DType[dtype]
 output_dtype = TE_DType[out.dtype]
 if use_bias:
 bias_dtype = TE_DType[grad_bias.dtype] if grad else TE_DType[bias.dtype]
 else:
 bias_dtype = output_dtype

 _ = torch.ops.tex_ts.te_gemm_ts(
 A,
 empty_tensor,
Expand Down

12 changes: 0 additions & 12 deletions
transformer_engine/pytorch/module.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -960,7 +960,6 @@ def backward(
 ctx.activation_dtype,
 get_workspace(),
 accumulate=accumulate_wgrad_into_param_main_grad,
 fp32_output=ctx.fuse_wgrad_accumulation,
 out=weight.main_grad if ctx.fuse_wgrad_accumulation else None,
 use_split_accumulator=_2X_ACC_WGRAD,
 )
Expand All@@ -980,7 +979,6 @@ def backward(
 layout="NT",
 grad=True,
 accumulate=accumulate_wgrad_into_param_main_grad,
 fp32_output=ctx.fuse_wgrad_accumulation,
 out=weight.main_grad if ctx.fuse_wgrad_accumulation else None,
 )
 else:
Expand All@@ -994,7 +992,6 @@ def backward(
 grad=True,
 use_bias=ctx.use_bias,
 accumulate=accumulate_wgrad_into_param_main_grad,
 fp32_output=ctx.fuse_wgrad_accumulation,
 out=weight.main_grad if ctx.fuse_wgrad_accumulation else None,
 )

Expand DownExpand Up@@ -1652,7 +1649,6 @@ def backward(
 ctx.activation_dtype,
 get_workspace(),
 accumulate=accumulate_wgrad_into_param_main_grad,
 fp32_output=ctx.fuse_wgrad_accumulation,
 out=weight.main_grad if ctx.fuse_wgrad_accumulation else None,
 use_split_accumulator=_2X_ACC_WGRAD,
 )
Expand All@@ -1665,7 +1661,6 @@ def backward(
 layout="NT",
 grad=True,
 accumulate=accumulate_wgrad_into_param_main_grad,
 fp32_output=ctx.fuse_wgrad_accumulation,
 out=weight.main_grad if ctx.fuse_wgrad_accumulation else None,
 )
 else:
Expand All@@ -1679,7 +1674,6 @@ def backward(
 grad=True,
 use_bias=ctx.use_bias,
 accumulate=accumulate_wgrad_into_param_main_grad,
 fp32_output=ctx.fuse_wgrad_accumulation,
 out=weight.main_grad if ctx.fuse_wgrad_accumulation else None,
 )

Expand DownExpand Up@@ -2358,7 +2352,6 @@ def backward(
 ctx.activation_dtype,
 get_workspace(),
 accumulate=accumulate_wgrad_into_param_main_grad,
 fp32_output=ctx.fuse_wgrad_accumulation,
 out=fc2_weight.main_grad
 if ctx.fuse_wgrad_accumulation
 else None,
Expand DownExpand Up@@ -2390,7 +2383,6 @@ def backward(
 grad=True,
 use_bias=ctx.use_bias,
 accumulate=accumulate_wgrad_into_param_main_grad,
 fp32_output=ctx.fuse_wgrad_accumulation,
 out=fc2_weight.main_grad
 if ctx.fuse_wgrad_accumulation
 else None,
Expand DownExpand Up@@ -2446,7 +2438,6 @@ def backward(
 grad=True,
 use_bias=ctx.use_bias,
 accumulate=accumulate_wgrad_into_param_main_grad,
 fp32_output=ctx.fuse_wgrad_accumulation,
 out=fc2_weight.main_grad if ctx.fuse_wgrad_accumulation else None,
 )

Expand DownExpand Up@@ -2491,7 +2482,6 @@ def backward(
 ctx.activation_dtype,
 get_workspace(),
 accumulate=accumulate_wgrad_into_param_main_grad,
 fp32_output=ctx.fuse_wgrad_accumulation,
 out=fc1_weight.main_grad
 if ctx.fuse_wgrad_accumulation
 else None,
Expand All@@ -2513,7 +2503,6 @@ def backward(
 layout="NT",
 grad=True,
 accumulate=accumulate_wgrad_into_param_main_grad,
 fp32_output=ctx.fuse_wgrad_accumulation,
 out=fc1_weight.main_grad
 if ctx.fuse_wgrad_accumulation
 else None,
Expand All@@ -2529,7 +2518,6 @@ def backward(
 grad=True,
 use_bias=not ctx.bias_gelu_nvfusion,
 accumulate=accumulate_wgrad_into_param_main_grad,
 fp32_output=ctx.fuse_wgrad_accumulation,
 out=fc1_weight.main_grad if ctx.fuse_wgrad_accumulation else None,
 )

Expand Down

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Auto-enable theater mode on YouTube\n(function() {\n function tryTheater() {\n var btn = document.querySelector('button[aria-label=\"Theater mode\"], ytd-player #player button[title=\"Theater mode\"]');\n if (btn && !btn.classList.contains('activated')) {\n btn.click();\n }\n }\n \n // Try immediately\n tryTheater();\n \n // Try after navigation (SPA)\n var lastUrl = location.href;\n setInterval(function() {\n if (location.href !== lastUrl) {\n lastUrl = location.href;\n setTimeout(tryTheater, 500);\n }\n }, 1000);\n \n // Also try on player load\n var observer = new MutationObserver(tryTheater);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "YouTube Theater Mode Default");
}
} catch(__e) { console.warn('[Userscript:YouTube Theater Mode Default]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

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Support arbitrary output dtypes in PyT GEMM functions#75

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 Merged

ksivaman
merged 4 commits into

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Feb 23, 2023
 Merged


Support arbitrary output dtypes in PyT GEMM functions
#75
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c1bc953

Deprecate fp32_output option for PyT linear layers


timmoon10 Feb 15, 2023
3ab330a

Remove deprecated options


timmoon10 Feb 16, 2023
b6baff1

Merge branch 'main' into bf16-wgrad-bugfix


timmoon10 Feb 22, 2023
bfc386b

Merge branch 'main' into bf16-wgrad-bugfix


ksivaman Feb 23, 2023

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1 change: 0 additions & 1 deletion
tests/test_onnx_export.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -371,7 +371,6 @@ def forward(self, inp, weight):
 get_workspace(),
 bias=self.bias,
 use_bias=self.use_bias,
 fp32_output=(self.precision==torch.float32),
 use_split_accumulator=False)
 return ret

Expand Down

28 changes: 14 additions & 14 deletions
transformer_engine/pytorch/cpp_extensions.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -26,9 +26,8 @@ def fp8_gemm(
 fp8_meta_tensor: tex.FP8TensorMeta = None,
 bias: Optional[torch.Tensor] = None,
 use_bias: bool = False,
 fp32_output: bool = False,
 use_split_accumulator: bool = False,
 D_dtype: tex.DType = None,
 D_dtype: Optional[tex.DType] = None,
) -> torch.Tensor:
 """TN layout GEMM with fp8 inputs."""

Expand All@@ -41,15 +40,14 @@ def fp8_gemm(
 out = torch.empty(
 B.shape[0],
 A.shape[0],
 dtype=torch.float32 if fp32_output else out_dtype,
 dtype=out_dtype,
 device="cuda",
 )
 return_output = True

 out_dtype = tex.DType.kFloat32 if fp32_output else TE_DType[out_dtype]
 out_dtype = TE_DType[out.dtype] if D_dtype is None else D_dtype
 # Use bfloat16 as default bias_dtype
 bias_dtype = tex.DType.kBFloat16 if bias is None else TE_DType[bias.dtype]
 out_dtype = D_dtype if D_dtype is not None else out_dtype

 _ = torch.ops.tex_ts.te_gemm_ts(
 A,
Expand DownExpand Up@@ -94,7 +92,6 @@ def gemm(
 out: Optional[torch.Tensor] = None,
 bias: Optional[torch.Tensor] = None,
 use_bias: bool = False,
 fp32_output: bool = False,
) -> Tuple[Union[torch.Tensor, None], ...]:
 """Non FP8 GEMM."""

Expand All@@ -104,16 +101,12 @@ def gemm(
 empty_tensor = torch.Tensor()
 fp8_index = -1 # dummy index

 input_dtype = TE_DType[dtype]
 output_dtype = tex.DType.kFloat32 if fp32_output else input_dtype
 bias_dtype = output_dtype if bias is None else TE_DType[bias.dtype]

 return_output = False
 if out is None:
 out = torch.empty(
 B.shape[1] if transb else B.shape[0],
 A.shape[0] if transa else A.shape[1],
 dtype=torch.float32 if fp32_output else dtype,
 dtype=dtype,
 device="cuda",
 )
 return_output = True
Expand All@@ -124,14 +117,21 @@ def gemm(
 gelu_input = empty_tensor

 if grad and use_bias:
 grad_bias = torch.empty(
 B.shape[1], dtype=torch.float32 if fp32_output else dtype, device="cuda"
 )
 grad_bias = torch.empty(B.shape[1], dtype=out.dtype, device="cuda")
 else:
 grad_bias = empty_tensor

 bias = bias if use_bias else empty_tensor

 assert A.dtype == dtype and B.dtype == dtype, \
 f'Expected dtype={dtype}, but found A.dtype={A.dtype} and B.dtype={B.dtype}'
 input_dtype = TE_DType[dtype]
 output_dtype = TE_DType[out.dtype]
 if use_bias:
 bias_dtype = TE_DType[grad_bias.dtype] if grad else TE_DType[bias.dtype]
 else:
 bias_dtype = output_dtype

 _ = torch.ops.tex_ts.te_gemm_ts(
 A,
 empty_tensor,
Expand Down

12 changes: 0 additions & 12 deletions
transformer_engine/pytorch/module.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -960,7 +960,6 @@ def backward(
 ctx.activation_dtype,
 get_workspace(),
 accumulate=accumulate_wgrad_into_param_main_grad,
 fp32_output=ctx.fuse_wgrad_accumulation,
 out=weight.main_grad if ctx.fuse_wgrad_accumulation else None,
 use_split_accumulator=_2X_ACC_WGRAD,
 )
Expand All@@ -980,7 +979,6 @@ def backward(
 layout="NT",
 grad=True,
 accumulate=accumulate_wgrad_into_param_main_grad,
 fp32_output=ctx.fuse_wgrad_accumulation,
 out=weight.main_grad if ctx.fuse_wgrad_accumulation else None,
 )
 else:
Expand All@@ -994,7 +992,6 @@ def backward(
 grad=True,
 use_bias=ctx.use_bias,
 accumulate=accumulate_wgrad_into_param_main_grad,
 fp32_output=ctx.fuse_wgrad_accumulation,
 out=weight.main_grad if ctx.fuse_wgrad_accumulation else None,
 )

Expand DownExpand Up@@ -1652,7 +1649,6 @@ def backward(
 ctx.activation_dtype,
 get_workspace(),
 accumulate=accumulate_wgrad_into_param_main_grad,
 fp32_output=ctx.fuse_wgrad_accumulation,
 out=weight.main_grad if ctx.fuse_wgrad_accumulation else None,
 use_split_accumulator=_2X_ACC_WGRAD,
 )
Expand All@@ -1665,7 +1661,6 @@ def backward(
 layout="NT",
 grad=True,
 accumulate=accumulate_wgrad_into_param_main_grad,
 fp32_output=ctx.fuse_wgrad_accumulation,
 out=weight.main_grad if ctx.fuse_wgrad_accumulation else None,
 )
 else:
Expand All@@ -1679,7 +1674,6 @@ def backward(
 grad=True,
 use_bias=ctx.use_bias,
 accumulate=accumulate_wgrad_into_param_main_grad,
 fp32_output=ctx.fuse_wgrad_accumulation,
 out=weight.main_grad if ctx.fuse_wgrad_accumulation else None,
 )

Expand DownExpand Up@@ -2358,7 +2352,6 @@ def backward(
 ctx.activation_dtype,
 get_workspace(),
 accumulate=accumulate_wgrad_into_param_main_grad,
 fp32_output=ctx.fuse_wgrad_accumulation,
 out=fc2_weight.main_grad
 if ctx.fuse_wgrad_accumulation
 else None,
Expand DownExpand Up@@ -2390,7 +2383,6 @@ def backward(
 grad=True,
 use_bias=ctx.use_bias,
 accumulate=accumulate_wgrad_into_param_main_grad,
 fp32_output=ctx.fuse_wgrad_accumulation,
 out=fc2_weight.main_grad
 if ctx.fuse_wgrad_accumulation
 else None,
Expand DownExpand Up@@ -2446,7 +2438,6 @@ def backward(
 grad=True,
 use_bias=ctx.use_bias,
 accumulate=accumulate_wgrad_into_param_main_grad,
 fp32_output=ctx.fuse_wgrad_accumulation,
 out=fc2_weight.main_grad if ctx.fuse_wgrad_accumulation else None,
 )

Expand DownExpand Up@@ -2491,7 +2482,6 @@ def backward(
 ctx.activation_dtype,
 get_workspace(),
 accumulate=accumulate_wgrad_into_param_main_grad,
 fp32_output=ctx.fuse_wgrad_accumulation,
 out=fc1_weight.main_grad
 if ctx.fuse_wgrad_accumulation
 else None,
Expand All@@ -2513,7 +2503,6 @@ def backward(
 layout="NT",
 grad=True,
 accumulate=accumulate_wgrad_into_param_main_grad,
 fp32_output=ctx.fuse_wgrad_accumulation,
 out=fc1_weight.main_grad
 if ctx.fuse_wgrad_accumulation
 else None,
Expand All@@ -2529,7 +2518,6 @@ def backward(
 grad=True,
 use_bias=not ctx.bias_gelu_nvfusion,
 accumulate=accumulate_wgrad_into_param_main_grad,
 fp32_output=ctx.fuse_wgrad_accumulation,
 out=fc1_weight.main_grad if ctx.fuse_wgrad_accumulation else None,
 )

Expand Down

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Remove or un-stick sticky/fixed headers that block content\n(function() {\n function unstick() {\n document.querySelectorAll('header, nav, [role=\"banner\"], .header, .navbar, .sticky, .fixed-top, [style*=\"position: fixed\"], [style*=\"position:sticky\"]').forEach(function(el) {\n if (el.style.position === 'fixed' || el.style.position === 'sticky' || \n getComputedStyle(el).position === 'fixed' || getComputedStyle(el).position === 'sticky') {\n el.style.position = 'static';\n el.style.top = 'auto';\n el.style.zIndex = 'auto';\n }\n });\n }\n \n unstick();\n \n var observer = new MutationObserver(unstick);\n observer.observe(document.body, { childList: true, subtree: true, attributes: true, attributeFilter: ['style', 'class'] });\n})();", "Kill Sticky Headers");
}
} catch(__e) { console.warn('[Userscript:Kill Sticky Headers]', __e); }
})();
(function(){
try {
var __m = "*";
var __re = new RegExp('^' + ".*" + '

Skip to content
Support arbitrary output dtypes in PyT GEMM functions#75

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 Merged

ksivaman
merged 4 commits into

from

Feb 23, 2023
 Merged


Support arbitrary output dtypes in PyT GEMM functions
#75
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c1bc953

Deprecate fp32_output option for PyT linear layers


timmoon10 Feb 15, 2023
3ab330a

Remove deprecated options


timmoon10 Feb 16, 2023
b6baff1

Merge branch 'main' into bf16-wgrad-bugfix


timmoon10 Feb 22, 2023
bfc386b

Merge branch 'main' into bf16-wgrad-bugfix


ksivaman Feb 23, 2023

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1 change: 0 additions & 1 deletion
tests/test_onnx_export.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -371,7 +371,6 @@ def forward(self, inp, weight):
 get_workspace(),
 bias=self.bias,
 use_bias=self.use_bias,
 fp32_output=(self.precision==torch.float32),
 use_split_accumulator=False)
 return ret

Expand Down

28 changes: 14 additions & 14 deletions
transformer_engine/pytorch/cpp_extensions.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -26,9 +26,8 @@ def fp8_gemm(
 fp8_meta_tensor: tex.FP8TensorMeta = None,
 bias: Optional[torch.Tensor] = None,
 use_bias: bool = False,
 fp32_output: bool = False,
 use_split_accumulator: bool = False,
 D_dtype: tex.DType = None,
 D_dtype: Optional[tex.DType] = None,
) -> torch.Tensor:
 """TN layout GEMM with fp8 inputs."""

Expand All@@ -41,15 +40,14 @@ def fp8_gemm(
 out = torch.empty(
 B.shape[0],
 A.shape[0],
 dtype=torch.float32 if fp32_output else out_dtype,
 dtype=out_dtype,
 device="cuda",
 )
 return_output = True

 out_dtype = tex.DType.kFloat32 if fp32_output else TE_DType[out_dtype]
 out_dtype = TE_DType[out.dtype] if D_dtype is None else D_dtype
 # Use bfloat16 as default bias_dtype
 bias_dtype = tex.DType.kBFloat16 if bias is None else TE_DType[bias.dtype]
 out_dtype = D_dtype if D_dtype is not None else out_dtype

 _ = torch.ops.tex_ts.te_gemm_ts(
 A,
Expand DownExpand Up@@ -94,7 +92,6 @@ def gemm(
 out: Optional[torch.Tensor] = None,
 bias: Optional[torch.Tensor] = None,
 use_bias: bool = False,
 fp32_output: bool = False,
) -> Tuple[Union[torch.Tensor, None], ...]:
 """Non FP8 GEMM."""

Expand All@@ -104,16 +101,12 @@ def gemm(
 empty_tensor = torch.Tensor()
 fp8_index = -1 # dummy index

 input_dtype = TE_DType[dtype]
 output_dtype = tex.DType.kFloat32 if fp32_output else input_dtype
 bias_dtype = output_dtype if bias is None else TE_DType[bias.dtype]

 return_output = False
 if out is None:
 out = torch.empty(
 B.shape[1] if transb else B.shape[0],
 A.shape[0] if transa else A.shape[1],
 dtype=torch.float32 if fp32_output else dtype,
 dtype=dtype,
 device="cuda",
 )
 return_output = True
Expand All@@ -124,14 +117,21 @@ def gemm(
 gelu_input = empty_tensor

 if grad and use_bias:
 grad_bias = torch.empty(
 B.shape[1], dtype=torch.float32 if fp32_output else dtype, device="cuda"
 )
 grad_bias = torch.empty(B.shape[1], dtype=out.dtype, device="cuda")
 else:
 grad_bias = empty_tensor

 bias = bias if use_bias else empty_tensor

 assert A.dtype == dtype and B.dtype == dtype, \
 f'Expected dtype={dtype}, but found A.dtype={A.dtype} and B.dtype={B.dtype}'
 input_dtype = TE_DType[dtype]
 output_dtype = TE_DType[out.dtype]
 if use_bias:
 bias_dtype = TE_DType[grad_bias.dtype] if grad else TE_DType[bias.dtype]
 else:
 bias_dtype = output_dtype

 _ = torch.ops.tex_ts.te_gemm_ts(
 A,
 empty_tensor,
Expand Down

12 changes: 0 additions & 12 deletions
transformer_engine/pytorch/module.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -960,7 +960,6 @@ def backward(
 ctx.activation_dtype,
 get_workspace(),
 accumulate=accumulate_wgrad_into_param_main_grad,
 fp32_output=ctx.fuse_wgrad_accumulation,
 out=weight.main_grad if ctx.fuse_wgrad_accumulation else None,
 use_split_accumulator=_2X_ACC_WGRAD,
 )
Expand All@@ -980,7 +979,6 @@ def backward(
 layout="NT",
 grad=True,
 accumulate=accumulate_wgrad_into_param_main_grad,
 fp32_output=ctx.fuse_wgrad_accumulation,
 out=weight.main_grad if ctx.fuse_wgrad_accumulation else None,
 )
 else:
Expand All@@ -994,7 +992,6 @@ def backward(
 grad=True,
 use_bias=ctx.use_bias,
 accumulate=accumulate_wgrad_into_param_main_grad,
 fp32_output=ctx.fuse_wgrad_accumulation,
 out=weight.main_grad if ctx.fuse_wgrad_accumulation else None,
 )

Expand DownExpand Up@@ -1652,7 +1649,6 @@ def backward(
 ctx.activation_dtype,
 get_workspace(),
 accumulate=accumulate_wgrad_into_param_main_grad,
 fp32_output=ctx.fuse_wgrad_accumulation,
 out=weight.main_grad if ctx.fuse_wgrad_accumulation else None,
 use_split_accumulator=_2X_ACC_WGRAD,
 )
Expand All@@ -1665,7 +1661,6 @@ def backward(
 layout="NT",
 grad=True,
 accumulate=accumulate_wgrad_into_param_main_grad,
 fp32_output=ctx.fuse_wgrad_accumulation,
 out=weight.main_grad if ctx.fuse_wgrad_accumulation else None,
 )
 else:
Expand All@@ -1679,7 +1674,6 @@ def backward(
 grad=True,
 use_bias=ctx.use_bias,
 accumulate=accumulate_wgrad_into_param_main_grad,
 fp32_output=ctx.fuse_wgrad_accumulation,
 out=weight.main_grad if ctx.fuse_wgrad_accumulation else None,
 )

Expand DownExpand Up@@ -2358,7 +2352,6 @@ def backward(
 ctx.activation_dtype,
 get_workspace(),
 accumulate=accumulate_wgrad_into_param_main_grad,
 fp32_output=ctx.fuse_wgrad_accumulation,
 out=fc2_weight.main_grad
 if ctx.fuse_wgrad_accumulation
 else None,
Expand DownExpand Up@@ -2390,7 +2383,6 @@ def backward(
 grad=True,
 use_bias=ctx.use_bias,
 accumulate=accumulate_wgrad_into_param_main_grad,
 fp32_output=ctx.fuse_wgrad_accumulation,
 out=fc2_weight.main_grad
 if ctx.fuse_wgrad_accumulation
 else None,
Expand DownExpand Up@@ -2446,7 +2438,6 @@ def backward(
 grad=True,
 use_bias=ctx.use_bias,
 accumulate=accumulate_wgrad_into_param_main_grad,
 fp32_output=ctx.fuse_wgrad_accumulation,
 out=fc2_weight.main_grad if ctx.fuse_wgrad_accumulation else None,
 )

Expand DownExpand Up@@ -2491,7 +2482,6 @@ def backward(
 ctx.activation_dtype,
 get_workspace(),
 accumulate=accumulate_wgrad_into_param_main_grad,
 fp32_output=ctx.fuse_wgrad_accumulation,
 out=fc1_weight.main_grad
 if ctx.fuse_wgrad_accumulation
 else None,
Expand All@@ -2513,7 +2503,6 @@ def backward(
 layout="NT",
 grad=True,
 accumulate=accumulate_wgrad_into_param_main_grad,
 fp32_output=ctx.fuse_wgrad_accumulation,
 out=fc1_weight.main_grad
 if ctx.fuse_wgrad_accumulation
 else None,
Expand All@@ -2529,7 +2518,6 @@ def backward(
 grad=True,
 use_bias=not ctx.bias_gelu_nvfusion,
 accumulate=accumulate_wgrad_into_param_main_grad,
 fp32_output=ctx.fuse_wgrad_accumulation,
 out=fc1_weight.main_grad if ctx.fuse_wgrad_accumulation else None,
 )

Expand Down

, 'i');
if (__m === '*' || __re.test(location.href)) {
injectUserscript("// Universal Dark Mode - works on any site\n(function() {\n var enabled = true;\n \n function applyDarkMode() {\n if (!enabled) return;\n \n // Create style element if it doesn't exist\n var style = document.getElementById('universal-dark-mode-style');\n if (!style) {\n style = document.createElement('style');\n style.id = 'universal-dark-mode-style';\n document.head.appendChild(style);\n }\n \n // Dark mode CSS - inverts colors but preserves images/video\n style.textContent = '\n /* Invert everything except media */\n html {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #1a1a2e !important;\n }\n \n /* Restore images, videos, iframes, canvas */\n img, video, iframe, canvas, svg, picture, [style*=\"background-image\"] {\n filter: invert(1) hue-rotate(180deg) !important;\n }\n \n /* Preserve specific elements that should not be inverted */\n .no-dark-mode, .no-dark-mode *,\n [data-theme=\"light\"], [data-theme=\"light\"],\n .ace_editor, .ace_editor *,\n .CodeMirror, .CodeMirror *,\n .monaco-editor, .monaco-editor *,\n .markdown-body pre, .markdown-body pre *,\n .highlight, .highlight *,\n pre code, pre code * {\n filter: none !important;\n }\n \n /* Fix common UI elements */\n .modal, .popup, .dropdown-menu, .tooltip, .popover {\n filter: invert(1) hue-rotate(180deg) !important;\n background: #2d2d44 !important;\n border-color: #444 !important;\n }\n \n /* Scrollbars */\n ::-webkit-scrollbar { background: #1a1a2e !important; }\n ::-webkit-scrollbar-thumb { background: #444 !important; }\n ::-webkit-scrollbar-thumb:hover { background: #555 !important; }\n \n /* Selection */\n ::selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ::-moz-selection { background: #4ecdc4 !important; color: #1a1a2e !important; }\n ';\n }\n \n function removeDarkMode() {\n var style = document.getElementById('universal-dark-mode-style');\n if (style) style.remove();\n }\n \n // Toggle with Alt+Shift+D\n document.addEventListener('keydown', function(e) {\n if (e.altKey && e.shiftKey && e.key === 'D') {\n e.preventDefault();\n enabled = !enabled;\n if (enabled) {\n applyDarkMode();\n console.log('[Universal Dark Mode] Enabled');\n } else {\n removeDarkMode();\n console.log('[Universal Dark Mode] Disabled');\n }\n }\n });\n \n // Apply on load\n applyDarkMode();\n \n // Re-apply on dynamic content\n var observer = new MutationObserver(function(mutations) {\n if (enabled && !document.getElementById('universal-dark-mode-style')) {\n applyDarkMode();\n }\n });\n observer.observe(document.head, { childList: true });\n \n console.log('[Universal Dark Mode] Loaded - Press Alt+Shift+D to toggle');\n})();", "Universal Dark Mode");
}
} catch(__e) { console.warn('[Userscript:Universal Dark Mode]', __e); }
})();
})();

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Support arbitrary output dtypes in PyT GEMM functions#75

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 Merged

ksivaman
merged 4 commits into

from

Feb 23, 2023
 Merged


Support arbitrary output dtypes in PyT GEMM functions
#75
Show file tree
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c1bc953

Deprecate fp32_output option for PyT linear layers


timmoon10 Feb 15, 2023
3ab330a

Remove deprecated options


timmoon10 Feb 16, 2023
b6baff1

Merge branch 'main' into bf16-wgrad-bugfix


timmoon10 Feb 22, 2023
bfc386b

Merge branch 'main' into bf16-wgrad-bugfix


ksivaman Feb 23, 2023

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1 change: 0 additions & 1 deletion
tests/test_onnx_export.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -371,7 +371,6 @@ def forward(self, inp, weight):
 get_workspace(),
 bias=self.bias,
 use_bias=self.use_bias,
 fp32_output=(self.precision==torch.float32),
 use_split_accumulator=False)
 return ret

Expand Down

28 changes: 14 additions & 14 deletions
transformer_engine/pytorch/cpp_extensions.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -26,9 +26,8 @@ def fp8_gemm(
 fp8_meta_tensor: tex.FP8TensorMeta = None,
 bias: Optional[torch.Tensor] = None,
 use_bias: bool = False,
 fp32_output: bool = False,
 use_split_accumulator: bool = False,
 D_dtype: tex.DType = None,
 D_dtype: Optional[tex.DType] = None,
) -> torch.Tensor:
 """TN layout GEMM with fp8 inputs."""

Expand All@@ -41,15 +40,14 @@ def fp8_gemm(
 out = torch.empty(
 B.shape[0],
 A.shape[0],
 dtype=torch.float32 if fp32_output else out_dtype,
 dtype=out_dtype,
 device="cuda",
 )
 return_output = True

 out_dtype = tex.DType.kFloat32 if fp32_output else TE_DType[out_dtype]
 out_dtype = TE_DType[out.dtype] if D_dtype is None else D_dtype
 # Use bfloat16 as default bias_dtype
 bias_dtype = tex.DType.kBFloat16 if bias is None else TE_DType[bias.dtype]
 out_dtype = D_dtype if D_dtype is not None else out_dtype

 _ = torch.ops.tex_ts.te_gemm_ts(
 A,
Expand DownExpand Up@@ -94,7 +92,6 @@ def gemm(
 out: Optional[torch.Tensor] = None,
 bias: Optional[torch.Tensor] = None,
 use_bias: bool = False,
 fp32_output: bool = False,
) -> Tuple[Union[torch.Tensor, None], ...]:
 """Non FP8 GEMM."""

Expand All@@ -104,16 +101,12 @@ def gemm(
 empty_tensor = torch.Tensor()
 fp8_index = -1 # dummy index

 input_dtype = TE_DType[dtype]
 output_dtype = tex.DType.kFloat32 if fp32_output else input_dtype
 bias_dtype = output_dtype if bias is None else TE_DType[bias.dtype]

 return_output = False
 if out is None:
 out = torch.empty(
 B.shape[1] if transb else B.shape[0],
 A.shape[0] if transa else A.shape[1],
 dtype=torch.float32 if fp32_output else dtype,
 dtype=dtype,
 device="cuda",
 )
 return_output = True
Expand All@@ -124,14 +117,21 @@ def gemm(
 gelu_input = empty_tensor

 if grad and use_bias:
 grad_bias = torch.empty(
 B.shape[1], dtype=torch.float32 if fp32_output else dtype, device="cuda"
 )
 grad_bias = torch.empty(B.shape[1], dtype=out.dtype, device="cuda")
 else:
 grad_bias = empty_tensor

 bias = bias if use_bias else empty_tensor

 assert A.dtype == dtype and B.dtype == dtype, \
 f'Expected dtype={dtype}, but found A.dtype={A.dtype} and B.dtype={B.dtype}'
 input_dtype = TE_DType[dtype]
 output_dtype = TE_DType[out.dtype]
 if use_bias:
 bias_dtype = TE_DType[grad_bias.dtype] if grad else TE_DType[bias.dtype]
 else:
 bias_dtype = output_dtype

 _ = torch.ops.tex_ts.te_gemm_ts(
 A,
 empty_tensor,
Expand Down

12 changes: 0 additions & 12 deletions
transformer_engine/pytorch/module.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -960,7 +960,6 @@ def backward(
 ctx.activation_dtype,
 get_workspace(),
 accumulate=accumulate_wgrad_into_param_main_grad,
 fp32_output=ctx.fuse_wgrad_accumulation,
 out=weight.main_grad if ctx.fuse_wgrad_accumulation else None,
 use_split_accumulator=_2X_ACC_WGRAD,
 )
Expand All@@ -980,7 +979,6 @@ def backward(
 layout="NT",
 grad=True,
 accumulate=accumulate_wgrad_into_param_main_grad,
 fp32_output=ctx.fuse_wgrad_accumulation,
 out=weight.main_grad if ctx.fuse_wgrad_accumulation else None,
 )
 else:
Expand All@@ -994,7 +992,6 @@ def backward(
 grad=True,
 use_bias=ctx.use_bias,
 accumulate=accumulate_wgrad_into_param_main_grad,
 fp32_output=ctx.fuse_wgrad_accumulation,
 out=weight.main_grad if ctx.fuse_wgrad_accumulation else None,
 )

Expand DownExpand Up@@ -1652,7 +1649,6 @@ def backward(
 ctx.activation_dtype,
 get_workspace(),
 accumulate=accumulate_wgrad_into_param_main_grad,
 fp32_output=ctx.fuse_wgrad_accumulation,
 out=weight.main_grad if ctx.fuse_wgrad_accumulation else None,
 use_split_accumulator=_2X_ACC_WGRAD,
 )
Expand All@@ -1665,7 +1661,6 @@ def backward(
 layout="NT",
 grad=True,
 accumulate=accumulate_wgrad_into_param_main_grad,
 fp32_output=ctx.fuse_wgrad_accumulation,
 out=weight.main_grad if ctx.fuse_wgrad_accumulation else None,
 )
 else:
Expand All@@ -1679,7 +1674,6 @@ def backward(
 grad=True,
 use_bias=ctx.use_bias,
 accumulate=accumulate_wgrad_into_param_main_grad,
 fp32_output=ctx.fuse_wgrad_accumulation,
 out=weight.main_grad if ctx.fuse_wgrad_accumulation else None,
 )

Expand DownExpand Up@@ -2358,7 +2352,6 @@ def backward(
 ctx.activation_dtype,
 get_workspace(),
 accumulate=accumulate_wgrad_into_param_main_grad,
 fp32_output=ctx.fuse_wgrad_accumulation,
 out=fc2_weight.main_grad
 if ctx.fuse_wgrad_accumulation
 else None,
Expand DownExpand Up@@ -2390,7 +2383,6 @@ def backward(
 grad=True,
 use_bias=ctx.use_bias,
 accumulate=accumulate_wgrad_into_param_main_grad,
 fp32_output=ctx.fuse_wgrad_accumulation,
 out=fc2_weight.main_grad
 if ctx.fuse_wgrad_accumulation
 else None,
Expand DownExpand Up@@ -2446,7 +2438,6 @@ def backward(
 grad=True,
 use_bias=ctx.use_bias,
 accumulate=accumulate_wgrad_into_param_main_grad,
 fp32_output=ctx.fuse_wgrad_accumulation,
 out=fc2_weight.main_grad if ctx.fuse_wgrad_accumulation else None,
 )

Expand DownExpand Up@@ -2491,7 +2482,6 @@ def backward(
 ctx.activation_dtype,
 get_workspace(),
 accumulate=accumulate_wgrad_into_param_main_grad,
 fp32_output=ctx.fuse_wgrad_accumulation,
 out=fc1_weight.main_grad
 if ctx.fuse_wgrad_accumulation
 else None,
Expand All@@ -2513,7 +2503,6 @@ def backward(
 layout="NT",
 grad=True,
 accumulate=accumulate_wgrad_into_param_main_grad,
 fp32_output=ctx.fuse_wgrad_accumulation,
 out=fc1_weight.main_grad
 if ctx.fuse_wgrad_accumulation
 else None,
Expand All@@ -2529,7 +2518,6 @@ def backward(
 grad=True,
 use_bias=not ctx.bias_gelu_nvfusion,
 accumulate=accumulate_wgrad_into_param_main_grad,
 fp32_output=ctx.fuse_wgrad_accumulation,
 out=fc1_weight.main_grad if ctx.fuse_wgrad_accumulation else None,
 )

Expand Down