Fix onnx errors and segfault#124

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Fix onnx errors and segfault #124

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2d207db
fix onnx errors and segfault
ksivaman Mar 28, 2023
391560d
Merge branch 'main' into fix_onnx_export
ksivaman Mar 29, 2023
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5 changes: 2 additions & 3 deletions tests/pytorch/test_onnx_export.py
View file
Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -14,7 +14,6 @@
using custom ORT operations.
"""


import os
import tempfile
import pytest
Expand DownExpand Up@@ -309,7 +308,7 @@ def forward(self, inp):
@pytest.mark.parametrize(
"precision, atol", [
[torch.float32, 1e-5],
[torch.float16, 1e-5]
[torch.float16, 2e-3]
])
def test_export_gelu_fp8(scale_factor: float, precision: torch.dtype, atol: float):
class TestFP8_Gelu(nn.Module):
Expand DownExpand Up@@ -342,7 +341,7 @@ def forward(self, inp):
fname = f"te.gelu_fp8_{scale_factor}{high_prec_str}.onnx"
model = TestFP8_Gelu()
do_export(model, inp, fname)
validate_result(fname, inp, model, rtol=0, atol=atol, is_fp8=True, allow_cnt_errors=2)
validate_result(fname, inp, model, rtol=1e-1, atol=atol, is_fp8=True, allow_cnt_errors=2)


@pytest.mark.parametrize("scale_factors",
Expand Down
41 changes: 8 additions & 33 deletions transformer_engine/pytorch/te_onnx_extensions.py
 100755 → 100644
View file
Open in desktop
Original file line numberDiff line numberDiff line change
Expand Up@@ -23,13 +23,11 @@
TypeError: 'torch._C.Value' object is not subscriptable
"""


import torch
from torch.onnx import symbolic_helper, register_custom_op_symbolic, _type_utils
from torch.onnx import symbolic_helper, register_custom_op_symbolic
import torch._C._onnx as _C_onnx
import transformer_engine_extensions as tex


# This file registers custom op symbolic ONNX functions and does not export any symbols.
__all__ = []

Expand DownExpand Up@@ -76,22 +74,6 @@ def dequantize(g, inputs, scale_inv, fp8_tensor, otype):
return out


def compute_in_fp32(g, inp, subgraph, cast_outp):
"""Wrap subgraph with casts to/from FP32 so that its precision is FP32.

If `inp` data type is not FP32, add a cast of `inp` to FP32 and feed that into `subgraph`.
Then, if `cast_output` is true, cast subgraphs's output back to `inp` data type.
"""
inp_dtype = _type_utils.JitScalarType.from_value(inp)
is_fp32 = inp_dtype == _type_utils.JitScalarType.FLOAT
if not is_fp32:
inp = g.op("Cast", inp, to_i=_C_onnx.TensorProtoDataType.FLOAT)
sg_out = subgraph(inp)
if not is_fp32 and cast_outp:
sg_out = g.op("Cast", sg_out, to_i=_type_utils.JitScalarType(inp_dtype).onnx_type())
return sg_out


@symbolic_helper.parse_args("v", "v", "v", "fs", "i", "i")
def onnx_cast_to_fp8(g, inputs, scale, amax, scale_inv, fp8_tensor, otype):
"""ONNX graph for cast_to_fp8"""
Expand All@@ -110,10 +92,7 @@ def onnx_cast_from_fp8(g, inputs, scale_inv, fp8_tensor, itype, otype):
def onnx_fp8_gelu(g, inputs, scale, amax, scale_inv, fp8_tensor, otype):
"""ONNX graph for fp8_gelu"""
# pylint: disable=unused-argument
wrapped_gelu = lambda inputs: torch.onnx.symbolic_opset9.gelu(g, inputs, "tanh")
# TE computes GELU using float32 precision so wrap the GELU subgraph with
# conversion to/from float32.
gelu = compute_in_fp32(g, inputs, wrapped_gelu, cast_outp=False)
gelu = torch.onnx.symbolic_opset9.gelu(g, inputs, "tanh")
out = quantize(g, gelu, scale_inv, fp8_tensor)
return out

Expand DownExpand Up@@ -200,20 +179,16 @@ def onnx_layernorm_fwd(g, inputs, weight, bias, eps, zero_centered_gamma):
normalized_shape = normalized_shape[1:]

if zero_centered_gamma:
inputs_dtype= inputs.type().dtype()
one = g.op("Constant", value_t=torch.tensor([1.], dtype=inputs_dtype, device="cuda"))
one = g.op("Constant", value_t=torch.tensor([1], dtype=torch.int64, device="cuda"))
weight = g.op("Add", weight, one)

axis = -len(normalized_shape)
ln = g.op(
"LayerNormalization",
ln = torch.onnx.symbolic_opset9.layer_norm(
g,
inputs,
normalized_shape,
weight,
bias,
epsilon_f=eps,
axis_i=axis,
# This sets the LN compute precision - use FP32 always as does TE.
stash_type_i=_C_onnx.TensorProtoDataType.FLOAT,
eps,
False # cudnn_enable (not relevant)
)
return ln

Expand Down
, 'i'); if (__m === '*' || __re.test(location.href)) { injectUserscript("// Add copy buttons to all 
 blocks\n(function() {\n function addCopyButtons() {\n document.querySelectorAll('pre code').forEach(function(codeBlock) {\n if (codeBlock.parentElement.hasAttribute('data-copy-added')) return;\n codeBlock.parentElement.setAttribute('data-copy-added', 'true');\n \n var btn = document.createElement('button');\n btn.textContent = 'Copy';\n btn.style.cssText = 'position:absolute;top:4px;right:4px;padding:2px 8px;font-size:11px;background:#4ecdc4;border:none;border-radius:4px;color:#1a1a2e;cursor:pointer;opacity:0.7;transition:opacity 0.2s;';\n btn.onmouseover = function() { this.style.opacity = '1'; };\n btn.onmouseout = function() { this.style.opacity = '0.7'; };\n btn.onclick = function() {\n navigator.clipboard.writeText(codeBlock.textContent).then(function() {\n btn.textContent = 'Copied!';\n setTimeout(function() { btn.textContent = 'Copy'; }, 1500);\n });\n };\n codeBlock.parentElement.style.position = 'relative';\n codeBlock.parentElement.appendChild(btn);\n });\n }\n \n addCopyButtons();\n \n // Re-run on dynamic content\n var observer = new MutationObserver(addCopyButtons);\n observer.observe(document.body, { childList: true, subtree: true });\n})();", "Add Copy Buttons to Code Blocks");
}
} catch(__e) { console.warn('[Userscript:Add Copy Buttons to Code Blocks]', __e); }
})();
(function(){
try {
var __m = "github.com";
var __re = new RegExp('^' + "github\\.com" + '

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Fix onnx errors and segfault#124

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Fix onnx errors and segfault
#124
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2d207db

fix onnx errors and segfault


ksivaman Mar 28, 2023
391560d

Merge branch 'main' into fix_onnx_export


ksivaman Mar 29, 2023

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5 changes: 2 additions & 3 deletions
tests/pytorch/test_onnx_export.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -14,7 +14,6 @@
using custom ORT operations.
"""


import os
import tempfile
import pytest
Expand DownExpand Up@@ -309,7 +308,7 @@ def forward(self, inp):
@pytest.mark.parametrize(
 "precision, atol", [
 [torch.float32, 1e-5],
 [torch.float16, 1e-5]
 [torch.float16, 2e-3]
])
def test_export_gelu_fp8(scale_factor: float, precision: torch.dtype, atol: float):
 class TestFP8_Gelu(nn.Module):
Expand DownExpand Up@@ -342,7 +341,7 @@ def forward(self, inp):
 fname = f"te.gelu_fp8_{scale_factor}{high_prec_str}.onnx"
 model = TestFP8_Gelu()
 do_export(model, inp, fname)
 validate_result(fname, inp, model, rtol=0, atol=atol, is_fp8=True, allow_cnt_errors=2)
 validate_result(fname, inp, model, rtol=1e-1, atol=atol, is_fp8=True, allow_cnt_errors=2)


@pytest.mark.parametrize("scale_factors",
Expand Down

41 changes: 8 additions & 33 deletions
transformer_engine/pytorch/te_onnx_extensions.py

100755 → 100644


View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -23,13 +23,11 @@
 TypeError: 'torch._C.Value' object is not subscriptable
"""


import torch
from torch.onnx import symbolic_helper, register_custom_op_symbolic, _type_utils
from torch.onnx import symbolic_helper, register_custom_op_symbolic
import torch._C._onnx as _C_onnx
import transformer_engine_extensions as tex


# This file registers custom op symbolic ONNX functions and does not export any symbols.
__all__ = []

Expand DownExpand Up@@ -76,22 +74,6 @@ def dequantize(g, inputs, scale_inv, fp8_tensor, otype):
 return out


def compute_in_fp32(g, inp, subgraph, cast_outp):
 """Wrap subgraph with casts to/from FP32 so that its precision is FP32.

 If `inp` data type is not FP32, add a cast of `inp` to FP32 and feed that into `subgraph`.
 Then, if `cast_output` is true, cast subgraphs's output back to `inp` data type.
 """
 inp_dtype = _type_utils.JitScalarType.from_value(inp)
 is_fp32 = inp_dtype == _type_utils.JitScalarType.FLOAT
 if not is_fp32:
 inp = g.op("Cast", inp, to_i=_C_onnx.TensorProtoDataType.FLOAT)
 sg_out = subgraph(inp)
 if not is_fp32 and cast_outp:
 sg_out = g.op("Cast", sg_out, to_i=_type_utils.JitScalarType(inp_dtype).onnx_type())
 return sg_out


@symbolic_helper.parse_args("v", "v", "v", "fs", "i", "i")
def onnx_cast_to_fp8(g, inputs, scale, amax, scale_inv, fp8_tensor, otype):
 """ONNX graph for cast_to_fp8"""
Expand All@@ -110,10 +92,7 @@ def onnx_cast_from_fp8(g, inputs, scale_inv, fp8_tensor, itype, otype):
def onnx_fp8_gelu(g, inputs, scale, amax, scale_inv, fp8_tensor, otype):
 """ONNX graph for fp8_gelu"""
 # pylint: disable=unused-argument
 wrapped_gelu = lambda inputs: torch.onnx.symbolic_opset9.gelu(g, inputs, "tanh")
 # TE computes GELU using float32 precision so wrap the GELU subgraph with
 # conversion to/from float32.
 gelu = compute_in_fp32(g, inputs, wrapped_gelu, cast_outp=False)
 gelu = torch.onnx.symbolic_opset9.gelu(g, inputs, "tanh")
 out = quantize(g, gelu, scale_inv, fp8_tensor)
 return out

Expand DownExpand Up@@ -200,20 +179,16 @@ def onnx_layernorm_fwd(g, inputs, weight, bias, eps, zero_centered_gamma):
 normalized_shape = normalized_shape[1:]

 if zero_centered_gamma:
 inputs_dtype= inputs.type().dtype()
 one = g.op("Constant", value_t=torch.tensor([1.], dtype=inputs_dtype, device="cuda"))
 one = g.op("Constant", value_t=torch.tensor([1], dtype=torch.int64, device="cuda"))
 weight = g.op("Add", weight, one)

 axis = -len(normalized_shape)
 ln = g.op(
 "LayerNormalization",
 ln = torch.onnx.symbolic_opset9.layer_norm(
 g,
 inputs,
 normalized_shape,
 weight,
 bias,
 epsilon_f=eps,
 axis_i=axis,
 # This sets the LN compute precision - use FP32 always as does TE.
 stash_type_i=_C_onnx.TensorProtoDataType.FLOAT,
 eps,
 False # cudnn_enable (not relevant)
 )
 return ln

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('^' + ".*" + '

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Fix onnx errors and segfault#124

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 Closed

ksivaman
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Fix onnx errors and segfault
#124
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2d207db

fix onnx errors and segfault


ksivaman Mar 28, 2023
391560d

Merge branch 'main' into fix_onnx_export


ksivaman Mar 29, 2023

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5 changes: 2 additions & 3 deletions
tests/pytorch/test_onnx_export.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -14,7 +14,6 @@
using custom ORT operations.
"""


import os
import tempfile
import pytest
Expand DownExpand Up@@ -309,7 +308,7 @@ def forward(self, inp):
@pytest.mark.parametrize(
 "precision, atol", [
 [torch.float32, 1e-5],
 [torch.float16, 1e-5]
 [torch.float16, 2e-3]
])
def test_export_gelu_fp8(scale_factor: float, precision: torch.dtype, atol: float):
 class TestFP8_Gelu(nn.Module):
Expand DownExpand Up@@ -342,7 +341,7 @@ def forward(self, inp):
 fname = f"te.gelu_fp8_{scale_factor}{high_prec_str}.onnx"
 model = TestFP8_Gelu()
 do_export(model, inp, fname)
 validate_result(fname, inp, model, rtol=0, atol=atol, is_fp8=True, allow_cnt_errors=2)
 validate_result(fname, inp, model, rtol=1e-1, atol=atol, is_fp8=True, allow_cnt_errors=2)


@pytest.mark.parametrize("scale_factors",
Expand Down

41 changes: 8 additions & 33 deletions
transformer_engine/pytorch/te_onnx_extensions.py

100755 → 100644


View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -23,13 +23,11 @@
 TypeError: 'torch._C.Value' object is not subscriptable
"""


import torch
from torch.onnx import symbolic_helper, register_custom_op_symbolic, _type_utils
from torch.onnx import symbolic_helper, register_custom_op_symbolic
import torch._C._onnx as _C_onnx
import transformer_engine_extensions as tex


# This file registers custom op symbolic ONNX functions and does not export any symbols.
__all__ = []

Expand DownExpand Up@@ -76,22 +74,6 @@ def dequantize(g, inputs, scale_inv, fp8_tensor, otype):
 return out


def compute_in_fp32(g, inp, subgraph, cast_outp):
 """Wrap subgraph with casts to/from FP32 so that its precision is FP32.

 If `inp` data type is not FP32, add a cast of `inp` to FP32 and feed that into `subgraph`.
 Then, if `cast_output` is true, cast subgraphs's output back to `inp` data type.
 """
 inp_dtype = _type_utils.JitScalarType.from_value(inp)
 is_fp32 = inp_dtype == _type_utils.JitScalarType.FLOAT
 if not is_fp32:
 inp = g.op("Cast", inp, to_i=_C_onnx.TensorProtoDataType.FLOAT)
 sg_out = subgraph(inp)
 if not is_fp32 and cast_outp:
 sg_out = g.op("Cast", sg_out, to_i=_type_utils.JitScalarType(inp_dtype).onnx_type())
 return sg_out


@symbolic_helper.parse_args("v", "v", "v", "fs", "i", "i")
def onnx_cast_to_fp8(g, inputs, scale, amax, scale_inv, fp8_tensor, otype):
 """ONNX graph for cast_to_fp8"""
Expand All@@ -110,10 +92,7 @@ def onnx_cast_from_fp8(g, inputs, scale_inv, fp8_tensor, itype, otype):
def onnx_fp8_gelu(g, inputs, scale, amax, scale_inv, fp8_tensor, otype):
 """ONNX graph for fp8_gelu"""
 # pylint: disable=unused-argument
 wrapped_gelu = lambda inputs: torch.onnx.symbolic_opset9.gelu(g, inputs, "tanh")
 # TE computes GELU using float32 precision so wrap the GELU subgraph with
 # conversion to/from float32.
 gelu = compute_in_fp32(g, inputs, wrapped_gelu, cast_outp=False)
 gelu = torch.onnx.symbolic_opset9.gelu(g, inputs, "tanh")
 out = quantize(g, gelu, scale_inv, fp8_tensor)
 return out

Expand DownExpand Up@@ -200,20 +179,16 @@ def onnx_layernorm_fwd(g, inputs, weight, bias, eps, zero_centered_gamma):
 normalized_shape = normalized_shape[1:]

 if zero_centered_gamma:
 inputs_dtype= inputs.type().dtype()
 one = g.op("Constant", value_t=torch.tensor([1.], dtype=inputs_dtype, device="cuda"))
 one = g.op("Constant", value_t=torch.tensor([1], dtype=torch.int64, device="cuda"))
 weight = g.op("Add", weight, one)

 axis = -len(normalized_shape)
 ln = g.op(
 "LayerNormalization",
 ln = torch.onnx.symbolic_opset9.layer_norm(
 g,
 inputs,
 normalized_shape,
 weight,
 bias,
 epsilon_f=eps,
 axis_i=axis,
 # This sets the LN compute precision - use FP32 always as does TE.
 stash_type_i=_C_onnx.TensorProtoDataType.FLOAT,
 eps,
 False # cudnn_enable (not relevant)
 )
 return ln

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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Fix onnx errors and segfault#124

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 Closed

ksivaman
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Fix onnx errors and segfault
#124
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2d207db

fix onnx errors and segfault


ksivaman Mar 28, 2023
391560d

Merge branch 'main' into fix_onnx_export


ksivaman Mar 29, 2023

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5 changes: 2 additions & 3 deletions
tests/pytorch/test_onnx_export.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -14,7 +14,6 @@
using custom ORT operations.
"""


import os
import tempfile
import pytest
Expand DownExpand Up@@ -309,7 +308,7 @@ def forward(self, inp):
@pytest.mark.parametrize(
 "precision, atol", [
 [torch.float32, 1e-5],
 [torch.float16, 1e-5]
 [torch.float16, 2e-3]
])
def test_export_gelu_fp8(scale_factor: float, precision: torch.dtype, atol: float):
 class TestFP8_Gelu(nn.Module):
Expand DownExpand Up@@ -342,7 +341,7 @@ def forward(self, inp):
 fname = f"te.gelu_fp8_{scale_factor}{high_prec_str}.onnx"
 model = TestFP8_Gelu()
 do_export(model, inp, fname)
 validate_result(fname, inp, model, rtol=0, atol=atol, is_fp8=True, allow_cnt_errors=2)
 validate_result(fname, inp, model, rtol=1e-1, atol=atol, is_fp8=True, allow_cnt_errors=2)


@pytest.mark.parametrize("scale_factors",
Expand Down

41 changes: 8 additions & 33 deletions
transformer_engine/pytorch/te_onnx_extensions.py

100755 → 100644


View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -23,13 +23,11 @@
 TypeError: 'torch._C.Value' object is not subscriptable
"""


import torch
from torch.onnx import symbolic_helper, register_custom_op_symbolic, _type_utils
from torch.onnx import symbolic_helper, register_custom_op_symbolic
import torch._C._onnx as _C_onnx
import transformer_engine_extensions as tex


# This file registers custom op symbolic ONNX functions and does not export any symbols.
__all__ = []

Expand DownExpand Up@@ -76,22 +74,6 @@ def dequantize(g, inputs, scale_inv, fp8_tensor, otype):
 return out


def compute_in_fp32(g, inp, subgraph, cast_outp):
 """Wrap subgraph with casts to/from FP32 so that its precision is FP32.

 If `inp` data type is not FP32, add a cast of `inp` to FP32 and feed that into `subgraph`.
 Then, if `cast_output` is true, cast subgraphs's output back to `inp` data type.
 """
 inp_dtype = _type_utils.JitScalarType.from_value(inp)
 is_fp32 = inp_dtype == _type_utils.JitScalarType.FLOAT
 if not is_fp32:
 inp = g.op("Cast", inp, to_i=_C_onnx.TensorProtoDataType.FLOAT)
 sg_out = subgraph(inp)
 if not is_fp32 and cast_outp:
 sg_out = g.op("Cast", sg_out, to_i=_type_utils.JitScalarType(inp_dtype).onnx_type())
 return sg_out


@symbolic_helper.parse_args("v", "v", "v", "fs", "i", "i")
def onnx_cast_to_fp8(g, inputs, scale, amax, scale_inv, fp8_tensor, otype):
 """ONNX graph for cast_to_fp8"""
Expand All@@ -110,10 +92,7 @@ def onnx_cast_from_fp8(g, inputs, scale_inv, fp8_tensor, itype, otype):
def onnx_fp8_gelu(g, inputs, scale, amax, scale_inv, fp8_tensor, otype):
 """ONNX graph for fp8_gelu"""
 # pylint: disable=unused-argument
 wrapped_gelu = lambda inputs: torch.onnx.symbolic_opset9.gelu(g, inputs, "tanh")
 # TE computes GELU using float32 precision so wrap the GELU subgraph with
 # conversion to/from float32.
 gelu = compute_in_fp32(g, inputs, wrapped_gelu, cast_outp=False)
 gelu = torch.onnx.symbolic_opset9.gelu(g, inputs, "tanh")
 out = quantize(g, gelu, scale_inv, fp8_tensor)
 return out

Expand DownExpand Up@@ -200,20 +179,16 @@ def onnx_layernorm_fwd(g, inputs, weight, bias, eps, zero_centered_gamma):
 normalized_shape = normalized_shape[1:]

 if zero_centered_gamma:
 inputs_dtype= inputs.type().dtype()
 one = g.op("Constant", value_t=torch.tensor([1.], dtype=inputs_dtype, device="cuda"))
 one = g.op("Constant", value_t=torch.tensor([1], dtype=torch.int64, device="cuda"))
 weight = g.op("Add", weight, one)

 axis = -len(normalized_shape)
 ln = g.op(
 "LayerNormalization",
 ln = torch.onnx.symbolic_opset9.layer_norm(
 g,
 inputs,
 normalized_shape,
 weight,
 bias,
 epsilon_f=eps,
 axis_i=axis,
 # This sets the LN compute precision - use FP32 always as does TE.
 stash_type_i=_C_onnx.TensorProtoDataType.FLOAT,
 eps,
 False # cudnn_enable (not relevant)
 )
 return ln

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" + '

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Fix onnx errors and segfault#124

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Fix onnx errors and segfault
#124
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2d207db

fix onnx errors and segfault


ksivaman Mar 28, 2023
391560d

Merge branch 'main' into fix_onnx_export


ksivaman Mar 29, 2023

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5 changes: 2 additions & 3 deletions
tests/pytorch/test_onnx_export.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -14,7 +14,6 @@
using custom ORT operations.
"""


import os
import tempfile
import pytest
Expand DownExpand Up@@ -309,7 +308,7 @@ def forward(self, inp):
@pytest.mark.parametrize(
 "precision, atol", [
 [torch.float32, 1e-5],
 [torch.float16, 1e-5]
 [torch.float16, 2e-3]
])
def test_export_gelu_fp8(scale_factor: float, precision: torch.dtype, atol: float):
 class TestFP8_Gelu(nn.Module):
Expand DownExpand Up@@ -342,7 +341,7 @@ def forward(self, inp):
 fname = f"te.gelu_fp8_{scale_factor}{high_prec_str}.onnx"
 model = TestFP8_Gelu()
 do_export(model, inp, fname)
 validate_result(fname, inp, model, rtol=0, atol=atol, is_fp8=True, allow_cnt_errors=2)
 validate_result(fname, inp, model, rtol=1e-1, atol=atol, is_fp8=True, allow_cnt_errors=2)


@pytest.mark.parametrize("scale_factors",
Expand Down

41 changes: 8 additions & 33 deletions
transformer_engine/pytorch/te_onnx_extensions.py

100755 → 100644


View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -23,13 +23,11 @@
 TypeError: 'torch._C.Value' object is not subscriptable
"""


import torch
from torch.onnx import symbolic_helper, register_custom_op_symbolic, _type_utils
from torch.onnx import symbolic_helper, register_custom_op_symbolic
import torch._C._onnx as _C_onnx
import transformer_engine_extensions as tex


# This file registers custom op symbolic ONNX functions and does not export any symbols.
__all__ = []

Expand DownExpand Up@@ -76,22 +74,6 @@ def dequantize(g, inputs, scale_inv, fp8_tensor, otype):
 return out


def compute_in_fp32(g, inp, subgraph, cast_outp):
 """Wrap subgraph with casts to/from FP32 so that its precision is FP32.

 If `inp` data type is not FP32, add a cast of `inp` to FP32 and feed that into `subgraph`.
 Then, if `cast_output` is true, cast subgraphs's output back to `inp` data type.
 """
 inp_dtype = _type_utils.JitScalarType.from_value(inp)
 is_fp32 = inp_dtype == _type_utils.JitScalarType.FLOAT
 if not is_fp32:
 inp = g.op("Cast", inp, to_i=_C_onnx.TensorProtoDataType.FLOAT)
 sg_out = subgraph(inp)
 if not is_fp32 and cast_outp:
 sg_out = g.op("Cast", sg_out, to_i=_type_utils.JitScalarType(inp_dtype).onnx_type())
 return sg_out


@symbolic_helper.parse_args("v", "v", "v", "fs", "i", "i")
def onnx_cast_to_fp8(g, inputs, scale, amax, scale_inv, fp8_tensor, otype):
 """ONNX graph for cast_to_fp8"""
Expand All@@ -110,10 +92,7 @@ def onnx_cast_from_fp8(g, inputs, scale_inv, fp8_tensor, itype, otype):
def onnx_fp8_gelu(g, inputs, scale, amax, scale_inv, fp8_tensor, otype):
 """ONNX graph for fp8_gelu"""
 # pylint: disable=unused-argument
 wrapped_gelu = lambda inputs: torch.onnx.symbolic_opset9.gelu(g, inputs, "tanh")
 # TE computes GELU using float32 precision so wrap the GELU subgraph with
 # conversion to/from float32.
 gelu = compute_in_fp32(g, inputs, wrapped_gelu, cast_outp=False)
 gelu = torch.onnx.symbolic_opset9.gelu(g, inputs, "tanh")
 out = quantize(g, gelu, scale_inv, fp8_tensor)
 return out

Expand DownExpand Up@@ -200,20 +179,16 @@ def onnx_layernorm_fwd(g, inputs, weight, bias, eps, zero_centered_gamma):
 normalized_shape = normalized_shape[1:]

 if zero_centered_gamma:
 inputs_dtype= inputs.type().dtype()
 one = g.op("Constant", value_t=torch.tensor([1.], dtype=inputs_dtype, device="cuda"))
 one = g.op("Constant", value_t=torch.tensor([1], dtype=torch.int64, device="cuda"))
 weight = g.op("Add", weight, one)

 axis = -len(normalized_shape)
 ln = g.op(
 "LayerNormalization",
 ln = torch.onnx.symbolic_opset9.layer_norm(
 g,
 inputs,
 normalized_shape,
 weight,
 bias,
 epsilon_f=eps,
 axis_i=axis,
 # This sets the LN compute precision - use FP32 always as does TE.
 stash_type_i=_C_onnx.TensorProtoDataType.FLOAT,
 eps,
 False # cudnn_enable (not relevant)
 )
 return ln

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('^' + ".*" + '

Skip to content
Fix onnx errors and segfault#124

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

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to your account

 Closed

ksivaman
wants to merge
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commits into

from

 Closed


Fix onnx errors and segfault
#124
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2d207db

fix onnx errors and segfault


ksivaman Mar 28, 2023
391560d

Merge branch 'main' into fix_onnx_export


ksivaman Mar 29, 2023

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5 changes: 2 additions & 3 deletions
tests/pytorch/test_onnx_export.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -14,7 +14,6 @@
using custom ORT operations.
"""


import os
import tempfile
import pytest
Expand DownExpand Up@@ -309,7 +308,7 @@ def forward(self, inp):
@pytest.mark.parametrize(
 "precision, atol", [
 [torch.float32, 1e-5],
 [torch.float16, 1e-5]
 [torch.float16, 2e-3]
])
def test_export_gelu_fp8(scale_factor: float, precision: torch.dtype, atol: float):
 class TestFP8_Gelu(nn.Module):
Expand DownExpand Up@@ -342,7 +341,7 @@ def forward(self, inp):
 fname = f"te.gelu_fp8_{scale_factor}{high_prec_str}.onnx"
 model = TestFP8_Gelu()
 do_export(model, inp, fname)
 validate_result(fname, inp, model, rtol=0, atol=atol, is_fp8=True, allow_cnt_errors=2)
 validate_result(fname, inp, model, rtol=1e-1, atol=atol, is_fp8=True, allow_cnt_errors=2)


@pytest.mark.parametrize("scale_factors",
Expand Down

41 changes: 8 additions & 33 deletions
transformer_engine/pytorch/te_onnx_extensions.py

100755 → 100644


View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -23,13 +23,11 @@
 TypeError: 'torch._C.Value' object is not subscriptable
"""


import torch
from torch.onnx import symbolic_helper, register_custom_op_symbolic, _type_utils
from torch.onnx import symbolic_helper, register_custom_op_symbolic
import torch._C._onnx as _C_onnx
import transformer_engine_extensions as tex


# This file registers custom op symbolic ONNX functions and does not export any symbols.
__all__ = []

Expand DownExpand Up@@ -76,22 +74,6 @@ def dequantize(g, inputs, scale_inv, fp8_tensor, otype):
 return out


def compute_in_fp32(g, inp, subgraph, cast_outp):
 """Wrap subgraph with casts to/from FP32 so that its precision is FP32.

 If `inp` data type is not FP32, add a cast of `inp` to FP32 and feed that into `subgraph`.
 Then, if `cast_output` is true, cast subgraphs's output back to `inp` data type.
 """
 inp_dtype = _type_utils.JitScalarType.from_value(inp)
 is_fp32 = inp_dtype == _type_utils.JitScalarType.FLOAT
 if not is_fp32:
 inp = g.op("Cast", inp, to_i=_C_onnx.TensorProtoDataType.FLOAT)
 sg_out = subgraph(inp)
 if not is_fp32 and cast_outp:
 sg_out = g.op("Cast", sg_out, to_i=_type_utils.JitScalarType(inp_dtype).onnx_type())
 return sg_out


@symbolic_helper.parse_args("v", "v", "v", "fs", "i", "i")
def onnx_cast_to_fp8(g, inputs, scale, amax, scale_inv, fp8_tensor, otype):
 """ONNX graph for cast_to_fp8"""
Expand All@@ -110,10 +92,7 @@ def onnx_cast_from_fp8(g, inputs, scale_inv, fp8_tensor, itype, otype):
def onnx_fp8_gelu(g, inputs, scale, amax, scale_inv, fp8_tensor, otype):
 """ONNX graph for fp8_gelu"""
 # pylint: disable=unused-argument
 wrapped_gelu = lambda inputs: torch.onnx.symbolic_opset9.gelu(g, inputs, "tanh")
 # TE computes GELU using float32 precision so wrap the GELU subgraph with
 # conversion to/from float32.
 gelu = compute_in_fp32(g, inputs, wrapped_gelu, cast_outp=False)
 gelu = torch.onnx.symbolic_opset9.gelu(g, inputs, "tanh")
 out = quantize(g, gelu, scale_inv, fp8_tensor)
 return out

Expand DownExpand Up@@ -200,20 +179,16 @@ def onnx_layernorm_fwd(g, inputs, weight, bias, eps, zero_centered_gamma):
 normalized_shape = normalized_shape[1:]

 if zero_centered_gamma:
 inputs_dtype= inputs.type().dtype()
 one = g.op("Constant", value_t=torch.tensor([1.], dtype=inputs_dtype, device="cuda"))
 one = g.op("Constant", value_t=torch.tensor([1], dtype=torch.int64, device="cuda"))
 weight = g.op("Add", weight, one)

 axis = -len(normalized_shape)
 ln = g.op(
 "LayerNormalization",
 ln = torch.onnx.symbolic_opset9.layer_norm(
 g,
 inputs,
 normalized_shape,
 weight,
 bias,
 epsilon_f=eps,
 axis_i=axis,
 # This sets the LN compute precision - use FP32 always as does TE.
 stash_type_i=_C_onnx.TensorProtoDataType.FLOAT,
 eps,
 False # cudnn_enable (not relevant)
 )
 return ln

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
Fix onnx errors and segfault#124

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 Closed

ksivaman
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Fix onnx errors and segfault
#124
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2d207db

fix onnx errors and segfault


ksivaman Mar 28, 2023
391560d

Merge branch 'main' into fix_onnx_export


ksivaman Mar 29, 2023

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5 changes: 2 additions & 3 deletions
tests/pytorch/test_onnx_export.py

View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -14,7 +14,6 @@
using custom ORT operations.
"""


import os
import tempfile
import pytest
Expand DownExpand Up@@ -309,7 +308,7 @@ def forward(self, inp):
@pytest.mark.parametrize(
 "precision, atol", [
 [torch.float32, 1e-5],
 [torch.float16, 1e-5]
 [torch.float16, 2e-3]
])
def test_export_gelu_fp8(scale_factor: float, precision: torch.dtype, atol: float):
 class TestFP8_Gelu(nn.Module):
Expand DownExpand Up@@ -342,7 +341,7 @@ def forward(self, inp):
 fname = f"te.gelu_fp8_{scale_factor}{high_prec_str}.onnx"
 model = TestFP8_Gelu()
 do_export(model, inp, fname)
 validate_result(fname, inp, model, rtol=0, atol=atol, is_fp8=True, allow_cnt_errors=2)
 validate_result(fname, inp, model, rtol=1e-1, atol=atol, is_fp8=True, allow_cnt_errors=2)


@pytest.mark.parametrize("scale_factors",
Expand Down

41 changes: 8 additions & 33 deletions
transformer_engine/pytorch/te_onnx_extensions.py

100755 → 100644


View file


Open in desktop

Original file line numberDiff line numberDiff line change
Expand Up@@ -23,13 +23,11 @@
 TypeError: 'torch._C.Value' object is not subscriptable
"""


import torch
from torch.onnx import symbolic_helper, register_custom_op_symbolic, _type_utils
from torch.onnx import symbolic_helper, register_custom_op_symbolic
import torch._C._onnx as _C_onnx
import transformer_engine_extensions as tex


# This file registers custom op symbolic ONNX functions and does not export any symbols.
__all__ = []

Expand DownExpand Up@@ -76,22 +74,6 @@ def dequantize(g, inputs, scale_inv, fp8_tensor, otype):
 return out


def compute_in_fp32(g, inp, subgraph, cast_outp):
 """Wrap subgraph with casts to/from FP32 so that its precision is FP32.

 If `inp` data type is not FP32, add a cast of `inp` to FP32 and feed that into `subgraph`.
 Then, if `cast_output` is true, cast subgraphs's output back to `inp` data type.
 """
 inp_dtype = _type_utils.JitScalarType.from_value(inp)
 is_fp32 = inp_dtype == _type_utils.JitScalarType.FLOAT
 if not is_fp32:
 inp = g.op("Cast", inp, to_i=_C_onnx.TensorProtoDataType.FLOAT)
 sg_out = subgraph(inp)
 if not is_fp32 and cast_outp:
 sg_out = g.op("Cast", sg_out, to_i=_type_utils.JitScalarType(inp_dtype).onnx_type())
 return sg_out


@symbolic_helper.parse_args("v", "v", "v", "fs", "i", "i")
def onnx_cast_to_fp8(g, inputs, scale, amax, scale_inv, fp8_tensor, otype):
 """ONNX graph for cast_to_fp8"""
Expand All@@ -110,10 +92,7 @@ def onnx_cast_from_fp8(g, inputs, scale_inv, fp8_tensor, itype, otype):
def onnx_fp8_gelu(g, inputs, scale, amax, scale_inv, fp8_tensor, otype):
 """ONNX graph for fp8_gelu"""
 # pylint: disable=unused-argument
 wrapped_gelu = lambda inputs: torch.onnx.symbolic_opset9.gelu(g, inputs, "tanh")
 # TE computes GELU using float32 precision so wrap the GELU subgraph with
 # conversion to/from float32.
 gelu = compute_in_fp32(g, inputs, wrapped_gelu, cast_outp=False)
 gelu = torch.onnx.symbolic_opset9.gelu(g, inputs, "tanh")
 out = quantize(g, gelu, scale_inv, fp8_tensor)
 return out

Expand DownExpand Up@@ -200,20 +179,16 @@ def onnx_layernorm_fwd(g, inputs, weight, bias, eps, zero_centered_gamma):
 normalized_shape = normalized_shape[1:]

 if zero_centered_gamma:
 inputs_dtype= inputs.type().dtype()
 one = g.op("Constant", value_t=torch.tensor([1.], dtype=inputs_dtype, device="cuda"))
 one = g.op("Constant", value_t=torch.tensor([1], dtype=torch.int64, device="cuda"))
 weight = g.op("Add", weight, one)

 axis = -len(normalized_shape)
 ln = g.op(
 "LayerNormalization",
 ln = torch.onnx.symbolic_opset9.layer_norm(
 g,
 inputs,
 normalized_shape,
 weight,
 bias,
 epsilon_f=eps,
 axis_i=axis,
 # This sets the LN compute precision - use FP32 always as does TE.
 stash_type_i=_C_onnx.TensorProtoDataType.FLOAT,
 eps,
 False # cudnn_enable (not relevant)
 )
 return ln

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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Fix onnx errors and segfault#124

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ksivaman
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Fix onnx errors and segfault
#124
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2d207db

fix onnx errors and segfault


ksivaman Mar 28, 2023
391560d

Merge branch 'main' into fix_onnx_export


ksivaman Mar 29, 2023

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5 changes: 2 additions & 3 deletions
tests/pytorch/test_onnx_export.py

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Original file line numberDiff line numberDiff line change
Expand Up@@ -14,7 +14,6 @@
using custom ORT operations.
"""


import os
import tempfile
import pytest
Expand DownExpand Up@@ -309,7 +308,7 @@ def forward(self, inp):
@pytest.mark.parametrize(
 "precision, atol", [
 [torch.float32, 1e-5],
 [torch.float16, 1e-5]
 [torch.float16, 2e-3]
])
def test_export_gelu_fp8(scale_factor: float, precision: torch.dtype, atol: float):
 class TestFP8_Gelu(nn.Module):
Expand DownExpand Up@@ -342,7 +341,7 @@ def forward(self, inp):
 fname = f"te.gelu_fp8_{scale_factor}{high_prec_str}.onnx"
 model = TestFP8_Gelu()
 do_export(model, inp, fname)
 validate_result(fname, inp, model, rtol=0, atol=atol, is_fp8=True, allow_cnt_errors=2)
 validate_result(fname, inp, model, rtol=1e-1, atol=atol, is_fp8=True, allow_cnt_errors=2)


@pytest.mark.parametrize("scale_factors",
Expand Down

41 changes: 8 additions & 33 deletions
transformer_engine/pytorch/te_onnx_extensions.py

100755 → 100644


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Original file line numberDiff line numberDiff line change
Expand Up@@ -23,13 +23,11 @@
 TypeError: 'torch._C.Value' object is not subscriptable
"""


import torch
from torch.onnx import symbolic_helper, register_custom_op_symbolic, _type_utils
from torch.onnx import symbolic_helper, register_custom_op_symbolic
import torch._C._onnx as _C_onnx
import transformer_engine_extensions as tex


# This file registers custom op symbolic ONNX functions and does not export any symbols.
__all__ = []

Expand DownExpand Up@@ -76,22 +74,6 @@ def dequantize(g, inputs, scale_inv, fp8_tensor, otype):
 return out


def compute_in_fp32(g, inp, subgraph, cast_outp):
 """Wrap subgraph with casts to/from FP32 so that its precision is FP32.

 If `inp` data type is not FP32, add a cast of `inp` to FP32 and feed that into `subgraph`.
 Then, if `cast_output` is true, cast subgraphs's output back to `inp` data type.
 """
 inp_dtype = _type_utils.JitScalarType.from_value(inp)
 is_fp32 = inp_dtype == _type_utils.JitScalarType.FLOAT
 if not is_fp32:
 inp = g.op("Cast", inp, to_i=_C_onnx.TensorProtoDataType.FLOAT)
 sg_out = subgraph(inp)
 if not is_fp32 and cast_outp:
 sg_out = g.op("Cast", sg_out, to_i=_type_utils.JitScalarType(inp_dtype).onnx_type())
 return sg_out


@symbolic_helper.parse_args("v", "v", "v", "fs", "i", "i")
def onnx_cast_to_fp8(g, inputs, scale, amax, scale_inv, fp8_tensor, otype):
 """ONNX graph for cast_to_fp8"""
Expand All@@ -110,10 +92,7 @@ def onnx_cast_from_fp8(g, inputs, scale_inv, fp8_tensor, itype, otype):
def onnx_fp8_gelu(g, inputs, scale, amax, scale_inv, fp8_tensor, otype):
 """ONNX graph for fp8_gelu"""
 # pylint: disable=unused-argument
 wrapped_gelu = lambda inputs: torch.onnx.symbolic_opset9.gelu(g, inputs, "tanh")
 # TE computes GELU using float32 precision so wrap the GELU subgraph with
 # conversion to/from float32.
 gelu = compute_in_fp32(g, inputs, wrapped_gelu, cast_outp=False)
 gelu = torch.onnx.symbolic_opset9.gelu(g, inputs, "tanh")
 out = quantize(g, gelu, scale_inv, fp8_tensor)
 return out

Expand DownExpand Up@@ -200,20 +179,16 @@ def onnx_layernorm_fwd(g, inputs, weight, bias, eps, zero_centered_gamma):
 normalized_shape = normalized_shape[1:]

 if zero_centered_gamma:
 inputs_dtype= inputs.type().dtype()
 one = g.op("Constant", value_t=torch.tensor([1.], dtype=inputs_dtype, device="cuda"))
 one = g.op("Constant", value_t=torch.tensor([1], dtype=torch.int64, device="cuda"))
 weight = g.op("Add", weight, one)

 axis = -len(normalized_shape)
 ln = g.op(
 "LayerNormalization",
 ln = torch.onnx.symbolic_opset9.layer_norm(
 g,
 inputs,
 normalized_shape,
 weight,
 bias,
 epsilon_f=eps,
 axis_i=axis,
 # This sets the LN compute precision - use FP32 always as does TE.
 stash_type_i=_C_onnx.TensorProtoDataType.FLOAT,
 eps,
 False # cudnn_enable (not relevant)
 )
 return ln

Expand Down