Model parallel documentation (#8741)
* Add parallelize methods to the .rst files * Correct format
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@@ -71,14 +71,14 @@ GPT2Model
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.GPT2Model
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:members: forward
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:members: forward, parallelize, deparallelize
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GPT2LMHeadModel
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.GPT2LMHeadModel
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:members: forward
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:members: forward, parallelize, deparallelize
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GPT2DoubleHeadsModel
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@@ -99,14 +99,14 @@ T5Model
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.T5Model
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:members: forward
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:members: forward, parallelize, deparallelize
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T5ForConditionalGeneration
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~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~~
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.. autoclass:: transformers.T5ForConditionalGeneration
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:members: forward
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:members: forward, parallelize, deparallelize
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TFT5Model
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@@ -492,7 +492,8 @@ PARALLELIZE_DOCSTRING = r"""
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- gpt2-xl: 48
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Example::
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Here is an example of a device map on a machine with 4 GPUs using gpt2-xl, which has a total of 48 attention modules:
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# Here is an example of a device map on a machine with 4 GPUs using gpt2-xl, which has a total of 48 attention modules:
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model = GPT2LMHeadModel.from_pretrained('gpt2-xl')
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device_map = {0: [0, 1, 2, 3, 4, 5, 6, 7, 8],
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@@ -505,7 +506,8 @@ DEPARALLELIZE_DOCSTRING = r"""
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Moves the model to cpu from a model parallel state.
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Example::
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On a 4 GPU machine with gpt2-large:
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# On a 4 GPU machine with gpt2-large:
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model = GPT2LMHeadModel.from_pretrained('gpt2-large')
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device_map = {0: [0, 1, 2, 3, 4, 5, 6, 7],
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@@ -196,7 +196,8 @@ PARALLELIZE_DOCSTRING = r"""
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- t5-11b: 24
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Example::
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Here is an example of a device map on a machine with 4 GPUs using t5-3b, which has a total of 24 attention modules:
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# Here is an example of a device map on a machine with 4 GPUs using t5-3b, which has a total of 24 attention modules:
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model = T5ForConditionalGeneration.from_pretrained('t5-3b')
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device_map = {0: [0, 1, 2],
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@@ -209,7 +210,8 @@ DEPARALLELIZE_DOCSTRING = r"""
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Moves the model to cpu from a model parallel state.
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Example::
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On a 4 GPU machine with t5-3b:
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# On a 4 GPU machine with t5-3b:
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model = T5ForConditionalGeneration.from_pretrained('t5-3b')
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device_map = {0: [0, 1, 2],
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