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fusion_gpt_attention.py
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fusion_gpt_attention.py
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#-------------------------------------------------------------------------
# Copyright (c) Microsoft Corporation. All rights reserved.
# Licensed under the MIT License.
#--------------------------------------------------------------------------
import numpy as np
from logging import getLogger
from onnx import helper, numpy_helper, TensorProto
from onnx_model import OnnxModel
from fusion_base import Fusion
from fusion_utils import FusionUtils
logger = getLogger(__name__)
class FusionGptAttentionPastBase(Fusion):
"""Base class for GPT Attention Fusion with past state
"""
def __init__(self, model: OnnxModel, num_heads: int):
super().__init__(model, "Attention", "LayerNormalization", "with past")
self.num_heads = num_heads
self.utils = FusionUtils(model)
self.casted_attention_mask = {} # map from name of attention mask to the name that casted to int32
def match_past_pattern_1(self, concat_k, concat_v, output_name_to_node):
# Pattern 1:
# {past}
# / \
# / \
# Gather(axes=0, indices=0) Gather(indices=1)
# | |
# Transpose (perm=0,1,3,2) |
# | |
# Concat_k Concat_v
# | /
# Transpose (perm=0,1,3,2) /
# | /
# Unsqueeze Unsqueeze
# \ /
# \ /
# Concat
# |
# {present}
gather = self.model.get_parent(concat_v, 0, output_name_to_node)
if gather.op_type != 'Gather':
logger.debug("match_past_pattern_1: expect Gather for past")
return None
if not self.model.find_constant_input(gather, 1) == 1:
logger.debug("match_past_pattern_1: expect indices=1 for Gather of past")
return None
past = gather.input[0]
past_k_nodes = self.model.match_parent_path(concat_k, ['Transpose', 'Gather'], [0, 0])
if past_k_nodes is None:
logger.debug("match_past_pattern_1: failed match Transpose and Gather")
return None
gather_past_k = past_k_nodes[-1]
if not self.model.find_constant_input(gather_past_k, 0) == 1:
logger.debug("match_past_pattern_1: expect indices=0 for Gather k of past")
return None
past_k = gather_past_k.input[0]
if past != past_k:
logger.debug("match_past_pattern_1: expect past to be same")
return None
return past
def match_past_pattern_2(self, concat_k, concat_v, output_name_to_node):
# Pattern 2:
# Split (QKV)
# / | |
# / | +----------------------+
# | |
# | {past} |
# | | |
# Reshape Split Reshape
# | / \ |
# Transpose_k Squeeze Squeeze Transpose_v
# | | \ /
# +------|---+ \ /
# | | \ /
# Concat_k Concat_v
# | |
# Unsqueeze Unsqueeze
# \ /
# Concat
# |
# {present}
#
squeeze = self.model.get_parent(concat_v, 0, output_name_to_node)
if squeeze.op_type != 'Squeeze':
logger.debug("match_past_pattern_2: expect Squeeze as parent of concat_v")
return None
split = self.model.get_parent(squeeze, 0, output_name_to_node)
if split.op_type != "Split":
logger.debug("match_past_pattern_2: expect Split for past path")
return None
opset_version = self.model.get_opset_version()
if opset_version < 13:
if not FusionUtils.check_node_attribute(squeeze, 'axes', [0]):
logger.debug("match_past_pattern_2: axes != [0] for Squeeze in past path")
return None
if not FusionUtils.check_node_attribute(split, 'split', [1, 1]):
logger.debug("match_past_pattern_2: split != [1, 1] for Split in past path")
return None
else:
if not self.utils.check_node_input_value(squeeze, 1, [0]):
logger.debug("match_past_pattern_2: axes != [0] for Squeeze in past path")
return None
if not self.utils.check_node_input_value(split, 1, [1, 1]):
logger.debug("match_past_pattern_2: split != [1, 1] for Split in past path")
return None
if not FusionUtils.check_node_attribute(split, 'axis', 0, default_value=0):
logger.debug("match_past_pattern_2: attribute axis of Split are not expected in past path")
return None
past = split.input[0]
past_k_nodes = self.model.match_parent_path(concat_k, ['Squeeze', 'Split'], [0, 0])
if past_k_nodes is None:
logger.debug("match_past_pattern_2: failed to match past_k_nodes path")
return None
past_k = past_k_nodes[-1].input[0]
if past != past_k:
logger.info("match_past_pattern_2: expect past to be same")
return None
return past
def match_present(self, concat_v, input_name_to_nodes):
unsqueeze_present_v = self.model.find_first_child_by_type(concat_v,
'Unsqueeze',
input_name_to_nodes,
recursive=False)
if not unsqueeze_present_v:
logger.info("expect unsqueeze for present")
return None
concat_present = self.model.find_first_child_by_type(unsqueeze_present_v,
'Concat',
input_name_to_nodes,
recursive=False)
if not concat_present:
logger.info("expect concat for present")
return None
present = concat_present.output[0]
return present
def cast_attention_mask(self, input_name):
if input_name in self.casted_attention_mask:
attention_mask_input_name = self.casted_attention_mask[input_name]
elif self.model.find_graph_input(input_name):
casted, attention_mask_input_name = self.utils.cast_graph_input_to_int32(input_name)
self.casted_attention_mask[input_name] = attention_mask_input_name
else:
attention_mask_input_name, cast_node = self.utils.cast_input_to_int32(input_name)
self.casted_attention_mask[input_name] = attention_mask_input_name
return attention_mask_input_name
class FusionGptAttention(FusionGptAttentionPastBase):
"""
Fuse GPT-2 Attention with past state subgraph into one Attention node.
"""
def __init__(self, model: OnnxModel, num_heads: int):
super().__init__(model, num_heads)
def create_attention_node(self, fc_weight, fc_bias, gemm_qkv, past, present, input, output, mask,
is_unidirectional):
attention_node_name = self.model.create_node_name('GptAttention')
attention_node = helper.make_node('Attention',
inputs=[input, fc_weight, fc_bias, mask, past],
outputs=[attention_node_name + "_output", present],
name=attention_node_name)
attention_node.domain = "com.microsoft"
attention_node.attribute.extend([
helper.make_attribute("num_heads", self.num_heads),
helper.make_attribute("unidirectional", 1 if is_unidirectional else 0)
])
matmul_node = helper.make_node('MatMul',
inputs=[attention_node_name + "_output", gemm_qkv.input[1]],
outputs=[attention_node_name + "_matmul_output"],
name=attention_node_name + "_matmul")
add_node = helper.make_node('Add',
inputs=[attention_node_name + "_matmul_output", gemm_qkv.input[2]],
outputs=[output],
name=attention_node_name + "_add")
self.nodes_to_add.extend([attention_node, matmul_node, add_node])
self.node_name_to_graph_name[attention_node.name] = self.this_graph_name
self.node_name_to_graph_name[matmul_node.name] = self.this_graph_name
self.node_name_to_graph_name[add_node.name] = self.this_graph_name
def fuse(self, normalize_node, input_name_to_nodes, output_name_to_node):
past = None
present = None
return_indice = []
qkv_nodes = self.model.match_parent_path(
normalize_node,
['Add', 'Reshape', 'Gemm', 'Reshape', 'Reshape', 'Transpose', 'MatMul'],
[0, None, 0, 0, 0, 0, 0],
output_name_to_node=output_name_to_node,
return_indice=return_indice
) # yapf: disable
if qkv_nodes is None:
return
(add_qkv, reshape_qkv, gemm_qkv, reshape_1, reshape_2, transpose_qkv, matmul_qkv) = qkv_nodes
another_input = add_qkv.input[1 - return_indice[0]]
v_nodes = self.model.match_parent_path(matmul_qkv, ['Concat', 'Transpose', 'Reshape', 'Split'], [1, 1, 0, 0])
if v_nodes is None:
logger.debug("fuse_attention: failed to match v path")
return
(concat_v, transpose_v, reshape_v, split_fc) = v_nodes
fc_nodes = self.model.match_parent_path(split_fc, ['Reshape', 'Gemm', 'Reshape', 'LayerNormalization'],
[0, 0, 0, 0], output_name_to_node)
if fc_nodes is None:
fc_nodes = self.model.match_parent_path(split_fc, ['Add', 'MatMul', 'LayerNormalization'], [0, None, 0],
output_name_to_node)
if fc_nodes is None:
logger.debug("fuse_attention: failed to match fc path")
return
fc_weight = fc_nodes[1].input[1]
i, _ = self.model.get_constant_input(fc_nodes[0])
fc_bias = fc_nodes[0].input[i]
else:
fc_weight = fc_nodes[1].input[1]
fc_bias = fc_nodes[1].input[2]
layernorm_before_attention = fc_nodes[-1]
if not another_input in layernorm_before_attention.input:
logger.debug("Add and LayerNormalization shall have one same input")
return
is_unidirectional = True
slice_mask = None
input_mask_nodes = None
concat_k_to_match = None
qk_nodes = self.model.match_parent_path(matmul_qkv, ['Softmax', 'Sub', 'Mul', 'Div', 'MatMul'], [0, 0, 0, 0, 0])
if qk_nodes is not None:
(softmax_qk, sub_qk, mul_qk, div_qk, matmul_qk) = qk_nodes
mask_nodes = self.model.match_parent_path(
sub_qk,
['Mul', 'Sub', 'Slice', 'Slice', 'Unsqueeze', 'Sub', 'Squeeze', 'Slice', 'Shape', 'Div'],
[1, 0, 1, 0, 1, 0, 0, 0, 0, 0]) # yapf: disable
if mask_nodes is None:
logger.debug("fuse_attention: failed to match unidirectional mask path")
return
div_mask = mask_nodes[-1]
slice_mask = mask_nodes[3]
if div_qk != div_mask:
logger.debug("fuse_attention: skip since div_qk != div_mask")
return
else:
# New pattern for gpt2 from PyTorch 1.5.0 and Transformers 2.9.0.
i, qk_nodes, _ = self.model.match_parent_paths(
matmul_qkv, [(['Softmax', 'Where', 'Div', 'MatMul'], [0, 0, 1, 0]),
(['Softmax', 'Add', 'Where', 'Div', 'MatMul'], [0, 0, None, 1, 0])], output_name_to_node)
if qk_nodes is None:
logger.debug("fuse_attention: failed to match qk nodes")
return
where_qk = qk_nodes[-3]
div_qk = qk_nodes[-2]
matmul_qk = qk_nodes[-1]
if i == 1:
add_qk = qk_nodes[1]
_, input_mask_nodes, _ = self.model.match_parent_paths(
add_qk,
[
(['Mul', 'Sub', 'Cast', 'Unsqueeze', 'Unsqueeze', 'Reshape'], [None, 0, 1, 0, 0, 0]),
(['Mul', 'Sub', 'Unsqueeze', 'Unsqueeze', 'Reshape'], [None, 0, 1, 0, 0]),
(['Mul', 'Sub', 'Unsqueeze', 'Unsqueeze'], [None, 0, 1, 0]), # useless cast and reshape are removed.
],
output_name_to_node) # yapf: disable
if input_mask_nodes is None:
logger.debug("fuse_attention: failed to match input attention mask path")
return
mask_nodes = self.model.match_parent_path(
where_qk,
['Cast', 'Slice', 'Slice', 'Unsqueeze', 'Sub', 'Squeeze', 'Slice', 'Shape'],
[ 0, 0, 0, 1, 0, 0, 0, 0],
output_name_to_node) # yapf: disable
if mask_nodes is None:
# TODO: match mask path for GPT2LMHeadModel_BeamSearchStep.
logger.debug("fuse_attention: failed to match mask path")
return
slice_mask = mask_nodes[2]
div_or_concat = self.model.get_parent(mask_nodes[-1], 0, output_name_to_node)
if div_or_concat.op_type == "Div":
div_mask = div_or_concat
if div_qk != div_mask:
logger.debug("fuse_attention: skip since div_qk != div_mask")
return
elif div_or_concat.op_type == "Concat":
concat_k_to_match = div_or_concat
else:
logger.debug("fuse_attention: failed to match mask path")
# Validate that the mask data is either lower triangular (unidirectional) or all ones
mask_data = numpy_helper.to_array(self.model.get_initializer(slice_mask.input[0]))
if not (len(mask_data.shape) == 4 and mask_data.shape[:2] == (1, 1)
and mask_data.shape[2] == mask_data.shape[3]):
logger.debug("fuse_attention: skip since mask shape is not 1x1xWxW")
return
if np.allclose(mask_data, np.ones_like(mask_data)):
is_unidirectional = False
elif not np.allclose(mask_data, np.tril(np.ones_like(mask_data))):
logger.debug("fuse_attention: skip since mask is neither lower triangular nor ones")
return
q_nodes = self.model.match_parent_path(matmul_qk, ['Transpose', 'Reshape', 'Split'], [0, 0, 0])
if q_nodes is None:
logger.debug("fuse_attention: failed to match q path")
return
(transpose_q, reshape_q, split_q) = q_nodes
if split_fc != split_q:
logger.debug("fuse_attention: skip since split_fc != split_q")
return
k_nodes = self.model.match_parent_path(matmul_qk, ['Concat', 'Transpose', 'Reshape', 'Split'], [1, 1, 0, 0])
if k_nodes is None:
# This pattern is from pytorch 1.7.1 and transformers 4.6.1
k_nodes = self.model.match_parent_path(matmul_qk, ['Transpose', 'Concat', 'Transpose', 'Reshape', 'Split'],
[1, 0, 1, 0, 0])
if k_nodes is None:
logger.debug("fuse_attention: failed to match k path")
return
else:
(_, concat_k, transpose_k, reshape_k, split_k) = k_nodes
else:
(concat_k, transpose_k, reshape_k, split_k) = k_nodes
if split_fc != split_k:
logger.debug("fuse_attention: skip since split_fc != split_k")
return
if concat_k_to_match and concat_k != concat_k_to_match:
logger.debug("fuse_attention: skip since concat_k != concat_k_to_match")
return
attention_mask_input_name = ''
if input_mask_nodes is not None:
input_name = input_mask_nodes[-1].input[0]
attention_mask_input_name = self.cast_attention_mask(input_name)
# Match past and present paths
past = self.match_past_pattern_1(concat_k, concat_v, output_name_to_node) or \
self.match_past_pattern_2(concat_k, concat_v, output_name_to_node)
if past is None:
logger.info("fuse_attention: failed to match past path")
return
if not self.model.find_graph_input(past):
logger.debug("past is not graph input.")
# For GPT2LMHeadModel_BeamSearchStep, there is an extra Gather node to select beam index so it is not graph input.
present = self.match_present(concat_v, input_name_to_nodes)
if present is None:
logger.info("fuse_attention: failed to match present path")
return
if not self.model.find_graph_output(present):
logger.info("expect present to be graph output")
return
self.create_attention_node(fc_weight, fc_bias, gemm_qkv, past, present, layernorm_before_attention.output[0],
reshape_qkv.output[0], attention_mask_input_name, is_unidirectional)
# we rely on prune_graph() to clean old subgraph nodes:
# qk_nodes + q_nodes + k_nodes + v_nodes + mask_nodes + [reshape_qkv, transpose_qkv, matmul_qkv]
self.prune_graph = True