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1011 lines
37 KiB
Python
1011 lines
37 KiB
Python
import math
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import paddle
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import paddle.nn.functional as F
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def fast_gelu(x):
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"""Mindspore's fast gelu implementation."""
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return x / (1 + paddle.exp(-1.702 * paddle.abs(x))) * paddle.exp(0.851 * (x - paddle.abs(x)))
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class MLP(paddle.nn.Layer):
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"""MLP.
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MLP will take the input with h hidden state, project it to 4*h
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hidden dimension, perform nonlinear transformation, and project the
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state back into h hidden dimension. At the end, dropout is also
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applied.
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"""
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def __init__(
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self,
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hidden_size,
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):
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super(MLP, self).__init__()
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self.hidden_size = hidden_size
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# Project to 4h.
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self.dense_h_to_4h = paddle.nn.Linear(
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self.hidden_size,
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4 * self.hidden_size,
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)
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self.activation_func = fast_gelu
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# Project back to h.
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self.dense_4h_to_h = paddle.nn.Linear(
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4 * self.hidden_size,
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self.hidden_size,
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)
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def forward(self, hidden_states):
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# [s, b, 4hp]
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intermediate_parallel = self.dense_h_to_4h(hidden_states)
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intermediate_parallel = self.activation_func(intermediate_parallel)
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# [s, b, h]
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output = self.dense_4h_to_h(intermediate_parallel)
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return output
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class SelfAttention(paddle.nn.Layer):
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"""self-attention layer abstract class.
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Self-attention layer takes input with size [b, s, h]
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and returns output of the same size.
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"""
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def __init__(
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self,
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hidden_size,
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num_attention_heads,
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layer_number,
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fp16=True,
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attention_softmax_in_fp32=True,
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):
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super(SelfAttention, self).__init__()
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self.hidden_size = hidden_size
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self.num_attention_heads = num_attention_heads
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self.fp16 = fp16
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self.attention_softmax_in_fp32 = attention_softmax_in_fp32
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self.layer_number = max(1, layer_number)
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assert self.hidden_size % self.num_attention_heads == 0
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self.hidden_size_per_attention_head = int(self.hidden_size // self.num_attention_heads)
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self.query = paddle.nn.Linear(self.hidden_size, self.hidden_size)
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self.key = paddle.nn.Linear(self.hidden_size, self.hidden_size)
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self.value = paddle.nn.Linear(self.hidden_size, self.hidden_size)
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self.norm_factor = math.sqrt(self.hidden_size_per_attention_head)
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self.softmax = paddle.nn.Softmax(axis=-1)
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self.dense = paddle.nn.Linear(self.hidden_size, self.hidden_size)
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def forward(
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self,
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hidden_states,
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attention_mask,
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layer_past=None,
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get_key_value=False,
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prompt_length=None,
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context_length=None,
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):
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# hidden_states: [sq, b, h]
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# =====================
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# Query, Key, and Value
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# =====================
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query_layer = self.query(hidden_states)
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key_layer = self.key(hidden_states)
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value_layer = self.value(hidden_states)
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new_query_layer_shape = query_layer.shape[:-1] + \
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[self.num_attention_heads,
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self.hidden_size_per_attention_head]
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query_layer = query_layer.reshape(new_query_layer_shape)
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new_query_layer_shape = key_layer.shape[:-1] + \
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[self.num_attention_heads,
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self.hidden_size_per_attention_head]
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key_layer = key_layer.reshape(new_query_layer_shape)
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new_query_layer_shape = value_layer.shape[:-1] + \
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[self.num_attention_heads,
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self.hidden_size_per_attention_head]
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value_layer = value_layer.reshape(new_query_layer_shape)
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# ==================================
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# Adjust key and value for inference
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# ==================================
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if layer_past is not None:
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past_key, past_value = layer_past
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key_layer = paddle.concat((past_key.cast(key_layer.dtype),
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key_layer), axis=0)
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value_layer = paddle.concat((past_value.cast(value_layer.dtype),
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value_layer), axis=0)
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if get_key_value:
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present = (key_layer, value_layer)
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# ===================================
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# Raw attention scores. [b, np, sq, sk]
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# ===================================
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# [b, np, sq, sk]
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output_size = (query_layer.shape[1],
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query_layer.shape[2],
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query_layer.shape[0],
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key_layer.shape[0])
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# [sq, b, np, hn] -> [sq, b * np, hn]
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query_layer = query_layer.reshape([output_size[2], output_size[0] * output_size[1], -1])
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key_layer = key_layer.reshape([output_size[3], output_size[0] * output_size[1], -1])
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# Raw attention scores. [b * np, sq, sk]
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matmul_result = paddle.matmul(query_layer.transpose([1, 0, 2]),
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key_layer.transpose([1, 0, 2]).transpose([0, 2, 1])) / self.norm_factor
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# change view to [b, np, sq, sk]
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attention_scores = matmul_result.reshape(output_size)
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# ==================================================
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# Update attention mask for inference. [b, np, sq, sk]
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# ==================================================
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if get_key_value:
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with paddle.no_grad():
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if layer_past is not None:
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attention_mask = attention_mask[
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...,
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attention_scores.shape[3] - 1,
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:attention_scores.shape[3]].unsqueeze(2)
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else:
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attention_mask = attention_mask[
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...,
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:attention_scores.shape[3],
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:attention_scores.shape[3]]
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if context_length is not None:
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attention_mask = paddle.clone(attention_mask)
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attention_mask[:, :, context_length:, :] = True
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# attention scores and attention mask [b, np, sq, sk]
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# attention_scores = attention_mask_func(attention_scores, attention_mask)
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attention_scores = attention_scores - attention_mask * 10000.0
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if self.attention_softmax_in_fp32:
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attention_probs = self.softmax(attention_scores.cast("float32")).cast("float16")
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else:
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attention_probs = self.softmax(attention_scores)
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# =========================
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# Context layer. [sq, b, hp]
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# =========================
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# value_layer -> context layer.
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# [sq, b, np, hn] --> [b, np, sq, hn]
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# context layer shape: [b, np, sq, hn]
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output_size = (value_layer.shape[1],
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value_layer.shape[2],
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query_layer.shape[0],
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value_layer.shape[3])
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# change view [sq, b * np, hn]
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value_layer = value_layer.reshape([value_layer.shape[0], output_size[0] * output_size[1], -1])
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# change view [b * np, sq, sk]
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attention_probs = attention_probs.reshape([output_size[0] * output_size[1],
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output_size[2], -1])
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context_layer = paddle.bmm(attention_probs, value_layer.unsqueeze(0).transpose([0, 2, 1, 3]).squeeze(0))
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# change view [b, np, sq, hn]
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context_layer = context_layer.reshape(output_size)
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# # [b, np, sq, hn] --> [sq, b, np, hn]
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context_layer = context_layer.transpose([2, 0, 1, 3])
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# # [sq, b, np, hn] --> [sq, b, hp]
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new_context_layer_shape = context_layer.shape[:-2] + \
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[self.hidden_size,]
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context_layer = context_layer.reshape(new_context_layer_shape)
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# =================
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# Output. [sq, b, h]
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# =================
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output = self.dense(context_layer)
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if get_key_value:
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output = [output, present]
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return output
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class TopQuerySelfAttention(paddle.nn.Layer):
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"""Top query self-attention layer abstract class.
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Self-attention layer takes input with size [b, s, h]
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and returns output of the same size.
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"""
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def __init__(
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self,
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hidden_size,
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num_attention_heads,
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layer_number,
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fp16=True,
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attention_softmax_in_fp32=True,
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):
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super(TopQuerySelfAttention, self).__init__()
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self.hidden_size = hidden_size
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self.num_attention_heads = num_attention_heads
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self.fp16 = fp16
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self.attention_softmax_in_fp32 = attention_softmax_in_fp32
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self.layer_number = max(1, layer_number)
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assert self.hidden_size % self.num_attention_heads == 0
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self.hidden_size_per_attention_head = int(self.hidden_size // self.num_attention_heads)
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self.query = paddle.nn.Linear(self.hidden_size, self.hidden_size)
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self.key = paddle.nn.Linear(self.hidden_size, self.hidden_size)
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self.value = paddle.nn.Linear(self.hidden_size, self.hidden_size)
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self.norm_factor = math.sqrt(self.hidden_size_per_attention_head)
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self.softmax = paddle.nn.Softmax(axis=-1)
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self.dense = paddle.nn.Linear(self.hidden_size, self.hidden_size)
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def forward(
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self,
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hidden_states,
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query_hidden_state,
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attention_mask,
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layer_past=None,
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get_key_value=False,
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prompt_length=None,
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context_length=None,
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):
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# hidden_states: [sq, b, h]
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query_layer = self.query(query_hidden_state)
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key_layer = self.key(hidden_states)
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value_layer = self.value(hidden_states)
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new_query_layer_shape = query_layer.shape[:-1] + \
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[self.num_attention_heads,
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self.hidden_size_per_attention_head]
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query_layer = query_layer.reshape(new_query_layer_shape)
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new_query_layer_shape = key_layer.shape[:-1] + \
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[self.num_attention_heads,
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self.hidden_size_per_attention_head]
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key_layer = key_layer.reshape(new_query_layer_shape)
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new_query_layer_shape = value_layer.shape[:-1] + \
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[self.num_attention_heads,
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self.hidden_size_per_attention_head]
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value_layer = value_layer.reshape(new_query_layer_shape)
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# ==================================
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# Adjust key and value for inference
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# ==================================
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if layer_past is not None:
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past_key, past_value = layer_past
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key_layer = paddle.concat((past_key.cast(key_layer.dtype),
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key_layer), axis=0)
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value_layer = paddle.concat((past_value.cast(value_layer.dtype),
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value_layer), axis=0)
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if get_key_value:
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present = (key_layer, value_layer)
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# ===================================
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# Raw attention scores. [b, np, sq, sk]
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# ===================================
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# [b, np, sq, sk]
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output_size = (query_layer.shape[1],
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query_layer.shape[2],
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query_layer.shape[0],
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key_layer.shape[0])
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# [s, b, np, hn] -> [s, b * np, hn]
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query_layer = query_layer.reshape([output_size[2], output_size[0] * output_size[1], -1])
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key_layer = key_layer.reshape([output_size[3], output_size[0] * output_size[1], -1])
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# Raw attention scores. [b * np, sq, sk]
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matmul_result = paddle.matmul(query_layer.transpose([1, 0, 2]),
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key_layer.transpose([1, 0, 2]).transpose([0, 2, 1])) / self.norm_factor
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# change view to [b, np, s, s]
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attention_scores = matmul_result.reshape(output_size)
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# ==================================================
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# Update attention mask for inference. [b, np, sq, sk]
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# ==================================================
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if get_key_value:
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with paddle.no_grad():
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if layer_past is not None:
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attention_mask = attention_mask[
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...,
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attention_scores.shape[3] - 1,
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:attention_scores.shape[3]].unsqueeze(2)
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else:
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attention_mask = attention_mask[
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...,
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:attention_scores.shape[3],
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:attention_scores.shape[3]]
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if context_length is not None:
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attention_mask = paddle.clone(attention_mask)
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attention_mask[:, :, context_length:, :] = True
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# attention scores and attention mask [b, np, sq, sk]
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# attention_scores = attention_mask_func(attention_scores, attention_mask)
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attention_scores = attention_scores - attention_mask * 10000.0
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if self.attention_softmax_in_fp32:
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attention_probs = self.softmax(attention_scores.cast("float32")).cast("float16")
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else:
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attention_probs = self.softmax(attention_scores)
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# =========================
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# Context layer. [sq, b, hp]
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# =========================
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# value_layer -> context layer.
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# [sq, b, np, hn] --> [b, np, sq, hn]
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# context layer shape: [b, np, sq, hn]
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output_size = (value_layer.shape[1],
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value_layer.shape[2],
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query_layer.shape[0],
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value_layer.shape[3])
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# change view [sq, b * np, hn]
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value_layer = value_layer.reshape([value_layer.shape[0], output_size[0] * output_size[1], -1])
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# change view [b * np, sq, sk]
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attention_probs = attention_probs.reshape([output_size[0] * output_size[1],
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output_size[2], -1])
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# matmul: [b * np, sq, hn]
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context_layer = paddle.bmm(attention_probs, value_layer.unsqueeze(0).transpose([0, 2, 1, 3]).squeeze(0))
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# change view [b, np, sq, hn]
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context_layer = context_layer.reshape(output_size)
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# [b, np, sq, hn] --> [sq, b, np, hn]
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context_layer = context_layer.transpose([2, 0, 1, 3])
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# [sq, b, np, hn] --> [sq, b, hp]
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new_context_layer_shape = context_layer.shape[:-2] + \
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[self.hidden_size,]
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context_layer = context_layer.reshape(new_context_layer_shape)
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# =================
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# Output. [sq, b, h]
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# =================
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output = self.dense(context_layer)
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if get_key_value:
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output = [output, present]
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return output
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|
|
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class TransformerLayer(paddle.nn.Layer):
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"""A single transformer layer.
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Transformore layer takes input with size [b, s, h] and returns an
|
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output of the same size.
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|
"""
|
|
|
|
def __init__(
|
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self,
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hidden_size,
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num_attention_heads,
|
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layer_number,
|
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layernorm_epsilon=1e-5,
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fp16=True,
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attention_softmax_in_fp32=True,
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):
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super(TransformerLayer, self).__init__()
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self.hidden_size = hidden_size
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self.layernorm_epsilon = layernorm_epsilon
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self.layer_number = layer_number
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|
|
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# Layernorm on the input data.
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self.input_layernorm = paddle.nn.LayerNorm(hidden_size,
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epsilon=self.layernorm_epsilon)
|
|
|
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# Self attention.
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self.attention = SelfAttention(hidden_size,
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num_attention_heads,
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layer_number,
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fp16,
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attention_softmax_in_fp32)
|
|
|
|
# Layernorm on the input data.
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self.post_attention_layernorm = paddle.nn.LayerNorm(self.hidden_size,
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epsilon=self.layernorm_epsilon)
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self.mlp = MLP(self.hidden_size)
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|
|
|
def forward(
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self,
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hidden_states,
|
|
attention_mask,
|
|
layer_past=None,
|
|
get_key_value=False,
|
|
prompt_length=None,
|
|
context_length=None,
|
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):
|
|
# hidden_states: [b, s, h]
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|
# Use FP32 for Layernorm
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|
# layernorm_output = self.input_layernorm(hidden_states.cast("float32")).cast("float16")
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layernorm_output = self.input_layernorm(hidden_states)
|
|
|
|
# Self attention.
|
|
attention_output = self.attention(layernorm_output,
|
|
attention_mask,
|
|
layer_past=layer_past,
|
|
get_key_value=get_key_value,
|
|
prompt_length=prompt_length,
|
|
context_length=context_length)
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|
|
|
if get_key_value:
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attention_output, presents = attention_output
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|
|
# Residual connection.
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|
residual = hidden_states
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layernorm_input = attention_output + residual
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|
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# Use FP32 for Layernorm
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# layernorm_output = self.post_attention_layernorm(layernorm_input.cast("float32")).cast("float16")
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layernorm_output = self.post_attention_layernorm(layernorm_input)
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mlp_output = self.mlp(layernorm_output)
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output = mlp_output + layernorm_input
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if get_key_value:
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output = [output, presents]
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return output
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|
|
|
|
class TopQueryLayer(paddle.nn.Layer):
|
|
"""A single top query layer.
|
|
|
|
Top query layer takes input with size [b, s, h] and returns an
|
|
output of the same size.
|
|
"""
|
|
|
|
def __init__(
|
|
self,
|
|
hidden_size,
|
|
num_attention_heads,
|
|
layer_number,
|
|
layernorm_epsilon=1e-5,
|
|
):
|
|
super(TopQueryLayer, self).__init__()
|
|
self.hidden_size = hidden_size
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|
self.num_attention_heads = num_attention_heads
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|
self.layernorm_epsilon = layernorm_epsilon
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|
self.layer_number = layer_number
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|
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# Use FP32 for Layernorm
|
|
self.input_layernorm = paddle.nn.LayerNorm(self.hidden_size,
|
|
epsilon=self.layernorm_epsilon)
|
|
|
|
# Self attention.
|
|
self.attention = TopQuerySelfAttention(self.hidden_size,
|
|
self.num_attention_heads,
|
|
self.layer_number)
|
|
# Layernorm on the input data.
|
|
self.post_attention_layernorm = paddle.nn.LayerNorm(self.hidden_size,
|
|
epsilon=self.layernorm_epsilon)
|
|
|
|
# MLP
|
|
self.mlp = MLP(self.hidden_size)
|
|
|
|
def forward(
|
|
self,
|
|
hidden_states,
|
|
query_hidden_state,
|
|
attention_mask,
|
|
layer_past=None,
|
|
get_key_value=False,
|
|
prompt_length=None,
|
|
context_length=None,
|
|
):
|
|
# hidden_states: [b, s, h]
|
|
# assert query_hidden_state != None
|
|
|
|
# Use FP32 for Layernorm
|
|
# layernorm_output = self.input_layernorm(hidden_states.cast("float32")).cast("float16")
|
|
layernorm_output = self.input_layernorm(hidden_states)
|
|
|
|
# Self attention.
|
|
attention_output = self.attention(layernorm_output,
|
|
query_hidden_state,
|
|
attention_mask,
|
|
layer_past=layer_past,
|
|
get_key_value=get_key_value,
|
|
prompt_length=prompt_length,
|
|
context_length=context_length)
|
|
|
|
if get_key_value:
|
|
attention_output, presents = attention_output
|
|
|
|
# Residual connection.
|
|
residual = hidden_states
|
|
layernorm_input = attention_output + residual
|
|
|
|
# Use FP32 for Layernorm
|
|
# layernorm_output = self.post_attention_layernorm(layernorm_input.cast("float32")).cast("float16")
|
|
layernorm_output = self.post_attention_layernorm(layernorm_input)
|
|
|
|
# MLP.
|
|
mlp_output = self.mlp(layernorm_output)
|
|
|
|
# Second residual connection.
|
|
residual = layernorm_input
|
|
output = mlp_output + residual
|
|
|
|
if get_key_value:
|
|
output = [output, presents]
|
|
|
|
return output
|
|
|
|
|
|
class Transformer(paddle.nn.Layer):
|
|
"""Transformer class."""
|
|
|
|
def __init__(
|
|
self,
|
|
hidden_size,
|
|
num_attention_heads,
|
|
num_layers,
|
|
layernorm_epsilon=1e-5,
|
|
):
|
|
super(Transformer, self).__init__()
|
|
self.hidden_size = hidden_size
|
|
self.num_attention_heads = num_attention_heads
|
|
self.layernorm_epsilon = layernorm_epsilon
|
|
# Number of layers:
|
|
self.num_layers = num_layers
|
|
self.num_unique_layers = None
|
|
|
|
#################
|
|
assert self.num_unique_layers is None
|
|
#################
|
|
|
|
if self.num_unique_layers is None:
|
|
self.num_unique_layers = self.num_layers
|
|
assert self.num_layers % self.num_unique_layers == 0, \
|
|
'number of layers should be divisible by number of unique layers'
|
|
|
|
# Transformer layers.
|
|
def build_layer(layer_number):
|
|
return TransformerLayer(self.hidden_size, self.num_attention_heads, layer_number)
|
|
|
|
self.layers = paddle.nn.LayerList(
|
|
[build_layer(i + 1) for i in range(self.num_unique_layers)])
|
|
|
|
self.topQueryLayer = TopQueryLayer(self.hidden_size,
|
|
self.num_attention_heads,
|
|
self.num_unique_layers)
|
|
|
|
self.final_layernorm = paddle.nn.LayerNorm(self.hidden_size,
|
|
epsilon=self.layernorm_epsilon)
|
|
|
|
def _get_layer_index(self, layer_number):
|
|
return layer_number % self.num_unique_layers
|
|
|
|
def _get_layer(self, layer_number):
|
|
return self.layers[self._get_layer_index(layer_number)]
|
|
|
|
def forward(
|
|
self,
|
|
hidden_states,
|
|
query_hidden_state,
|
|
attention_mask,
|
|
layer_past=None,
|
|
get_key_value=False,
|
|
prompt_length=None,
|
|
context_length=None,
|
|
):
|
|
# data format change to avoid explicit tranposes : [b s h] --> [s b h]
|
|
hidden_states = hidden_states.transpose([1, 0, 2])
|
|
query_hidden_state = query_hidden_state.transpose([1, 0, 2])
|
|
|
|
|
|
if get_key_value:
|
|
presents = []
|
|
for index in range(self.num_layers):
|
|
layer = self._get_layer(index)
|
|
past = None
|
|
if layer_past is not None:
|
|
past = layer_past[index]
|
|
hidden_states = layer(hidden_states,
|
|
attention_mask,
|
|
layer_past=past,
|
|
get_key_value=get_key_value,
|
|
prompt_length=prompt_length,
|
|
context_length=context_length)
|
|
if get_key_value:
|
|
hidden_states, present = hidden_states
|
|
presents.append(present)
|
|
|
|
# Use FP32 for Layernorm
|
|
# hidden_states_ = self.final_layernorm(hidden_states.cast("float32")).cast("float16")
|
|
hidden_states_ = self.final_layernorm(hidden_states)
|
|
|
|
#################################
|
|
# top query layer
|
|
#################################
|
|
past = None
|
|
if layer_past is not None:
|
|
past = layer_past[self.num_layers]
|
|
hidden_states = self.topQueryLayer(hidden_states_,
|
|
query_hidden_state,
|
|
attention_mask,
|
|
layer_past=past,
|
|
get_key_value=get_key_value,
|
|
prompt_length=prompt_length,
|
|
context_length=context_length)
|
|
|
|
if get_key_value:
|
|
hidden_states, present = hidden_states
|
|
presents.append(present)
|
|
|
|
# reverting data format change [s b h] --> [b s h]
|
|
output = hidden_states.transpose([1, 0, 2])
|
|
|
|
if get_key_value:
|
|
output = [output, presents]
|
|
|
|
return output
|
|
|
|
def state_dict_for_save_checkpoint(
|
|
self, destination=None, prefix="", keep_vars=False
|
|
):
|
|
return self.state_dict(destination, prefix, keep_vars)
|
|
|
|
|
|
class Embedding(paddle.nn.Layer):
|
|
"""Language model embeddings.
|
|
|
|
Arguments:
|
|
hidden_size: hidden size
|
|
vocab_size: vocabulary size
|
|
max_sequence_length: maximum size of sequence. This
|
|
is used for positional embedding
|
|
"""
|
|
|
|
def __init__(
|
|
self,
|
|
hidden_size,
|
|
vocab_size,
|
|
max_sequence_length,
|
|
):
|
|
super(Embedding, self).__init__()
|
|
self.hidden_size = hidden_size
|
|
self.vocab_size = vocab_size
|
|
self.max_sequence_length = max_sequence_length
|
|
|
|
# Word embeddings.
|
|
self.word_embeddings = paddle.nn.Embedding(self.vocab_size, self.hidden_size)
|
|
self._word_embeddings_key = 'word_embeddings'
|
|
|
|
# Position embedding.
|
|
self.position_embeddings = paddle.nn.Embedding(self.max_sequence_length, self.hidden_size)
|
|
self.position_embeddings = self.position_embeddings.to(dtype="float16")
|
|
self._position_embeddings_key = 'position_embeddings'
|
|
|
|
def forward(self, input_ids, position_ids):
|
|
# Embeddings.
|
|
words_embeddings = self.word_embeddings(input_ids)
|
|
position_embeddings = self.position_embeddings(position_ids)
|
|
embeddings = words_embeddings + position_embeddings
|
|
|
|
return embeddings
|
|
|
|
def state_dict_for_save_checkpoint(self, destination=None, prefix='',
|
|
keep_vars=False):
|
|
"""For easy load."""
|
|
|
|
state_dict_ = {}
|
|
state_dict_[self._word_embeddings_key] \
|
|
= self.word_embeddings.state_dict(destination, prefix, keep_vars)
|
|
state_dict_[self._position_embeddings_key] \
|
|
= self.position_embeddings.state_dict(
|
|
destination, prefix, keep_vars)
|
|
|
|
return state_dict_
|
|
|
|
def set_state_dict(self, state_dict, use_structured_name=True):
|
|
"""Customized load."""
|
|
|
|
# Word embedding.
|
|
if self._word_embeddings_key in state_dict:
|
|
state_dict_ = state_dict[self._word_embeddings_key]
|
|
else:
|
|
# for backward compatibility.
|
|
state_dict_ = {}
|
|
for key in state_dict.keys():
|
|
if 'word_embeddings' in key:
|
|
state_dict_[key.split('word_embeddings.')[1]] \
|
|
= state_dict[key]
|
|
state_dict_["weight"] = state_dict_["weight"][:self.vocab_size]
|
|
self.word_embeddings.set_state_dict(state_dict_, use_structured_name=use_structured_name)
|
|
|
|
# Position embedding.
|
|
if self._position_embeddings_key in state_dict:
|
|
state_dict_ = state_dict[self._position_embeddings_key]
|
|
else:
|
|
# for backward compatibility.
|
|
state_dict_ = {}
|
|
for key in state_dict.keys():
|
|
if 'position_embeddings' in key:
|
|
state_dict_[key.split('position_embeddings.')[1]] \
|
|
= state_dict[key]
|
|
self.position_embeddings.set_state_dict(state_dict_, use_structured_name=use_structured_name)
|
|
|
|
|
|
class QueryEmbedding(paddle.nn.Layer):
|
|
"""Language model embeddings.
|
|
|
|
Arguments:
|
|
hidden_size: hidden size
|
|
vocab_size: vocabulary size
|
|
max_sequence_length: maximum size of sequence. This
|
|
is used for positional embedding
|
|
"""
|
|
|
|
def __init__(
|
|
self,
|
|
hidden_size,
|
|
vocab_size,
|
|
max_sequence_length,
|
|
):
|
|
super(QueryEmbedding, self).__init__()
|
|
|
|
self.hidden_size = hidden_size
|
|
self.vocab_size = vocab_size
|
|
self.max_sequence_length = max_sequence_length
|
|
|
|
# Top query position embedding (serial).
|
|
self.top_query_embeddings = paddle.nn.Embedding(self.max_sequence_length, self.hidden_size)
|
|
self.top_query_embeddings = self.top_query_embeddings.to(dtype="float16")
|
|
self._top_query_embeddings_key = 'top_query_embeddings'
|
|
|
|
def forward(self, position_ids):
|
|
# Embeddings.
|
|
embeddings = self.top_query_embeddings(position_ids)
|
|
|
|
return embeddings
|
|
|
|
def state_dict_for_save_checkpoint(self, destination=None, prefix='',
|
|
keep_vars=False):
|
|
"""For easy load."""
|
|
|
|
state_dict_ = {}
|
|
state_dict_[self._top_query_embeddings_key] \
|
|
= self.top_query_embeddings.state_dict(
|
|
destination, prefix, keep_vars)
|
|
|
|
return state_dict_
|
|
|
|
def set_state_dict(self, state_dict, use_structured_name=True):
|
|
"""Customized load."""
|
|
|
|
# Position embedding.
|
|
if self._top_query_embeddings_key in state_dict:
|
|
state_dict_ = state_dict[self._top_query_embeddings_key]
|
|
else:
|
|
# for backward compatibility.
|
|
state_dict_ = {}
|
|
for key in state_dict.keys():
|
|
if 'top_query_embeddings' in key:
|
|
state_dict_[key.split('top_query_embeddings.')[1]] \
|
|
= state_dict[key]
|
|
self.top_query_embeddings.set_state_dict(state_dict_, use_structured_name=use_structured_name)
|
|
|
|
|
|
class TransformerLanguageModel(paddle.nn.Layer):
|
|
"""Transformer language model.
|
|
|
|
Arguments:
|
|
transformer_hparams: transformer hyperparameters
|
|
attention_mask_func: a function that takes `unmaksed-attention-scores`
|
|
with size [b, np, s, s] and an `attention-mask` and will apply
|
|
the masking. The function should return a masked score of the
|
|
same size [b, np, s, s].
|
|
masked-attention-scores = attention_mask_func(
|
|
unmaksed-attention-scores, attention-mask)
|
|
vocab_size: vocabulary size
|
|
max_sequence_length: maximum size of sequence. This
|
|
is used for positional embedding
|
|
"""
|
|
|
|
def __init__(
|
|
self,
|
|
hidden_size,
|
|
num_layers,
|
|
num_attention_heads,
|
|
padded_vocab_size,
|
|
max_position_embeddings,
|
|
):
|
|
super(TransformerLanguageModel, self).__init__()
|
|
self.hidden_size = hidden_size
|
|
self.num_layers = num_layers
|
|
self.num_attention_heads = num_attention_heads
|
|
self.padded_vocab_size = padded_vocab_size
|
|
self.max_position_embeddings = max_position_embeddings
|
|
|
|
# Embeddings
|
|
self.embedding = Embedding(self.hidden_size,
|
|
self.padded_vocab_size,
|
|
self.max_position_embeddings)
|
|
self._embedding_key = 'embedding'
|
|
|
|
# Query embeddings
|
|
self.topQueryEmbedding = QueryEmbedding(self.hidden_size,
|
|
self.padded_vocab_size,
|
|
self.max_position_embeddings)
|
|
self._topQueryEmbedding_key = 'topQueryEmbedding'
|
|
|
|
# Transformer
|
|
self.transformer = Transformer(self.hidden_size,
|
|
self.num_attention_heads,
|
|
self.num_layers)
|
|
self._transformer_key = 'transformer'
|
|
|
|
def forward(
|
|
self,
|
|
input_ids,
|
|
position_ids,
|
|
attention_mask,
|
|
layer_past=None,
|
|
get_key_value=False,
|
|
prompt_length=None,
|
|
context_length=None,
|
|
):
|
|
|
|
# Embeddings.
|
|
embedding_output = self.embedding(input_ids, position_ids)
|
|
query_position_ids = position_ids
|
|
queryEmbedding_out = self.topQueryEmbedding(query_position_ids)
|
|
|
|
# Transformer.
|
|
transformer_output = self.transformer(embedding_output,
|
|
queryEmbedding_out,
|
|
attention_mask,
|
|
layer_past=layer_past,
|
|
get_key_value=get_key_value,
|
|
prompt_length=prompt_length,
|
|
context_length=context_length)
|
|
|
|
return transformer_output
|
|
|
|
def state_dict_for_save_checkpoint(self, destination=None, prefix='',
|
|
keep_vars=False):
|
|
"""For easy load."""
|
|
|
|
state_dict_ = {}
|
|
state_dict_[self._embedding_key] \
|
|
= self.embedding.state_dict_for_save_checkpoint(
|
|
destination, prefix, keep_vars)
|
|
state_dict_[self._topQueryEmbedding_key] \
|
|
= self.topQueryEmbedding.state_dict_for_save_checkpoint(
|
|
destination, prefix, keep_vars)
|
|
state_dict_[self._transformer_key] \
|
|
= self.transformer.state_dict_for_save_checkpoint(
|
|
destination, prefix, keep_vars)
|
|
|
|
return state_dict_
|
|
|
|
def set_state_dict(self, state_dict, use_structured_name=True):
|
|
"""Customized load."""
|
|
|
|
# Embedding.
|
|
if self._embedding_key in state_dict:
|
|
state_dict_ = state_dict[self._embedding_key]
|
|
else:
|
|
# for backward compatibility.
|
|
state_dict_ = {}
|
|
for key in state_dict.keys():
|
|
if '_embeddings' in key:
|
|
state_dict_[key] = state_dict[key]
|
|
self.embedding.set_state_dict(state_dict_, use_structured_name=use_structured_name)
|
|
|
|
if self._topQueryEmbedding_key in state_dict:
|
|
state_dict_ = state_dict[self._topQueryEmbedding_key]
|
|
else:
|
|
# for backward compatibility.
|
|
state_dict_ = {}
|
|
for key in state_dict.keys():
|
|
if '_embeddings' in key:
|
|
state_dict_[key] = state_dict[key]
|
|
self.topQueryEmbedding.set_state_dict(state_dict_, use_structured_name=use_structured_name)
|
|
|
|
# Transformer.
|
|
if self._transformer_key in state_dict:
|
|
state_dict_ = state_dict[self._transformer_key]
|
|
else:
|
|
# for backward compatibility.
|
|
state_dict_ = {}
|
|
for key in state_dict.keys():
|
|
if 'transformer.' in key:
|
|
state_dict_[key.split('transformer.')[1]] = state_dict[key]
|
|
self.transformer.set_state_dict(state_dict_, use_structured_name=use_structured_name)
|
|
|
|
|
|
class CodeGeeXModel(paddle.nn.Layer):
|
|
"""CodeGeeX: A Multilingual Code Generation Model."""
|
|
|
|
def __init__(
|
|
self,
|
|
hidden_size,
|
|
num_layers,
|
|
num_attention_heads,
|
|
padded_vocab_size,
|
|
max_position_embeddings,
|
|
):
|
|
super(CodeGeeXModel, self).__init__()
|
|
|
|
self.language_model = TransformerLanguageModel(hidden_size,
|
|
num_layers,
|
|
num_attention_heads,
|
|
padded_vocab_size,
|
|
max_position_embeddings)
|
|
self._language_model_key = "language_model"
|
|
|
|
def forward(
|
|
self,
|
|
input_ids,
|
|
position_ids,
|
|
attention_mask,
|
|
layer_past=None,
|
|
get_key_value=False,
|
|
prompt_length=None,
|
|
context_length=None,
|
|
):
|
|
# Language model.
|
|
lm_output = self.language_model(input_ids,
|
|
position_ids,
|
|
attention_mask,
|
|
layer_past=layer_past,
|
|
get_key_value=get_key_value,
|
|
prompt_length=prompt_length,
|
|
context_length=context_length)
|
|
|
|
if get_key_value:
|
|
lm_output, presents = lm_output
|
|
|
|
output = F.linear(lm_output, self.language_model.embedding.word_embeddings.weight.cast("float16").transpose([1, 0]))
|
|
|
|
if get_key_value:
|
|
output = [output, presents]
|
|
|
|
return output
|
|
|
|
def state_dict_for_save_checkpoint(self, destination=None, prefix='',
|
|
keep_vars=False):
|
|
|
|
state_dict_ = {}
|
|
state_dict_[self._language_model_key] \
|
|
= self.language_model.state_dict_for_save_checkpoint(
|
|
destination, prefix, keep_vars)
|
|
return state_dict_
|
|
|
|
def set_state_dict(self, state_dict, use_structured_name=True):
|
|
"""Customized load."""
|
|
|
|
if self._language_model_key in state_dict:
|
|
state_dict = state_dict[self._language_model_key]
|
|
self.language_model.set_state_dict(state_dict, use_structured_name=use_structured_name)
|