[tutorial] edited hands-on practices (#1899)

* Add handson to ColossalAI.

* Change names of handsons and edit sequence parallel example.

* Edit wrong folder name

* resolve conflict

* delete readme

examples/tutorial/sequence_parallel/model/layers/__init__.py

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+from .embedding import VocabEmbedding, Embedding
+from .bert_layer import BertLayer
+from .head import BertDualHead
+from .preprocess import PreProcessor

examples/tutorial/sequence_parallel/model/layers/bert_layer.py

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+import torch
+import torch.nn as nn
+from colossalai.nn.layer.parallel_sequence import TransformerSelfAttentionRing
+from colossalai.kernel.jit import bias_dropout_add_fused_train, bias_dropout_add_fused_inference
+from colossalai.kernel.cuda_native import LayerNorm
+from .mlp import TransformerMLP
+from .dropout import get_bias_dropout_add
+
+
+def attention_mask_func(attention_scores, attention_mask):
+    attention_scores.masked_fill_(attention_mask, -10000.0)
+    return attention_scores
+
+
+class BertLayer(nn.Module):
+    """A single transformer layer.
+    Transformer layer takes input with size [b, s, h] and returns an
+    output of the same size.
+    """
+
+    def __init__(self,
+                 layer_number,
+                 hidden_size,
+                 num_attention_heads,
+                 attention_dropout,
+                 mlp_ratio,
+                 hidden_dropout,
+                 is_naive_fp16,
+                 apply_residual_connection_post_layernorm=False,
+                 fp32_residual_connection=False,
+                 bias_dropout_fusion: bool = True,
+                 convert_fp16_to_fp32_in_softmax: bool = False):
+        super().__init__()
+        self.layer_number = layer_number
+
+        self.apply_residual_connection_post_layernorm = apply_residual_connection_post_layernorm
+        self.fp32_residual_connection = fp32_residual_connection
+
+        # Layernorm on the input data.
+        self.input_layernorm = LayerNorm(hidden_size)
+
+        # Self attention.
+        self.self_attention = TransformerSelfAttentionRing(
+            hidden_size=hidden_size,
+            num_attention_heads=num_attention_heads,
+            attention_dropout=attention_dropout,
+            attention_mask_func=attention_mask_func,
+            layer_number=layer_number,
+            apply_query_key_layer_scaling=True,
+            convert_fp16_to_fp32_in_softmax=convert_fp16_to_fp32_in_softmax,
+            fp16=is_naive_fp16
+        )
+
+        self.hidden_dropout = hidden_dropout
+        self.bias_dropout_fusion = bias_dropout_fusion
+
+        # Layernorm on the attention output
+        self.post_attention_layernorm = LayerNorm(hidden_size)
+
+        self.mlp = TransformerMLP(hidden_size=hidden_size, mlp_ratio=mlp_ratio)
+
+    def forward(self, hidden_states, attention_mask):
+        # hidden_states: [batch_size, sub_seq_len, hidden_size]
+        # attention_mask: [batch_size, 1, sub_seq_len, seq_len]
+
+        # Layer norm at the beginning of the transformer layer.
+        layernorm_output = self.input_layernorm(hidden_states)
+
+        # Self attention.
+        attention_output, attention_bias = self.self_attention(layernorm_output, attention_mask)
+
+        # Residual connection.
+        if self.apply_residual_connection_post_layernorm:
+            residual = layernorm_output
+        else:
+            residual = hidden_states
+
+        # jit scripting for a nn.module (with dropout) is not
+        # trigerring the fusion kernel. For now, we use two
+        # different nn.functional routines to account for varying
+        # dropout semantics during training and inference phases.
+        if self.bias_dropout_fusion:
+            if self.training:
+                bias_dropout_add_func = bias_dropout_add_fused_train
+            else:
+                bias_dropout_add_func = bias_dropout_add_fused_inference
+        else:
+            bias_dropout_add_func = get_bias_dropout_add(self.training)
+
+        # re-enable torch grad to enable fused optimization.
+        with torch.enable_grad():
+            layernorm_input = bias_dropout_add_func(
+                attention_output,
+                attention_bias.expand_as(residual),
+                residual,
+                self.hidden_dropout)
+
+        # Layer norm post the self attention.
+        layernorm_output = self.post_attention_layernorm(layernorm_input)
+
+        # MLP.
+        mlp_output, mlp_bias = self.mlp(layernorm_output)
+
+        # Second residual connection.
+        if self.apply_residual_connection_post_layernorm:
+            residual = layernorm_output
+        else:
+            residual = layernorm_input
+
+        # re-enable torch grad to enable fused optimization.
+        with torch.enable_grad():
+            output = bias_dropout_add_func(
+                mlp_output,
+                mlp_bias.expand_as(residual),
+                residual,
+                self.hidden_dropout)
+
+        return output

examples/tutorial/sequence_parallel/model/layers/dropout.py

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+import torch
+
+def bias_dropout_add(x, bias, residual, prob, training):
+    # type: (Tensor, Tensor, Tensor, float, bool) -> Tensor
+    out = torch.nn.functional.dropout(x + bias, p=prob, training=training)
+    out = residual + out
+    return out
+
+
+def get_bias_dropout_add(training):
+    def _bias_dropout_add(x, bias, residual, prob):
+        return bias_dropout_add(x, bias, residual, prob, training)
+    return _bias_dropout_add

examples/tutorial/sequence_parallel/model/layers/embedding.py

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+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+import torch.nn.init as init
+
+
+class VocabEmbedding(torch.nn.Module):
+
+    def __init__(self, num_embeddings, embedding_dim):
+        super(VocabEmbedding, self).__init__()
+        # Keep the input dimensions.
+        self.num_embeddings = num_embeddings
+        self.embedding_dim = embedding_dim
+        self.padding_idx = None
+        self.max_norm = None
+        self.norm_type = 2.
+        self.scale_grad_by_freq = False
+        self.sparse = False
+        self._weight = None
+
+        # Allocate weights and initialize.
+        self.weight = nn.Parameter(torch.empty(
+            self.num_embeddings, self.embedding_dim))
+        init.xavier_uniform_(self.weight)
+
+    def forward(self, hidden_state):
+        output = F.embedding(hidden_state, self.weight,
+                             self.padding_idx, self.max_norm,
+                             self.norm_type, self.scale_grad_by_freq,
+                             self.sparse)
+        return output
+
+    def __repr__(self):
+        return f'VocabEmbedding(num_embeddings={self.num_embeddings}, ' \
+            f'embedding_dim={self.embedding_dim})'
+
+
+class Embedding(nn.Module):
+    """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
+        embedding_dropout_prob: dropout probability for embeddings
+        init_method: weight initialization method
+        num_tokentypes: size of the token-type embeddings. 0 value
+                        will ignore this embedding
+    """
+
+    def __init__(self,
+                 hidden_size,
+                 vocab_size,
+                 max_sequence_length,
+                 embedding_dropout_prob,
+                 num_tokentypes):
+        super(Embedding, self).__init__()
+
+        self.hidden_size = hidden_size
+        self.num_tokentypes = num_tokentypes
+
+        self.word_embeddings = VocabEmbedding(vocab_size, self.hidden_size)
+
+        # Position embedding (serial).
+        self.position_embeddings = torch.nn.Embedding(
+            max_sequence_length, self.hidden_size)
+
+        # Token type embedding.
+        # Add this as an optional field that can be added through
+        # method call so we can load a pretrain model without
+        # token types and add them as needed.
+        if self.num_tokentypes > 0:
+            self.tokentype_embeddings = torch.nn.Embedding(self.num_tokentypes,
+                                                           self.hidden_size)
+        else:
+            self.tokentype_embeddings = None
+
+        # Embeddings dropout
+        self.embedding_dropout = torch.nn.Dropout(embedding_dropout_prob)
+
+    @property
+    def word_embedding_weight(self):
+        return self.word_embeddings.weight
+
+    def forward(self, input_ids, position_ids, tokentype_ids=None):
+        # Embeddings.
+        words_embeddings = self.word_embeddings(input_ids)
+        position_embeddings = self.position_embeddings(position_ids)
+        embeddings = words_embeddings + position_embeddings
+        if tokentype_ids is not None and self.tokentype_embeddings is not None:
+            embeddings = embeddings + self.tokentype_embeddings(tokentype_ids)
+
+        # Dropout.
+        embeddings = self.embedding_dropout(embeddings)
+
+        return embeddings

examples/tutorial/sequence_parallel/model/layers/head.py

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+import colossalai
+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+from .pooler import Pooler
+from .linear import Linear
+from .embedding import VocabEmbedding
+from colossalai.core import global_context as gpc
+from colossalai.context import ParallelMode
+from colossalai.kernel import LayerNorm
+from loss_func.cross_entropy import vocab_cross_entropy
+
+
+class BertLMHead(nn.Module):
+    """Masked LM head for Bert
+    Arguments:
+        hidden_size: hidden size
+        init_method: init method for weight initialization
+        layernorm_epsilon: tolerance for layer norm divisions
+    """
+
+    def __init__(self,
+                 vocab_size,
+                 hidden_size,
+                 ):
+
+        super(BertLMHead, self).__init__()
+        self.bias = torch.nn.Parameter(torch.zeros(vocab_size))
+
+        self.dense = Linear(hidden_size, hidden_size)
+        self.layernorm = LayerNorm(hidden_size)
+        self.gelu = torch.nn.functional.gelu
+
+    def forward(self, hidden_states, word_embeddings_weight, lm_labels):
+        hidden_states = self.dense(hidden_states)
+        hidden_states = self.gelu(hidden_states)
+        hidden_states = self.layernorm(hidden_states)
+
+        output = F.linear(hidden_states, word_embeddings_weight, self.bias)
+        lm_loss = vocab_cross_entropy(output, lm_labels)
+
+        return lm_loss
+
+
+class BertBinaryHead(nn.Module):
+
+    def __init__(self, hidden_size):
+        super().__init__()
+        self.pooler = Pooler(hidden_size)
+        self.dense = Linear(hidden_size, 2)
+
+    def forward(self, hidden_states):
+        if gpc.get_local_rank(ParallelMode.SEQUENCE) == 0:
+            output = self.pooler(hidden_states)
+            output = self.dense(output)
+        else:
+            output = None
+        return output
+
+
+class BertDualHead(nn.Module):
+
+    def __init__(self, hidden_size, vocab_size, add_binary_head):
+        super().__init__()
+        self.lm_head = BertLMHead(vocab_size, hidden_size)
+        self.add_binary_head = add_binary_head
+        if add_binary_head:
+            self.binary_head = BertBinaryHead(hidden_size)
+        else:
+            self.binary_head = None
+
+    def forward(self, hidden_states, word_embeddings_weight, lm_labels):
+        if self.add_binary_head:
+            binary_output = self.binary_head(hidden_states)
+        else:
+            binary_output = None
+        lm_loss = self.lm_head(hidden_states, word_embeddings_weight, lm_labels)
+        return lm_loss, binary_output

examples/tutorial/sequence_parallel/model/layers/init_method.py

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+import torch
+import math
+
+def init_normal(tensor, sigma):
+    """Init method based on N(0, sigma)."""
+    torch.nn.init.normal_(tensor, mean=0.0, std=sigma)
+
+
+def output_init_normal(tensor, sigma, num_layers):
+    """Init method based on N(0, sigma/sqrt(2*num_layers)."""
+    std = sigma / math.sqrt(2.0 * num_layers)
+    torch.nn.init.normal_(tensor, mean=0.0, std=std)

examples/tutorial/sequence_parallel/model/layers/linear.py

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+import torch
+import torch.nn as nn
+from torch.nn import Parameter
+import torch.nn.functional as F
+import torch.nn.init as init
+
+
+class Linear(nn.Module):
+    """Linear layer with column parallelism.
+    The linear layer is defined as Y = XA + b. A is parallelized along
+    its second dimension as A = [A_1, ..., A_p].
+    Arguments:
+        input_size: first dimension of matrix A.
+        output_size: second dimension of matrix A.
+        bias: If true, add bias
+        init_method: method to initialize weights. Note that bias is always set
+                     to zero.
+        stride: For the strided linear layers.
+        keep_master_weight_for_test: This was added for testing and should be
+                                     set to False. It returns the master weights
+                                     used for initialization.
+        skip_bias_add: This was added to enable performance optimations where bias
+                       can be fused with other elementwise operations. we skip
+                       adding bias but instead return it.
+    """
+
+    def __init__(self,
+                 input_size,
+                 output_size,
+                 bias=True,
+                 skip_bias_add=False):
+        super(Linear, self).__init__()
+
+        # Keep input parameters
+        self.input_size = input_size
+        self.output_size = output_size
+        self.skip_bias_add = skip_bias_add
+
+        self.weight = Parameter(torch.empty(self.output_size,
+                                            self.input_size,
+                                            ))
+        init.normal_(self.weight)
+        if bias:
+            self.bias = Parameter(torch.empty(self.output_size))
+            # Always initialize bias to zero.
+            with torch.no_grad():
+                self.bias.zero_()
+        else:
+            self.register_parameter('bias', None)
+
+    def forward(self, input_):
+        # Matrix multiply.
+        bias = self.bias if not self.skip_bias_add else None
+        output = F.linear(input_, self.weight, bias)
+
+        if self.skip_bias_add:
+            return output, self.bias
+        else:
+            return output
+
+    def __repr__(self):
+        return f'Linear(in_features={self.input_size}, out_features={self.output_size}, ' + \
+            f'bias={self.bias is not None}, skip_bias_add={self.skip_bias_add})'

examples/tutorial/sequence_parallel/model/layers/mlp.py

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+import torch
+import torch.nn as nn
+import torch.nn.functional as F
+
+from .linear import Linear
+from colossalai.kernel.jit import bias_gelu_impl
+
+
+class TransformerMLP(nn.Module):
+    """MLP.
+    MLP will take the input with h hidden state, project it to 4*h
+    hidden dimension, perform nonlinear transformation, and project the
+    state back into h hidden dimension. At the end, dropout is also
+    applied.
+    """
+
+    def __init__(self, hidden_size, mlp_ratio, fuse_gelu=True):
+        super(TransformerMLP, self).__init__()
+
+        # Project to 4h.
+        self.dense_h_to_4h = Linear(
+            hidden_size,
+            int(hidden_size*mlp_ratio),
+            skip_bias_add=True)
+
+        self.bias_gelu_fusion = fuse_gelu
+        self.activation_func = F.gelu
+
+        # Project back to h.
+        self.dense_4h_to_h = Linear(
+            int(hidden_size*mlp_ratio),
+            hidden_size,
+            skip_bias_add=True)
+
+    def forward(self, hidden_states):
+        # hidden states should be in the shape of [s, b, h]
+        # it will be projects into [s, b, 4h]
+        # and projected back to [s, b, h]
+        intermediate_parallel, bias_parallel = self.dense_h_to_4h(hidden_states)
+
+        if self.bias_gelu_fusion:
+            intermediate_parallel = \
+                bias_gelu_impl(intermediate_parallel, bias_parallel)
+        else:
+            intermediate_parallel = \
+                self.activation_func(intermediate_parallel + bias_parallel)
+
+        # [s, b, h]
+        output, output_bias = self.dense_4h_to_h(intermediate_parallel)
+        return output, output_bias

examples/tutorial/sequence_parallel/model/layers/pooler.py

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+import torch
+import torch.nn as nn
+from .linear import Linear
+
+
+class Pooler(nn.Module):
+    """Pooler layer.
+
+    Pool hidden states of a specific token (for example start of the
+    sequence) and add a linear transformation followed by a tanh.
+
+    Arguments:
+        hidden_size: hidden size
+        init_method: weight initialization method for the linear layer.
+            bias is set to zero.
+    """
+
+    def __init__(self, hidden_size):
+        super(Pooler, self).__init__()
+        self.dense = Linear(hidden_size, hidden_size)
+
+    def forward(self, hidden_states, sequence_index=0):
+        # hidden_states: [b, s, h]
+        # sequence_index: index of the token to pool.
+        pooled = hidden_states[:, sequence_index, :]
+        pooled = self.dense(pooled)
+        pooled = torch.tanh(pooled)
+        return pooled

examples/tutorial/sequence_parallel/model/layers/preprocess.py

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+from colossalai.context.parallel_mode import ParallelMode
+import torch
+import torch.nn as nn
+from colossalai.core import global_context as gpc
+
+
+class PreProcessor(nn.Module):
+
+    def __init__(self, sub_seq_length):
+        super().__init__()
+        self.sub_seq_length = sub_seq_length
+
+    def bert_position_ids(self, token_ids):
+        # Create position ids
+        seq_length = token_ids.size(1)
+        local_rank = gpc.get_local_rank(ParallelMode.SEQUENCE)
+        position_ids = torch.arange(seq_length*local_rank,
+                                    seq_length * (local_rank+1),
+                                    dtype=torch.long,
+                                    device=token_ids.device)
+        position_ids = position_ids.unsqueeze(0).expand_as(token_ids)
+
+        return position_ids
+
+    def bert_extended_attention_mask(self, attention_mask):
+        local_rank = gpc.get_local_rank(ParallelMode.SEQUENCE)
+        start_index = local_rank * self.sub_seq_length
+        end_index = (local_rank + 1) * self.sub_seq_length
+
+        # We create a 3D attention mask from a 2D tensor mask.
+        # [b, 1, s]
+        attention_mask_b1s = attention_mask.unsqueeze(1)
+        # [b, s, 1]
+        attention_mask_bs1 = attention_mask.unsqueeze(2)
+        # [b, s/D, s]
+        attention_mask_bss = attention_mask_b1s * attention_mask_bs1
+
+        attention_mask_bss = attention_mask_bss[:, start_index:end_index, :]
+
+        # [b, 1, s/D, s]
+        extended_attention_mask = attention_mask_bss.unsqueeze(1)
+
+        # Convert attention mask to binary:
+        extended_attention_mask = (extended_attention_mask < 0.5)
+
+        return extended_attention_mask
+
+    def forward(self, input_ids=None, attention_mask=None):
+        if attention_mask is not None:
+            extended_attention_mask = self.bert_extended_attention_mask(attention_mask)
+        else:
+            extended_attention_mask = None
+
+        if input_ids is not None:
+            position_ids = self.bert_position_ids(input_ids)
+        else:
+            position_ids = None
+        return position_ids, extended_attention_mask