Source code for deepspeed.ops.transformer.transformer

'''
Copyright 2020 The Microsoft DeepSpeed Team
'''
import json
import math
import importlib
import torch
from torch import nn
from torch.autograd import Function

from ..op_builder import TransformerBuilder, StochasticTransformerBuilder

# Cuda modules will be imported if needed
transformer_cuda_module = None
stochastic_transformer_cuda_module = None


class TransformerConfig():
    def __init__(self,
                 batch_size,
                 hidden_size,
                 intermediate_size,
                 heads,
                 attn_dropout_ratio,
                 hidden_dropout_ratio,
                 num_hidden_layers,
                 initializer_range):
        self.layer_id = -1
        self.batch_size = batch_size
        self.hidden_size = hidden_size
        self.intermediate_size = intermediate_size
        self.heads = heads
        self.attn_dropout_ratio = attn_dropout_ratio
        self.hidden_dropout_ratio = hidden_dropout_ratio
        self.num_hidden_layers = num_hidden_layers
        self.initializer_range = initializer_range


[docs]class DeepSpeedTransformerConfig(TransformerConfig): """Initialize the DeepSpeed Transformer Config. Arguments: batch_size: The maximum batch size used for running the kernel on each GPU hidden_size: The hidden size of the transformer layer intermediate_size: The intermediate size of the feed-forward part of transformer layer heads: The number of heads in the self-attention of the transformer layer attn_dropout_ratio: The ratio of dropout for the attention's output hidden_dropout_ratio: The ratio of dropout for the transformer's output num_hidden_layers: The number of transformer layers initializer_range: BERT model's initializer range for initializing parameter data local_rank: Optional: The rank of GPU running the transformer kernel, it is not required to use if the model already set the current device, otherwise need to set it so that the transformer kernel can work on the right device seed: The random seed for the dropout layers fp16: Enable half-precision computation pre_layer_norm: Select between Pre-LN or Post-LN transformer architecture normalize_invertible: Optional: Enable invertible LayerNorm execution (dropping the input activation), default is False gelu_checkpoint: Optional: Enable checkpointing of Gelu activation output to save memory, default is False adjust_init_range: Optional: Set as True (default) if the model adjusts the weight initial values of its self-attention output and layer output, False keeps the initializer_range no change. See the adjustment below: output_std = self.config.initializer_range / math.sqrt(2.0 * num_layers) attn_dropout_checkpoint: Optional: Enable checkpointing of attention dropout to save memory, default is False stochastic_mode: Enable for high performance, please note that this flag has some level of non-determinism and can produce different results on different runs. However, we have seen that by enabling it, the pretraining tasks such as BERT are not affected and can obtain a high accuracy level. On the other hand, for the downstream tasks, such as fine-tuning, we recommend to turn it off in order to be able to reproduce the same result through the regular kernel execution. huggingface: Enable if using the HuggingFace interface style for sending out the forward results. training: Enable for training rather than inference. """ def __init__(self, batch_size=-1, hidden_size=-1, intermediate_size=-1, heads=-1, attn_dropout_ratio=-1, hidden_dropout_ratio=-1, num_hidden_layers=-1, initializer_range=-1, layer_norm_eps=1e-12, local_rank=-1, seed=-1, fp16=False, pre_layer_norm=True, normalize_invertible=False, gelu_checkpoint=False, adjust_init_range=True, attn_dropout_checkpoint=False, stochastic_mode=False, huggingface=False, training=True): super(DeepSpeedTransformerConfig, self).__init__( batch_size, hidden_size, (intermediate_size if intermediate_size > 0 else 4 * hidden_size), heads, attn_dropout_ratio, hidden_dropout_ratio, num_hidden_layers, initializer_range) self.fp16 = fp16 self.pre_layer_norm = pre_layer_norm self.local_rank = local_rank self.seed = seed self.normalize_invertible = normalize_invertible self.gelu_checkpoint = gelu_checkpoint # True: if higher batch size is required self.adjust_init_range = adjust_init_range self.test_gemm = False self.layer_norm_eps = layer_norm_eps self.training = training self.is_grad_enabled = True self.attn_dropout_checkpoint = attn_dropout_checkpoint self.stochastic_mode = stochastic_mode self.huggingface = huggingface @classmethod def from_dict(cls, json_object): config = DeepSpeedTransformerConfig() for key, value in json_object.items(): config.__dict__[key] = value return config @classmethod def from_json_file(cls, json_file): with open(json_file, "r", encoding='utf-16') as reader: text = reader.read() return cls.from_dict(json.loads(text))
class DeepSpeedTransformerFunction(Function): @staticmethod def forward(ctx, input, input_mask, self, grads, layer_id, attn_qkvw, attn_qkvb, attn_ow, attn_ob, attn_nw, attn_nb, inter_w, inter_b, output_w, output_b, norm_w, norm_b, config): cuda_module = stochastic_transformer_cuda_module if config.stochastic_mode else transformer_cuda_module forward_func = cuda_module.forward_fp16 if config.fp16 else cuda_module.forward_fp32 inp_size = input.size() if inp_size[1] % 16 != 0: input = torch.cat((input, torch.randn((inp_size[0], (16 - (inp_size[1] % 16)), inp_size[2]), device=input.device, dtype=input.dtype)), 1) input_mask = torch.cat((input_mask, torch.ones((inp_size[0], input_mask.shape[1], input_mask.shape[2], \ (16 - (inp_size[1] % 16))), device=input_mask.device, dtype=input_mask.dtype) * -10000), 3) (output, inp_norm, qkv_tf, soft_inp, ctx_bufB, attn_o_inp, add_res, ff1_inp, gelu_inp, ff2_inp, attn_prob_dropout_mask, attn_output_dropout_mask, layer_output_dropout_mask, attn_layer_norm_var, attn_layer_norm_mean, layer_norm_var, layer_norm_mean) = forward_func(config.layer_id, input, input_mask, attn_qkvw, attn_qkvb, attn_ow, attn_ob, attn_nw, attn_nb, inter_w, inter_b, output_w, output_b, norm_w, norm_b, config.training, config.pre_layer_norm, config.attn_dropout_checkpoint, config.normalize_invertible, config.gelu_checkpoint) # For testing only. if grads is not None: for i in [2]: attn_qkvw.register_hook( lambda x, i=i, self=self: grads.append([ x[i * attn_ow.size(0):(i + 1) * attn_ow.size(0)], ("Q_W" if i == 0 else "K_W" if i == 1 else "V_W") ])) for i in [2]: attn_qkvb.register_hook( lambda x, i=i, self=self: grads.append([ x[i * attn_ow.size(0):(i + 1) * attn_ow.size(0)], ("Q_B" if i == 0 else "K_B" if i == 1 else "V_B") ])) attn_ow.register_hook(lambda x, self=self: grads.append([x, "O_W"])) attn_ob.register_hook(lambda x, self=self: grads.append([x, "O_B"])) attn_nw.register_hook(lambda x, self=self: grads.append([x, "N2_W"])) attn_nb.register_hook(lambda x, self=self: grads.append([x, "N2_B"])) inter_w.register_hook(lambda x, self=self: grads.append([x, "int_W"])) inter_b.register_hook(lambda x, self=self: grads.append([x, "int_B"])) output_w.register_hook(lambda x, self=self: grads.append([x, "out_W"])) output_b.register_hook(lambda x, self=self: grads.append([x, "out_B"])) norm_w.register_hook(lambda x, self=self: grads.append([x, "norm_W"])) norm_b.register_hook(lambda x, self=self: grads.append([x, "norm_B"])) if config.is_grad_enabled and config.training: if (config.pre_layer_norm and config.normalize_invertible): ctx.save_for_backward(input_mask, attn_qkvw, attn_qkvb, attn_ow, attn_ob, attn_nw, attn_nb, inter_w, inter_b, output_w, output_b, norm_w, norm_b) else: ctx.save_for_backward(output, input, input_mask, attn_qkvw, attn_qkvb, attn_ow, attn_ob, attn_nw, attn_nb, inter_w, inter_b, output_w, output_b, norm_w, norm_b) ctx.config = config if (config.pre_layer_norm or not config.normalize_invertible): ctx.inp_norm = inp_norm ctx.qkv_tf = qkv_tf ctx.soft_inp = soft_inp if not config.attn_dropout_checkpoint: ctx.ctx_bufB = ctx_bufB ctx.attn_o_inp = attn_o_inp if not config.normalize_invertible: ctx.add_res = add_res ctx.attn_layer_norm_mean = attn_layer_norm_mean ctx.layer_norm_mean = layer_norm_mean ctx.ff1_inp = ff1_inp if not config.gelu_checkpoint: ctx.gelu_inp = gelu_inp ctx.ff2_inp = ff2_inp ctx.attn_prob_dropout_mask = attn_prob_dropout_mask ctx.attn_output_dropout_mask = attn_output_dropout_mask ctx.layer_output_dropout_mask = layer_output_dropout_mask ctx.attn_layer_norm_var = attn_layer_norm_var ctx.layer_norm_var = layer_norm_var if inp_size[1] % 16 != 0: output = torch.narrow(output, 1, 0, inp_size[1]) if config.huggingface: return (output, ) # outputs -> (output) : outputs[0] = output else: return output @staticmethod def backward(ctx, grad_output): bsz = grad_output.shape[0] grad_output_shape = grad_output.size() if grad_output_shape[1] % 16 != 0: grad_output = torch.cat((grad_output, torch.zeros((bsz, (16 - (grad_output_shape[1] % 16)), \ grad_output_shape[2]), device=grad_output.device, dtype=grad_output.dtype)), 1) assert ctx.config.training if (ctx.config.pre_layer_norm and ctx.config.normalize_invertible): (input_mask, attn_qkvw, attn_qkvb, attn_ow, attn_ob, attn_nw, attn_nb, inter_w, inter_b, output_w, output_b, norm_w, norm_b) = ctx.saved_tensors else: (output, input, input_mask, attn_qkvw, attn_qkvb, attn_ow, attn_ob, attn_nw, attn_nb, inter_w, inter_b, output_w, output_b, norm_w, norm_b) = ctx.saved_tensors cuda_module = stochastic_transformer_cuda_module if ctx.config.stochastic_mode else transformer_cuda_module backward_func = cuda_module.backward_fp16 if ctx.config.fp16 else cuda_module.backward_fp32 (grad_input, grad_attn_qkvw, grad_attn_qkvb, grad_attn_ow, grad_attn_ob, grad_attn_nw, grad_attn_nb, grad_inter_w, grad_inter_b, grad_output_w, grad_output_b, grad_norm_w, grad_norm_b) = backward_func( ctx.config.layer_id, grad_output, (ctx.inp_norm if (ctx.config.pre_layer_norm and ctx.config.normalize_invertible) else output), (ctx.inp_norm if (ctx.config.pre_layer_norm or not ctx.config.normalize_invertible) else input), ctx.qkv_tf, ctx.soft_inp, (ctx.soft_inp if ctx.config.attn_dropout_checkpoint else ctx.ctx_bufB), ctx.attn_o_inp, (ctx.ff1_inp if ctx.config.normalize_invertible else ctx.add_res), ctx.ff1_inp, (ctx.ff2_inp if ctx.config.gelu_checkpoint else ctx.gelu_inp), ctx.ff2_inp, ctx.attn_prob_dropout_mask, ctx.attn_output_dropout_mask, ctx.layer_output_dropout_mask, ctx.attn_layer_norm_var, ctx.attn_layer_norm_mean, ctx.layer_norm_var, ctx.layer_norm_mean, (ctx.inp_norm if (ctx.config.pre_layer_norm and ctx.config.normalize_invertible) else input), input_mask, attn_qkvw, attn_qkvb, attn_ow, attn_ob, attn_nw, attn_nb, inter_w, inter_b, output_w, output_b, norm_w, norm_b) # This appears to be an effective way to release context memory ctx.qkv_tf = None ctx.soft_inp = None ctx.ctx_bufB = None ctx.gelu_inp = None ctx.ff2_inp = None ctx.attn_o_inp = None ctx.ff1_inp = None ctx.add_res = None ctx.inp_norm = None ctx.config = None ctx.attn_layer_norm_mean = None ctx.layer_norm_mean = None ctx.attn_prob_dropout_mask = None ctx.attn_output_dropout_mask = None ctx.layer_output_dropout_mask = None ctx.attn_layer_norm_var = None ctx.layer_norm_var = None if grad_output_shape[1] % 16 != 0: grad_input = torch.narrow(grad_input, 1, 0, grad_output_shape[1]) return (grad_input, None, None, None, None, grad_attn_qkvw, grad_attn_qkvb, grad_attn_ow, grad_attn_ob, grad_attn_nw, grad_attn_nb, grad_inter_w, grad_inter_b, grad_output_w, grad_output_b, grad_norm_w, grad_norm_b, None)
[docs]class DeepSpeedTransformerLayer(nn.Module): """Initialize the DeepSpeed Transformer Layer. Static variable: layer_id: The layer-index counter starting from 0 and incrementing by 1 every time a layer object is instantiated, e.g. if a model has 24 transformer layers, layer_id goes from 0 to 23. Arguments: config: An object of DeepSpeedTransformerConfig initial_weights: Optional: Only used for unit test initial_biases: Optional: Only used for unit test """ layer_id = 0 def __init__(self, config, initial_weights=None, initial_biases=None): super(DeepSpeedTransformerLayer, self).__init__() self.config = config self.config.layer_id = DeepSpeedTransformerLayer.layer_id DeepSpeedTransformerLayer.layer_id = DeepSpeedTransformerLayer.layer_id + 1 print("DeepSpeed Transformer config is ", self.config.__dict__) if self.config.local_rank >= 0: torch.cuda.set_device(self.config.local_rank) if initial_weights is None and initial_biases is None: self.attn_qkvw = nn.Parameter( torch.Tensor(self.config.hidden_size * 3, self.config.hidden_size)) self.attn_qkvb = nn.Parameter(torch.Tensor(self.config.hidden_size * 3)) self.attn_ow = nn.Parameter( torch.Tensor(self.config.hidden_size, self.config.hidden_size)) self.attn_ob = nn.Parameter(torch.Tensor(self.config.hidden_size)) self.attn_nw = nn.Parameter(torch.Tensor(self.config.hidden_size)) self.attn_nb = nn.Parameter(torch.Tensor(self.config.hidden_size)) self.inter_w = nn.Parameter( torch.Tensor(self.config.intermediate_size, self.config.hidden_size)) self.inter_b = nn.Parameter(torch.Tensor(self.config.intermediate_size)) self.output_w = nn.Parameter( torch.Tensor(self.config.hidden_size, self.config.intermediate_size)) self.output_b = nn.Parameter(torch.Tensor(self.config.hidden_size)) self.norm_w = nn.Parameter(torch.Tensor(self.config.hidden_size)) self.norm_b = nn.Parameter(torch.Tensor(self.config.hidden_size)) self.init_transformer_weights(self.config.adjust_init_range) else: # For testing only. q = initial_weights[0].data k = initial_weights[1].data v = initial_weights[2].data self.attn_qkvw = nn.Parameter(torch.cat((q, k, v))) #self.attn_qkvw[i * self.config.hidden_size:(i + 1) * self.config.hidden_size] = \ # initial_weights[i].clone() #torch.empty_like(initial_weights[i]).data.copy_(initial_weights[i].data) self.attn_qkvb = nn.Parameter(torch.Tensor(self.config.hidden_size * 3)) self.attn_qkvb.data.zero_() self.attn_ow = initial_weights[3] self.attn_ob = initial_biases[3] self.attn_nw = initial_weights[4] self.attn_nb = initial_biases[4] self.inter_w = initial_weights[5] self.inter_b = initial_biases[5] self.output_w = initial_weights[6] self.output_b = initial_biases[6] self.norm_w = initial_weights[7] self.norm_b = initial_biases[7] # Load cuda modules if needed global transformer_cuda_module, stochastic_transformer_cuda_module if transformer_cuda_module is None and not self.config.stochastic_mode: transformer_cuda_module = TransformerBuilder().load() if stochastic_transformer_cuda_module is None and self.config.stochastic_mode: stochastic_transformer_cuda_module = StochasticTransformerBuilder().load() # create the layer in cuda kernels. cuda_module = stochastic_transformer_cuda_module if self.config.stochastic_mode else transformer_cuda_module create_layer_func = cuda_module.create_transformer_layer_fp16 if self.config.fp16 else cuda_module.create_transformer_layer_fp32 create_layer_func(self.config.layer_id, self.config.batch_size, self.config.hidden_size, self.config.heads, self.config.intermediate_size, self.config.attn_dropout_ratio, self.config.hidden_dropout_ratio, self.config.layer_norm_eps, self.config.seed, self.config.pre_layer_norm, self.config.test_gemm, self.config.attn_dropout_checkpoint, self.config.normalize_invertible, self.config.gelu_checkpoint, self.config.stochastic_mode) def init_transformer_weights(self, adjust_init_range=False): num_layers = self.config.num_hidden_layers output_std = self.config.initializer_range if adjust_init_range and self.config.local_rank == 0: print("Accounting for accumulation on the residual path") output_std = self.config.initializer_range / math.sqrt(2.0 * num_layers) self.attn_qkvw.data.normal_(mean=0.0, std=self.config.initializer_range) self.attn_qkvb.data.zero_() self.attn_ow.data.normal_(mean=0.0, std=output_std) self.attn_ob.data.zero_() self.attn_nw.data.fill_(1.0) self.attn_nb.data.zero_() self.inter_w.data.normal_(mean=0.0, std=self.config.initializer_range) self.inter_b.data.zero_() self.output_w.data.normal_(mean=0.0, std=output_std) self.output_b.data.zero_() self.norm_w.data.fill_(1.0) self.norm_b.data.zero_() def forward(self, hidden_states, attention_mask=None, head_mask=None, layer_head_mask=None, encoder_hidden_states=None, encoder_attention_mask=None, past_key_value=None, output_attentions=False, grads=None): self.config.is_grad_enabled = torch.is_grad_enabled() return DeepSpeedTransformerFunction.apply(hidden_states, attention_mask, self, grads, self.config.layer_id, self.attn_qkvw, self.attn_qkvb, self.attn_ow, self.attn_ob, self.attn_nw, self.attn_nb, self.inter_w, self.inter_b, self.output_w, self.output_b, self.norm_w, self.norm_b, self.config)