Module replacement support

deepspeed/module_inject/inject.py

@@ -0,0 +1,122 @@
+import copy
+import torch
+from deepspeed.ops.transformer import DeepSpeedTransformerLayer, DeepSpeedTransformerConfig
+
+
+def module_inject(layer_obj,
+                  model,
+                  config,
+                  micro_batch_size,
+                  max_seq_length,
+                  seed,
+                  preln,
+                  fp16=True):
+    for name, child in model.named_children():
+        if isinstance(child, layer_obj):
+            print('REPLACING BertLayer')
+
+            cuda_config = DeepSpeedTransformerConfig(
+                batch_size=micro_batch_size,
+                max_seq_length=max_seq_length,
+                hidden_size=config.hidden_size,
+                heads=config.num_attention_heads,
+                attn_dropout_ratio=config.attention_probs_dropout_prob,
+                hidden_dropout_ratio=config.hidden_dropout_prob,
+                num_hidden_layers=config.num_hidden_layers,
+                initializer_range=config.initializer_range,
+                seed=seed,
+                fp16=fp16,
+                pre_layer_norm=preln)
+
+            new_module = DeepSpeedTransformerLayer(cuda_config)
+
+            # copy relevant state from child -> new module
+            qw = child.attention.self.query.weight
+            qb = child.attention.self.query.bias
+            kw = child.attention.self.key.weight
+            kb = child.attention.self.key.bias
+            vw = child.attention.self.value.weight
+            vb = child.attention.self.value.bias
+
+            qkvw = torch.cat((qw, kw, vw), 0)
+            qkvb = torch.cat((qb, kb, vb), 0)
+
+            new_module.attn_qkvw.data = qkvw
+            new_module.attn_qkvb.data = qkvb
+            new_module.attn_ow.data = child.attention.output.dense.weight
+            new_module.attn_ob.data = child.attention.output.dense.bias
+            if preln:
+                attention_layerNorm = child.PostAttentionLayerNorm
+            else:
+                attention_layerNorm = child.attention.output.LayerNorm
+            new_module.attn_nw.data = attention_layerNorm.weight
+            new_module.attn_nb.data = attention_layerNorm.bias
+            if preln:
+                intermediate_FF = child.intermediate.dense_act
+            else:
+                intermediate_FF = child.intermediate.dense
+            new_module.inter_w.data = intermediate_FF.weight
+            new_module.inter_b.data = intermediate_FF.bias
+            new_module.output_w.data = child.output.dense.weight
+            new_module.output_b.data = child.output.dense.bias
+            if preln:
+                transformer_LayerNorm = child.PreAttentionLayerNorm
+            else:
+                transformer_LayerNorm = child.output.LayerNorm
+            new_module.norm_w.data = transformer_LayerNorm.weight
+            new_module.norm_b.data = transformer_LayerNorm.bias
+
+            setattr(model, name, copy.deepcopy(new_module))
+
+        else:
+            module_inject(layer_obj,
+                          child,
+                          config,
+                          micro_batch_size,
+                          max_seq_length,
+                          seed,
+                          preln,
+                          fp16)
+
+    return model
+
+
+def test_hi():
+    from turing.nvidia_modelingpreln import BertConfig as BertConfigPreLN
+    from turing.nvidia_modelingpreln import BertForQuestionAnswering as BertForQuestionAnsweringPreLN
+    from turing.nvidia_modelingpreln import BertLayer
+    bert_model_config = {
+        "vocab_size_or_config_json_file": 119547,
+        "hidden_size": 1024,
+        "num_hidden_layers": 1,
+        "num_attention_heads": 16,
+        "intermediate_size": 4096,
+        "hidden_act": "gelu",
+        "hidden_dropout_prob": 0.1,
+        "attention_probs_dropout_prob": 0.1,
+        "hidden_dropout_prob": 0.1,
+        "attention_probs_dropout_prob": 0.1,
+        "max_position_embeddings": 512,
+        "type_vocab_size": 2,
+        "initializer_range": 0.02
+    }
+    bert_config = BertConfigPreLN(**bert_model_config)
+    base_model = BertForQuestionAnsweringPreLN(bert_config, args=None)
+
+    #base_model = LinearStack()
+
+    test_model = copy.deepcopy(base_model)
+    test_model = module_inject(BertLayer, test_model, bert_config, 4, 384, 1234)
+
+    print('BASE', base_model)
+    print('TEST', test_model)
+
+    #base_model.eval()
+    #test_model.eval()
+
+    #test_input = torch.rand(1, base_model.input_dim)
+
+    #base_output = base_model(test_input)
+    #test_output = test_model(test_input)
+    #
+    #assert torch.allclose(base_output, test_output, atol=3e-8)

deepspeed/module_inject/replace_module.py

@@ -0,0 +1,192 @@
+import copy
+import torch
+import deepspeed
+
+
+def replace_transformer_layer(orig_layer_impl,
+                              model,
+                              micro_batch_size,
+                              bert_config,
+                              seed,
+                              max_seq_length,
+                              preln=False,
+                              fp16=True,
+                              huggingface=False,
+                              local_rank=-1):
+    """ Replace bert-style transformer layers with DeepSpeed's transformer layer
+    Arguments:
+        orig_layer_impl (torch.nn.Module): the original transformer layer implementation to look for,
+            e.g., transformers.modeling_bert.BertLayer.
+        model (torch.nn.Module): user's nn.module representing their model
+        micro_batch_size (int): micro batch size per gpu used during training/eval
+        bert_config (dict): model config containing hidden size, attention heads, etc.
+        seed (int): random seed value
+        max_seq_length (int): max sequence length for training
+        preln (bool): does the original layer implementation do pre or post layer norm?
+        fp16 (bool): fp16 or fp32
+        huggingface (bool): huggingface implementation is unique (supports both encoder/decoder modes)
+
+    Returns:
+        Updated nn.module with replaced transformer layers
+    """
+    def replace_fn(child):
+        transformer_config = deepspeed.DeepSpeedTransformerConfig(
+            batch_size=micro_batch_size,
+            max_seq_length=max_seq_length,
+            hidden_size=bert_config.hidden_size,
+            heads=bert_config.num_attention_heads,
+            attn_dropout_ratio=bert_config.attention_probs_dropout_prob,
+            hidden_dropout_ratio=bert_config.hidden_dropout_prob,
+            num_hidden_layers=bert_config.num_hidden_layers,
+            initializer_range=bert_config.initializer_range,
+            seed=seed,
+            fp16=fp16,
+            pre_layer_norm=preln,
+            huggingface=huggingface,
+            local_rank=local_rank)
+        new_module = deepspeed.DeepSpeedTransformerLayer(transformer_config)
+
+        # copy relevant state from child -> new module
+        qw = child.attention.self.query.weight
+        qb = child.attention.self.query.bias
+        kw = child.attention.self.key.weight
+        kb = child.attention.self.key.bias
+        vw = child.attention.self.value.weight
+        vb = child.attention.self.value.bias
+
+        qkvw = torch.cat((qw, kw, vw), 0)
+        qkvb = torch.cat((qb, kb, vb), 0)
+
+        #qw.data,kw.data,vw.data = torch.chunk(qkvw, 3, axis=0)
+        #qb.data,kb.data,vb.data = torch.chunk(qkvb, 3, axis=0)
+
+        new_module.attn_qkvw.data = qkvw
+        new_module.attn_qkvb.data = qkvb
+        new_module.attn_ow.data = child.attention.output.dense.weight
+        new_module.attn_ob.data = child.attention.output.dense.bias
+        if preln:
+            attention_layernorm = child.PostAttentionLayerNorm
+        else:
+            attention_layernorm = child.attention.output.LayerNorm
+        new_module.attn_nw.data = attention_layernorm.weight
+        new_module.attn_nb.data = attention_layernorm.bias
+        if preln:
+            intermediate_ff = child.intermediate.dense_act
+        else:
+            intermediate_ff = child.intermediate.dense
+        new_module.inter_w.data = intermediate_ff.weight
+        new_module.inter_b.data = intermediate_ff.bias
+        new_module.output_w.data = child.output.dense.weight
+        new_module.output_b.data = child.output.dense.bias
+        if preln:
+            transformer_layernorm = child.PreAttentionLayerNorm
+        else:
+            transformer_layernorm = child.output.LayerNorm
+        new_module.norm_w.data = transformer_layernorm.weight
+        new_module.norm_b.data = transformer_layernorm.bias
+        return new_module
+
+    return replace_module(model=model, orig_class=orig_layer_impl, replace_fn=replace_fn)
+
+
+def revert_transformer_layer(orig_layer_impl, model, bert_config, preln=False):
+    """ Revert DeepSpeed's transformer layer back to original bert-style transformer layer
+    Arguments:
+        orig_layer_impl (torch.nn.Module): the original transformer layer implementation that was replaced,
+            e.g., transformers.modeling_bert.BertLayer.
+        model (torch.nn.Module): user's nn.module representing their model
+        bert_config (dict): model config containing hidden size, attention heads, etc.
+
+    Returns:
+        Updated nn.module with original bert-style transformer layers
+    """
+    def replace_fn(child):
+        #from turing.nvidia_modelingpreln import BertLayer
+        orig_module = orig_layer_impl(bert_config)
+
+        # copy relevant state from child -> original module
+        qkvw = child.attn_qkvw.data
+        qkvb = child.attn_qkvb.data
+
+        qw, kw, vw = torch.chunk(qkvw, 3, axis=0)
+        qb, kb, vb = torch.chunk(qkvb, 3, axis=0)
+
+        orig_module.attention.self.query.weight.data = qw
+        orig_module.attention.self.query.bias.data = qb
+        orig_module.attention.self.key.weight.data = kw
+        orig_module.attention.self.key.bias.data = kb
+        orig_module.attention.self.value.weight.data = vw
+        orig_module.attention.self.value.bias.data = vb
+
+        orig_module.attention.output.dense.weight.data = child.attn_ow.data
+        orig_module.attention.output.dense.bias.data = child.attn_ob.data
+
+        attn_ln_w = child.attn_nw.data
+        attn_ln_b = child.attn_nb.data
+        if preln:
+            orig_module.PostAttentionLayerNorm.weight.data = attn_ln_w
+            orig_module.PostAttentionLayerNorm.bias.data = attn_ln_b
+        else:
+            orig_module.attention.output.LayerNorm.weight.data = attn_ln_w
+            orig_module.attention.output.LayerNorm.bias.data = attn_ln_b
+
+        inter_ff_w = child.inter_w.data
+        inter_ff_b = child.inter_b.data
+        if preln:
+            orig_module.intermediate.dense_act.weight.data = inter_ff_w
+            orig_module.intermediate.dense_act.bias.data = inter_ff_b
+        else:
+            orig_module.intermediate.dense.weight.data = inter_ff_w
+            orig_module.intermediate.dense.bias.data = inter_ff_b
+
+        orig_module.output.dense.weight.data = child.output_w.data
+        orig_module.output.dense.bias.data = child.output_b.data
+
+        transformer_ln_w = child.norm_w.data
+        transformer_ln_b = child.norm_b.data
+        if preln:
+            orig_module.PreAttentionLayerNorm.weight.data = transformer_ln_w
+            orig_module.PreAttentionLayerNorm.bias.data = transformer_ln_b
+        else:
+            orig_module.output.LayerNorm.weight.data = transformer_ln_w
+            orig_module.output.LayerNorm.bias.data = transformer_ln_b
+        return orig_module
+
+    return replace_module(model=model,
+                          orig_class=deepspeed.DeepSpeedTransformerLayer,
+                          replace_fn=replace_fn)
+
+
+def replace_module(model, orig_class, replace_fn):
+    """ Scan the model for instances of ``orig_clas:`` to replace using ``replace_fn``.
+    Arguments:
+        model (torch.nn.Module): the model to augment
+        orig_class (torch.nn.Module): the module to search for
+        replace_fn (method): a method to convert instances of ``orig_class`` to the
+                             desired type and return a new instance.
+
+    Returns:
+        A modified ``model``.
+    """
+    policy = {orig_class: replace_fn}
+    return _replace_module(model, policy)
+
+
+def _replace_module(model, policies):
+    """ Traverse model's children recursively and apply any transformations in ``policies``.
+    Arguments:
+        model (torch.nn.Module): model to augment
+        policies (dict): Mapping of source class to replacement function.
+
+    Returns:
+        Modified ``model``.
+    """
+    for name, child in model.named_children():
+        if child.__class__ in policies:
+            orig = repr(child)
+            setattr(model, name, policies[child.__class__](child))
+            new = getattr(model, name)
+        else:
+            _replace_module(child, policies)
+
+    return model

deepspeed/ops/__init__.py

@@ -3,4 +3,7 @@
 from . import sparse_attention
 from . import transformer
 
+from .transformer import DeepSpeedTransformerLayer, DeepSpeedTransformerConfig
+from .module_inject import replace_module
+
 from ..git_version_info import compatible_ops as __compatible_ops__