Create a small tutorial on how to accelerate HF Llama models with Tra…

…nsformer-Engine (#615)

docs/examples/te_llama/te_llama.py

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+# Copyright (c) 2022-2024, NVIDIA CORPORATION & AFFILIATES. All rights reserved.
+#
+# See LICENSE for license information.
+
+import os
+import re
+import gc
+from contextlib import contextmanager
+
+import torch
+from torch import nn
+
+import transformer_engine as te
+from transformer_engine.pytorch.attention import RotaryPositionEmbedding
+from transformer_engine.pytorch.fp8 import fp8_model_init
+
+import transformers
+from transformers.models.llama.modeling_llama import LlamaModel, LlamaForCausalLM, LlamaRMSNorm, LlamaConfig
+from transformers.modeling_utils import _add_variant, load_state_dict, _load_state_dict_into_model
+from transformers.utils import WEIGHTS_INDEX_NAME
+from transformers.utils.hub import get_checkpoint_shard_files
+
+@contextmanager
+def replace_decoder(te_decodder_cls):
+    """
+    Replace `LlamaDecoderLayer` with custom `TELlamaDecoderLayer`.
+    """
+    original_llama_decoder_cls = transformers.models.llama.modeling_llama.LlamaDecoderLayer
+    transformers.models.llama.modeling_llama.LlamaDecoderLayer = te_decodder_cls
+    try:
+        yield
+    finally:
+        transformers.models.llama.modeling_llama.LlamaDecoderLayer = original_llama_decoder_cls
+
+
+class TELlamaDecoderLayer(te.pytorch.TransformerLayer):
+    """
+    Wrapper class over TE's `TransformerLayer`. This makes the wrapper very
+    similar to HF's `LlamaDecoderLayer` and easier to replace it in the code.
+
+    Args:
+        config: LlamaConfig
+        args: positional args (for compatibility with `LlamaDecoderLayer`)
+        kwargs: keyword args (for compatibility with `LlamaDecoderLayer`)
+    """
+    def __init__(self, config, *args, **kwargs):
+        super().__init__(
+            hidden_size=config.hidden_size,
+            ffn_hidden_size=config.intermediate_size,
+            num_attention_heads=config.num_attention_heads,
+            bias=False,
+            layernorm_epsilon=config.rms_norm_eps,
+            hidden_dropout=0,
+            attention_dropout=0,
+            fuse_qkv_params=False,
+            normalization="RMSNorm",
+            activation="swiglu",
+            attn_input_format="bshd",
+        )
+        te_rope = RotaryPositionEmbedding(config.hidden_size//config.num_attention_heads)
+        self.te_rope_emb = te_rope(max_seq_len=config.max_position_embeddings).cuda()
+
+    def forward(self,
+                hidden_states,
+                *args,
+                attention_mask,
+                **kwargs):
+        """
+        Custom forward to make sure we only pass relevant arguments to the
+        forward pass of the `TransformerLayer`. Also, make sure the output
+        format matches the output of the HF's `LlamaDecoderLayer`.
+        """
+        return (super().forward(hidden_states, attention_mask=attention_mask, rotary_pos_emb=self.te_rope_emb),)
+
+
+class TELlamaForCausalLM:
+    """
+    Causal LM created with `LlamaModel`. The underlying `LlamaDecoderLayer`
+    class is monkey-patched with `TELlamaDecoderLayer` class before
+    initializing the causal LM with `LlamaForCausalLM`.
+
+    Args:
+        config: LlamaConfig
+    """
+
+    def __new__(cls, config: LlamaConfig):
+        with replace_decoder(te_decodder_cls=TELlamaDecoderLayer):
+            llama_for_causal_lm = LlamaForCausalLM(config)
+        return llama_for_causal_lm
+
+    @classmethod
+    def from_pretrained_local(cls, pretrained_model_name_or_path, *args, config, **kwargs):
+        """
+        Custom method adapted from `from_pretrained` method in HuggingFace
+        Transformers repo: https://github.com/huggingface/transformers/blob/f497f564bb76697edab09184a252fc1b1a326d1e/src/transformers/modeling_utils.py#L2579
+        """
+        vanilla_model = cls(config).to(kwargs['torch_dtype'])
+        is_local = os.path.isdir(pretrained_model_name_or_path)
+        subfolder = ""
+        variant = None
+        if os.path.isfile(
+                    os.path.join(pretrained_model_name_or_path, subfolder, _add_variant(WEIGHTS_INDEX_NAME, variant))
+            ):
+                # Load from a sharded PyTorch checkpoint
+                archive_file = os.path.join(
+                    pretrained_model_name_or_path, subfolder, _add_variant(WEIGHTS_INDEX_NAME, variant)
+                )
+                is_sharded = True
+        else:
+            raise AssertionError("Only sharded PyTorch ckpt format supported at the moment")
+
+
+        resolved_archive_file, sharded_metadata = get_checkpoint_shard_files(
+                pretrained_model_name_or_path,
+                archive_file,
+        )
+
+        # If the checkpoint is not sharded, it's a trivial sharding case
+        if not is_sharded:
+            assert not isinstance(resolved_archive_file, list)
+            resolved_archive_file = [resolved_archive_file]
+
+        error_msgs = []
+        for shard_file in resolved_archive_file:
+            state_dict = load_state_dict(shard_file)
+            replaced_layers = replace_params(state_dict, vanilla_model.state_dict())
+
+            error_msgs += _load_state_dict_into_model(vanilla_model, state_dict, start_prefix="")
+
+            # Force mem release. Taken from huggingface code
+            del state_dict
+            gc.collect()
+
+        return vanilla_model
+
+def replace_params(hf_state_dict, te_state_dict):
+    # collect all layer prefixes to update
+    all_layer_prefixes = set()
+    for param_key in hf_state_dict.keys():
+        layer_prefix_pat = 'model.layers.\d+.'
+        m = re.match(layer_prefix_pat, param_key)
+        if m is not None:
+            all_layer_prefixes.add(m.group())
+
+    for layer_prefix in all_layer_prefixes:
+        # When loading weights into models with less number of layers, skip the
+        # copy if the corresponding layer doesn't exist in TE model
+        if layer_prefix + 'self_attention.layernorm_qkv.layer_norm_weight' in te_state_dict:
+            te_state_dict[layer_prefix + 'self_attention.layernorm_qkv.layer_norm_weight'].data[:] = hf_state_dict[layer_prefix + 'input_layernorm.weight'].data[:]
+
+        if layer_prefix + 'self_attention.layernorm_qkv.query_weight' in te_state_dict:
+            te_state_dict[layer_prefix + 'self_attention.layernorm_qkv.query_weight'].data[:] = hf_state_dict[layer_prefix + 'self_attn.q_proj.weight'].data[:]
+
+        if layer_prefix + 'self_attention.layernorm_qkv.key_weight' in te_state_dict:
+            te_state_dict[layer_prefix + 'self_attention.layernorm_qkv.key_weight'].data[:] = hf_state_dict[layer_prefix + 'self_attn.k_proj.weight'].data[:]
+
+        if layer_prefix + 'self_attention.layernorm_qkv.value_weight' in te_state_dict:
+            te_state_dict[layer_prefix + 'self_attention.layernorm_qkv.value_weight'].data[:] = hf_state_dict[layer_prefix + 'self_attn.v_proj.weight'].data[:]
+
+        if layer_prefix + 'self_attention.proj.weight' in te_state_dict:
+            te_state_dict[layer_prefix + 'self_attention.proj.weight'].data[:] = hf_state_dict[layer_prefix + 'self_attn.o_proj.weight'].data[:]
+
+        if layer_prefix + 'layernorm_mlp.layer_norm_weight' in te_state_dict:
+            te_state_dict[layer_prefix + 'layernorm_mlp.layer_norm_weight'].data[:] = hf_state_dict[layer_prefix + 'post_attention_layernorm.weight'].data[:]
+
+        if layer_prefix + 'layernorm_mlp.fc1_weight' in te_state_dict:
+            te_state_dict[layer_prefix + 'layernorm_mlp.fc1_weight'].data[:] = torch.cat((hf_state_dict[layer_prefix + 'mlp.gate_proj.weight'].data[:], hf_state_dict[layer_prefix + 'mlp.up_proj.weight'].data[:]), dim=0)
+
+        if layer_prefix + 'layernorm_mlp.fc2_weight' in te_state_dict:
+            te_state_dict[layer_prefix + 'layernorm_mlp.fc2_weight'].data[:] = hf_state_dict[layer_prefix + 'mlp.down_proj.weight'].data[:]
+
+    return all_layer_prefixes