Implement BTLM model

flash_attn/models/btlm.py

+# Copyright (c) 2023, Tri Dao.
+
+import math
+import json
+import re
+from pathlib import Path
+
+from collections import OrderedDict
+
+import torch
+import torch.nn.functional as F
+
+from einops import rearrange
+from transformers import GPT2Config, AutoConfig, PretrainedConfig
+
+
+def remap_state_dict_hf_btlm(state_dict, config):
+    # Word embedding and position embedding
+    def key_mapping_pos_emb(key):
+        return re.sub(r"^transformer.wpe.", "transformer.embeddings.position_embeddings.", key)
+
+    if "transformer.wpe.weight" in state_dict:
+        state_dict = OrderedDict((key_mapping_pos_emb(k), v) for k, v in state_dict.items())
+    word_embeddings = state_dict.pop("transformer.wte.weight")
+    # It's possible that vocab_size is padded to be a multiple of 8, for example.
+    pad_vocab_size_multiple = getattr(config, "pad_vocab_size_multiple", 1)
+    vocab_size = math.ceil(config.vocab_size / pad_vocab_size_multiple) * pad_vocab_size_multiple
+    state_dict["transformer.embeddings.word_embeddings.weight"] = F.pad(
+        word_embeddings, (0, 0, 0, vocab_size - word_embeddings.shape[0])
+    )
+    state_dict["lm_head.weight"] = state_dict["transformer.embeddings.word_embeddings.weight"]
+
+    # LayerNorm
+    def key_mapping_ln(key):
+        key = re.sub(r"^transformer.ln_f.(weight|bias)", r"transformer.ln_f.\1", key)
+        key = re.sub(r"^transformer.h.(\d+).ln_(1|2).(weight|bias)", r"transformer.layers.\1.norm\2.\3", key)
+        return key
+
+    state_dict = OrderedDict((key_mapping_ln(k), v) for k, v in state_dict.items())
+
+    # MLP
+    for d in range(config.num_hidden_layers):
+        W1 = state_dict.pop(f"transformer.h.{d}.mlp.c_fc.weight")
+        W3 = state_dict.pop(f"transformer.h.{d}.mlp.c_fc2.weight")
+        state_dict[f"transformer.layers.{d}.mlp.fc1.weight"] = torch.cat([W1.t(), W3.t()], dim=0)
+        b1 = state_dict.pop(f"transformer.h.{d}.mlp.c_fc.bias")
+        b3 = state_dict.pop(f"transformer.h.{d}.mlp.c_fc2.bias")
+        state_dict[f"transformer.layers.{d}.mlp.fc1.bias"] = torch.cat([b1, b3], dim=0)
+        W2 = state_dict.pop(f"transformer.h.{d}.mlp.c_proj.weight")
+        state_dict[f"transformer.layers.{d}.mlp.fc2.weight"] = W2.t()
+
+    def key_mapping_mlp(key):
+        key = re.sub(r"^transformer.h.(\d+).mlp.c_proj.bias", r"transformer.layers.\1.mlp.fc2.bias", key)
+        return key
+
+    state_dict = OrderedDict((key_mapping_mlp(k), v) for k, v in state_dict.items())
+
+    # Attention
+    for d in range(config.num_hidden_layers):
+        Wqkv = state_dict.pop(f"transformer.h.{d}.attn.c_attn.weight")
+        state_dict[f"transformer.layers.{d}.mixer.Wqkv.weight"] = Wqkv.t()
+        Wout = state_dict.pop(f"transformer.h.{d}.attn.c_proj.weight")
+        state_dict[f"transformer.layers.{d}.mixer.out_proj.weight"] = Wout.t()
+    state_dict.pop(f"transformer.relative_pe.slopes")  # We don't store the Alibi slopes
+
+    def key_mapping_attn(key):
+        key = re.sub(r"^transformer.h.(\d+).attn.c_attn.bias", r"transformer.layers.\1.mixer.Wqkv.bias", key)
+        key = re.sub(
+            r"^transformer.h.(\d+).attn.c_proj.bias", r"transformer.layers.\1.mixer.out_proj.bias", key
+        )
+        return key
+
+    state_dict = OrderedDict((key_mapping_attn(k), v) for k, v in state_dict.items())
+
+    return state_dict
+
+
+def btlm_config_to_gpt2_config(btlm_config: PretrainedConfig) -> GPT2Config:
+    return GPT2Config(
+        vocab_size=btlm_config.vocab_size,
+        n_positions=0 if btlm_config.position_embedding_type == "alibi" else btlm_config.n_positions,
+        n_embd=btlm_config.hidden_size,
+        n_layer=btlm_config.num_hidden_layers,
+        n_head=btlm_config.num_attention_heads,
+        n_inner=btlm_config.n_inner,
+        activation_function=btlm_config.activation_function,
+        resid_pdrop=btlm_config.resid_pdrop,
+        embd_pdrop=btlm_config.embd_pdrop,
+        attn_pdrop=btlm_config.attn_pdrop,
+        layer_norm_epsilon=btlm_config.layer_norm_epsilon,
+        initializer_range=btlm_config.initializer_range,
+        bos_token_id=btlm_config.bos_token_id,
+        eos_token_id=btlm_config.eos_token_id,
+        # These are new arguments not in the original GPT2Config
+        use_alibi=btlm_config.position_embedding_type == "alibi",
+        use_flash_attn=btlm_config.position_embedding_type == "alibi",  # Alibi code path requires flash_attn
+        mup_width_scale=btlm_config.mup_width_scale,
+        mup_embeddings_multiplier=btlm_config.mup_embeddings_scale,
+        mup_output_multiplier=btlm_config.mup_output_alpha,
+        mup_scale_qk_dot_by_d=btlm_config.mup_scale_qk_dot_by_d,
+        mlp_multiple_of=1,
+    )

tests/models/test_btlm.py

+# Copyright (c) 2023, Tri Dao.
+import os
+import time
+from pathlib import Path
+
+import torch
+import pytest
+
+from einops import rearrange
+
+from transformers import AutoConfig, AutoTokenizer, AutoModelForCausalLM
+
+from flash_attn.models.gpt import GPTLMHeadModel
+from flash_attn.models.btlm import btlm_config_to_gpt2_config, remap_state_dict_hf_btlm
+from flash_attn.utils.pretrained import state_dict_from_pretrained
+from flash_attn.utils.generation import update_graph_cache
+
+
+@pytest.mark.parametrize("model_name", ["cerebras/btlm-3b-8k-base"])
+def test_btlm_state_dict(model_name):
+    config = btlm_config_to_gpt2_config(
+        AutoConfig.from_pretrained(model_name, trust_remote_code=True)
+    )
+    pretrained_state_dict = remap_state_dict_hf_btlm(
+        state_dict_from_pretrained(model_name), config
+    )
+    model = GPTLMHeadModel(config, device="meta")  # Without device='meta' init is very slow
+    state_dict = model.state_dict()
+    assert len(state_dict.keys()) == len(pretrained_state_dict.keys())
+    assert state_dict.keys() == pretrained_state_dict.keys()
+    for k in state_dict.keys():
+        assert state_dict[k].shape == pretrained_state_dict[k].shape
+
+
+@pytest.mark.parametrize("model_name", ["cerebras/btlm-3b-8k-base"])
+def test_btlm_optimized(model_name):
+    """Check that our implementation of Btlm (with all optimizations enabled) matches the
+    HF implementation: the output of our forward pass in fp16 should be around the same as the HF
+    forward pass in fp16, when compared to the HF forward pass in fp32.
+    """
+    dtype = torch.float16
+    device = "cuda"
+    config = btlm_config_to_gpt2_config(
+        AutoConfig.from_pretrained(model_name, trust_remote_code=True)
+    )
+    config.fused_bias_fc = True
+    config.fused_dropout_add_ln = True
+    config.residual_in_fp32 = True
+
+    pretrained_state_dict = remap_state_dict_hf_btlm(
+        state_dict_from_pretrained(model_name), config
+    )
+    model = GPTLMHeadModel(config, device=device, dtype=dtype)
+    model.load_state_dict(pretrained_state_dict)
+    model.eval()
+
+    torch.manual_seed(0)
+    batch_size = 2
+    max_seqlen = 256
+    seqlens = torch.randint(max_seqlen // 2, max_seqlen + 1, (batch_size,), device=device)
+    input_ids = torch.randint(
+        0, config.vocab_size, (batch_size, max_seqlen), dtype=torch.long, device=device
+    )
+    with torch.no_grad():
+        out = model.transformer(input_ids)
+        logits = model(input_ids).logits
+    del model
+
+    # Without device_map, the model is loaded on the CPU, which is very slow
+    # Need auto here since the 13B fp32 model doesn't fit in memory on a A100 40GB
+    model_ref = AutoModelForCausalLM.from_pretrained(
+        model_name, device_map="auto", trust_remote_code=True
+    )
+    model_ref.eval()
+    with torch.no_grad():
+        out_ref = model_ref.transformer(input_ids).last_hidden_state.to(device=device)
+        logits_ref = model_ref(input_ids).logits.to(device=device)
+    del model_ref
+
+    model_hf = AutoModelForCausalLM.from_pretrained(
+        model_name,
+        torch_dtype=dtype,
+        device_map={"": device},
+        trust_remote_code=True,
+    )
+    model_hf.eval()
+    with torch.no_grad():
+        out_hf = model_hf.transformer(input_ids).last_hidden_state
+        logits_hf = model_hf(input_ids).logits
+    del model_hf
+
+    print(f"Output max diff: {(out - out_ref).abs().max().item()}")
+    print(f"Output mean diff: {(out - out_ref).abs().mean().item()}")
+    print(f"HF fp16 max diff: {(out_hf - out_ref).abs().max().item()}")
+    print(f"HF fp16 mean diff: {(out_hf - out_ref).abs().mean().item()}")
+    assert (out - out_ref).abs().max().item() < 3 * (out_hf - out_ref).abs().max().item()
+
+    print(f"Logits max diff: {(logits - logits_ref).abs().max().item()}")
+    print(f"Logits mean diff: {(logits - logits_ref).abs().mean().item()}")
+    print(f"HF fp16 max diff: {(logits_hf - logits_ref).abs().max().item()}")
+    print(f"HF fp16 mean diff: {(logits_hf - logits_ref).abs().mean().item()}")
+    assert (logits - logits_ref).abs().max().item() < 3 * (
+        logits_hf - logits_ref
+    ).abs().max().item()
+
+
+@pytest.mark.parametrize("model_name", ["cerebras/btlm-3b-8k-base"])
+def test_btlm_generation(model_name):
+    dtype = torch.float16
+    device = "cuda"
+    config = btlm_config_to_gpt2_config(
+        AutoConfig.from_pretrained(model_name, trust_remote_code=True)
+    )
+    config.fused_bias_fc = True
+    config.fused_dropout_add_ln = True
+    config.residual_in_fp32 = True
+
+    tokenizer = AutoTokenizer.from_pretrained(model_name, trust_remote_code=True)
+    eos_token_id = tokenizer.eos_token_id
+
+    torch.manual_seed(0)
+    batch_size = 1
+    seqlen = 2048
+    max_length = 2048 + 150
+    input_ids = torch.randint(
+        0, config.vocab_size, (batch_size, seqlen), dtype=torch.long, device=device
+    )
+
+    model_hf = AutoModelForCausalLM.from_pretrained(
+        model_name, torch_dtype=dtype, device_map={"": device}, trust_remote_code=True
+    )
+    model_hf.eval()
+    print("HF fp16")
+    torch.cuda.synchronize()
+    start = time.time()
+    out_hf = model_hf.generate(
+        input_ids=input_ids,
+        max_length=max_length,
+        return_dict_in_generate=True,
+        output_scores=True,
+    )
+    torch.cuda.synchronize()
+    print(f"Prompt processing + decoding time: {(time.time() - start) * 1000:.0f}ms")
+    del model_hf
+
+    # Need auto here since the 13B fp32 model doesn't fit in memory on a A100 40GB
+    model_ref = AutoModelForCausalLM.from_pretrained(
+        model_name, device_map="auto", trust_remote_code=True
+    )
+    model_ref.eval()
+    with torch.no_grad():
+        logits_ref = model_ref(out_hf.sequences).logits[:, (seqlen - 1) : -1].to(device=device)
+    del model_ref
+
+    pretrained_state_dict = remap_state_dict_hf_btlm(
+        state_dict_from_pretrained(model_name), config
+    )
+    model = GPTLMHeadModel(config, device=device, dtype=dtype)
+    model.load_state_dict(pretrained_state_dict)
+    model.eval()
+
+    model(input_ids)  # Warm up
+    print("Without CUDA graph")
+    torch.cuda.synchronize()
+    start = time.time()
+    out = model.generate(
+        input_ids=input_ids,
+        max_length=max_length,
+        eos_token_id=eos_token_id,
+        return_dict_in_generate=True,
+        output_scores=True,
+        enable_timing=True,
+        teacher_outputs=out_hf.sequences,
+    )
+    torch.cuda.synchronize()
+    print(f"Prompt processing + decoding time: {(time.time() - start) * 1000:.0f}ms")
+
+    # Capture graph outside the timing loop
+    batch_size, seqlen_og = input_ids.shape
+    model._decoding_cache = update_graph_cache(model, None, batch_size, seqlen_og, max_length)
+    print("With CUDA graph")
+    torch.cuda.synchronize()
+    start = time.time()
+    out_cg = model.generate(
+        input_ids=input_ids,
+        max_length=max_length,
+        cg=True,
+        return_dict_in_generate=True,
+        output_scores=True,
+        enable_timing=True,
+        teacher_outputs=out_hf.sequences,
+    )
+    torch.cuda.synchronize()
+    print(f"Prompt processing + decoding time: {(time.time() - start) * 1000:.0f}ms")
+
+    with torch.no_grad():
+        logits_parallel = model(out_hf.sequences).logits[:, (seqlen - 1) : -1]
+    logits_hf = torch.stack(out_hf.scores, dim=1)
+    logits = torch.stack(out.scores, dim=1)
+    logits_cg = torch.stack(out_cg.scores, dim=1)
+
+    del model
+
+    hf_error = (logits_hf - logits_ref).abs().max().item()
+
+    print(f"HF fp16 logits max diff: {hf_error}")
+    print(f"Logits max diff: {(logits - logits_ref).abs().max().item() }")
+    print(f"Logits CG max diff: {(logits_cg - logits_ref).abs().max().item() }")
+
+    assert (logits_parallel - logits_ref).abs().max().item() < 2 * hf_error
+    assert (logits - logits_ref).abs().max().item() < 2 * hf_error
+    assert torch.equal(logits_cg, logits)
+
+