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[example] Added gemma support #115

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[example] Added gemma support #115

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ef055fc
Added gemma support
Chillee Feb 28, 2024
ae2f82d
Added gemma-7b performance
Chillee Mar 7, 2024
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23 changes: 13 additions & 10 deletions model.py
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Original file line number Diff line number Diff line change
Expand Up @@ -26,7 +26,7 @@ class ModelArgs:
dim: int = 4096
intermediate_size: int = None
n_local_heads: int = -1
head_dim: int = 64
head_dim: int = None
rope_base: float = 10000
norm_eps: float = 1e-5

Expand All @@ -37,7 +37,8 @@ def __post_init__(self):
hidden_dim = 4 * self.dim
n_hidden = int(2 * hidden_dim / 3)
self.intermediate_size = find_multiple(n_hidden, 256)
self.head_dim = self.dim // self.n_head
if self.head_dim is None:
self.head_dim = self.dim // self.n_head

@classmethod
def from_name(cls, name: str):
Expand All @@ -50,6 +51,8 @@ def from_name(cls, name: str):


transformer_configs = {
"gemma-2b": dict(dim=2048, vocab_size=256000, n_layer=18, n_head=8, n_local_heads=1, intermediate_size=16384),
"gemma-7b": dict(dim=3072, vocab_size=256000, n_layer=28, n_head=16, n_local_heads=16, intermediate_size=24576, head_dim=256),
"CodeLlama-7b-Python-hf": dict(block_size=16384, vocab_size=32000, n_layer=32, dim = 4096, rope_base=1000000),
"7B": dict(n_layer=32, n_head=32, dim=4096),
"13B": dict(n_layer=40, n_head=40, dim=5120),
Expand Down Expand Up @@ -94,21 +97,21 @@ def __init__(self, config: ModelArgs) -> None:
def setup_caches(self, max_batch_size, max_seq_length):
if self.max_seq_length >= max_seq_length and self.max_batch_size >= max_batch_size:
return
head_dim = self.config.dim // self.config.n_head
max_seq_length = find_multiple(max_seq_length, 8)
self.max_seq_length = max_seq_length
self.max_batch_size = max_batch_size
for b in self.layers:
b.attention.kv_cache = KVCache(max_batch_size, max_seq_length, self.config.n_local_heads, head_dim)
b.attention.kv_cache = KVCache(max_batch_size, max_seq_length, self.config.n_local_heads, self.config.head_dim)

self.freqs_cis = precompute_freqs_cis(self.config.block_size, self.config.dim // self.config.n_head, self.config.rope_base)
self.freqs_cis = precompute_freqs_cis(self.config.block_size, self.config.head_dim, self.config.rope_base)
self.causal_mask = torch.tril(torch.ones(self.max_seq_length, self.max_seq_length, dtype=torch.bool))

def forward(self, idx: Tensor, input_pos: Optional[Tensor] = None) -> Tensor:
assert self.freqs_cis is not None, "Caches must be initialized first"
mask = self.causal_mask[None, None, input_pos]
freqs_cis = self.freqs_cis[input_pos]
x = self.tok_embeddings(idx)
x = (self.config.dim ** 0.5) * x

for i, layer in enumerate(self.layers):
x = layer(x, input_pos, freqs_cis, mask)
Expand Down Expand Up @@ -143,7 +146,7 @@ def __init__(self, config: ModelArgs):
total_head_dim = (config.n_head + 2 * config.n_local_heads) * config.head_dim
# key, query, value projections for all heads, but in a batch
self.wqkv = nn.Linear(config.dim, total_head_dim, bias=False)
self.wo = nn.Linear(config.dim, config.dim, bias=False)
self.wo = nn.Linear(config.n_head * config.head_dim, config.dim, bias=False)
self.kv_cache = None

self.n_head = config.n_head
Expand All @@ -163,7 +166,7 @@ def forward(self, x: Tensor, freqs_cis: Tensor, mask: Tensor, input_pos: Optiona
bsz, seqlen, _ = x.shape

kv_size = self.n_local_heads * self.head_dim
q, k, v = self.wqkv(x).split([self.dim, kv_size, kv_size], dim=-1)
q, k, v = self.wqkv(x).split([self.n_head * self.head_dim, kv_size, kv_size], dim=-1)

q = q.view(bsz, seqlen, self.n_head, self.head_dim)
k = k.view(bsz, seqlen, self.n_local_heads, self.head_dim)
Expand All @@ -181,7 +184,7 @@ def forward(self, x: Tensor, freqs_cis: Tensor, mask: Tensor, input_pos: Optiona
v = v.repeat_interleave(self.n_head // self.n_local_heads, dim=1)
y = F.scaled_dot_product_attention(q, k, v, attn_mask=mask, dropout_p=0.0)

y = y.transpose(1, 2).contiguous().view(bsz, seqlen, self.dim)
y = y.transpose(1, 2).contiguous().view(bsz, seqlen, self.n_head * self.head_dim)

y = self.wo(y)
return y
Expand All @@ -195,7 +198,7 @@ def __init__(self, config: ModelArgs) -> None:
self.w2 = nn.Linear(config.intermediate_size, config.dim, bias=False)

def forward(self, x: Tensor) -> Tensor:
return self.w2(F.silu(self.w1(x)) * self.w3(x))
return self.w2(F.gelu(self.w1(x), approximate="tanh") * self.w3(x))


class RMSNorm(nn.Module):
Expand All @@ -209,7 +212,7 @@ def _norm(self, x):

def forward(self, x: Tensor) -> Tensor:
output = self._norm(x.float()).type_as(x)
return output * self.weight
return output * (1 + self.weight)


def precompute_freqs_cis(
Expand Down
10 changes: 8 additions & 2 deletions scripts/convert_hf_checkpoint.py
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Original file line number Diff line number Diff line change
Expand Up @@ -30,8 +30,10 @@ def convert_hf_checkpoint(
config = ModelArgs.from_name(model_name)
print(f"Model config {config.__dict__}")

from safetensors import safe_open

# Load the json file containing weight mapping
model_map_json = checkpoint_dir / "pytorch_model.bin.index.json"
model_map_json = checkpoint_dir / "model.safetensors.index.json"

assert model_map_json.is_file()

Expand Down Expand Up @@ -65,7 +67,8 @@ def permute(w, n_head):

merged_result = {}
for file in sorted(bin_files):
state_dict = torch.load(str(file), map_location="cpu", mmap=True, weights_only=True)
state_dict = safe_open(str(file), framework="pt", device='cpu')
state_dict = {k: state_dict.get_tensor(k) for k in state_dict.keys()}
merged_result.update(state_dict)
final_result = {}
for key, value in merged_result.items():
Expand All @@ -92,6 +95,9 @@ def permute(w, n_head):
del final_result[key]
del final_result[key.replace("wq", "wk")]
del final_result[key.replace("wq", "wv")]
if "output.weight" not in final_result:
final_result["output.weight"] = final_result["tok_embeddings.weight"]

print(f"Saving checkpoint to {checkpoint_dir / 'model.pth'}")
torch.save(final_result, checkpoint_dir / "model.pth")

Expand Down