# Copyright 2024 X.AI Corp. # # Licensed under the Apache License, Version 2.0 (the "License"); # you may not use this file except in compliance with the License. # You may obtain a copy of the License at # # http://www.apache.org/licenses/LICENSE-2.0 # # Unless required by applicable law or agreed to in writing, software # distributed under the License is distributed on an "AS IS" BASIS, # WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. # See the License for the specific language governing permissions and # limitations under the License. import functools import logging import re from dataclasses import dataclass from typing import Any, Callable, Dict, List, NamedTuple, Optional, Sequence, Tuple, Union import haiku as hk import jax import jax.experimental.maps import jax.numpy as jnp from jax import config, tree_util from jax.experimental.shard_map import shard_map from jax.lax import with_sharding_constraint as pjit_sharding_constraint from jax.sharding import PartitionSpec from jax.sharding import PartitionSpec as P config.update("jax_spmd_mode", "allow_all") logger = logging.getLogger(__name__) rank_logger = logging.getLogger("rank") @dataclass class QuantizedWeight8bit: weight: jnp.array scales: jnp.array @property def shape(self): return self.weight.shape tree_util.register_pytree_node( QuantizedWeight8bit, lambda qw: ([qw.weight, qw.scales], ()), lambda _, children: QuantizedWeight8bit(children[0], children[1]), ) class TrainingState(NamedTuple): """Container for the training state.""" params: hk.Params def _match(qs, ks): """Return True if regexes in qs match any window of strings in tuple ks.""" # compile regexes and force complete match qts = tuple(map(lambda x: re.compile(x + "$"), qs)) for i in range(len(ks) - len(qs) + 1): matches = [x.match(y) for x, y in zip(qts, ks[i:])] if matches and all(matches): return True return False def with_sharding_constraint(x, constraint): if jax.experimental.maps.thread_resources.env.physical_mesh.empty: return x else: return pjit_sharding_constraint(x, constraint) def cast_bfloat16(x): if x.dtype.kind == "f": return x.astype(jnp.bfloat16) else: return x def ffn_size(emb_size, widening_factor): _ffn_size = int(widening_factor * emb_size) * 2 // 3 _ffn_size = _ffn_size + (8 - _ffn_size) % 8 # ensure it's a multiple of 8 logger.debug(f"emd_size: {emb_size} adjusted ffn_size: {_ffn_size}") return _ffn_size def apply_rules(rules): def _apply_rules(path, value): del value # Unused. path_list = [str(i.key).split("/") for i in path if isinstance(i, jax.tree_util.DictKey)] flattened_path = jax.tree_util.tree_flatten(path_list)[0] for rule, replacement in rules: if _match(rule, flattened_path): if isinstance(replacement, PartitionSpec): if "layer_stack" in flattened_path: replacement = PartitionSpec(None, *replacement) rank_logger.debug(f"Apply {replacement} to {flattened_path} with rule {rule}") return replacement rank_logger.info(f"{flattened_path} no matching found!") return None return _apply_rules TRANSFORMER_PARTITION_RULES = [ # attention (("multi_head_attention", "(query|key|value)", "w"), P("data", "model")), (("multi_head_attention", "(query|key|value)", "b"), P(None)), (("multi_head_attention", "linear", "w"), P("model", "data")), (("multi_head_attention", "linear", "b"), P(None)), # mlp ((r"decoder_layer_[0-9]+", "linear", "w"), P("data", "model")), ((r"decoder_layer_[0-9]+", "linear", "b"), P(None)), ((r"decoder_layer_[0-9]+", "linear_v", "w"), P("data", "model")), ((r"decoder_layer_[0-9]+", "linear_v", "b"), P(None)), ( (r"decoder_layer_[0-9]+", "linear_1", "w"), P( "model", "data", ), ), ((r"decoder_layer_[0-9]+", "linear_1", "b"), P(None)), # layer norms ((r"decoder_layer_[0-9]+", "layer_norm", "offset"), P(None)), ((r"decoder_layer_[0-9]+", "layer_norm", "scale"), P(None)), ((r"decoder_layer_[0-9]+", "layer_norm_1", "offset"), P(None)), ((r"decoder_layer_[0-9]+", "layer_norm_1", "scale"), P(None)), # rms norms ((r"decoder_layer_[0-9]+", "rms_norm", "scale"), P(None)), ((r"decoder_layer_[0-9]+", "rms_norm_1", "scale"), P(None)), ((r"decoder_layer_[0-9]+", "rms_norm_2", "scale"), P(None)), ((r"decoder_layer_[0-9]+", "rms_norm_3", "scale"), P(None)), # router (("router", "w"), P("data")), # moe mlp (("moe", "linear", "w"), P(None, "data", "model")), (("moe", "linear", "b"), P(None)), (("moe", "linear_v", "w"), P(None, "data", "model")), (("moe", "linear_v", "b"), P(None)), (("moe", "linear_1", "w"), P(None, "model", "data")), (("moe", "linear_1", "b"), P(None)), # layer norms (("moe", "layer_norm", "offset"), P(None)), (("moe", "layer_norm", "scale"), P(None)), (("moe", "layer_norm_1", "offset"), P(None)), (("moe", "layer_norm_1", "scale"), P(None)), # rms norms (("moe", "rms_norm", "scale"), P(None)), (("moe", "rms_norm_1", "scale"), P(None)), (("moe", "rms_norm_2", "scale"), P(None)), (("moe", "rms_norm_3", "scale"), P(None)), ] LM_PARTITION_RULES = [ # Embedding layer. ( ("language_model", "positional_embeddings"), P(None, ("data", "model")), ), ( ("language_model", "in_out_embed", "embeddings"), P(None, ("data", "model")), ), # Final RMSNorm. (("language_model", "rms_norm"), P(None)), ] TOP_K = 8 class KVMemory(NamedTuple): k: Optional[jax.Array] v: Optional[jax.Array] step: Optional[jax.Array] def init_layer_memories( batch_size: int, sequence_len: int, num_kv_heads: int, key_size: int, num_layers: int, step: Optional[jax.Array] = None, dtype=jnp.bfloat16, ): return [ KVMemory( k=jnp.zeros((batch_size, sequence_len, num_kv_heads, key_size), dtype=dtype), v=jnp.zeros((batch_size, sequence_len, num_kv_heads, key_size), dtype=dtype), step=step, ) for _ in range(num_layers) ] class Memory(NamedTuple): # Self-attention key/value cache. layers: List[KVMemory] class Router(hk.Module): def __init__( self, num_selected_experts: int, data_axis: Union[str, Tuple[str, ...]] = "data", model_axis: Union[str, Tuple[str, ...]] = "model", shard_activations: bool = False, mesh: Any = None, name: str = "router", ): super().__init__(name) self.shard_activations = shard_activations self.data_axis = data_axis self.model_axis = model_axis self.mesh = mesh self.num_selected_experts = num_selected_experts def compute_routing_prob( self, inputs: jax.Array, padding_mask: Optional[jax.Array], num_experts: int ): return self._compute_routing_prob(inputs, padding_mask, num_experts) @hk.transparent def _compute_routing_prob( self, inputs: jax.Array, padding_mask: Optional[jax.Array], num_experts: int, ): # Using fp32 for the routing prob computation. inputs = jax.lax.convert_element_type(inputs, jnp.float32) # [batch_size, seq_len, num_experts] routing_logits = self._router_weights(inputs, num_experts, sharding=P("data")) assert routing_logits.dtype == jnp.float32 routing_probs = jax.nn.softmax(routing_logits) if padding_mask is not None: routing_probs *= padding_mask return routing_probs, routing_logits, 0 @hk.transparent def _router_weights( self, x: jax.Array, num_experts: int, sharding: Optional[P] = None, ): fprop_dtype = x.dtype if not x.shape: raise ValueError("Input must not be scalar.") input_size = self.input_size = x.shape[-1] w = hk.get_parameter( "w", [input_size, num_experts], jnp.float32, init=hk.initializers.Constant(0) ) if sharding: w = with_sharding_constraint(w, sharding) out = jnp.dot(x, w.astype(fprop_dtype)) return out class MoELayer(hk.Module): def __init__( self, num_experts: int, layer_fn: Callable, router: Router, mesh: Any = None, shard_activations: bool = False, data_axis: Union[str, Tuple[str, ...]] = "data", model_axis: Union[str, Tuple[str, ...]] = "model", name: Optional[str] = "moe", ): super().__init__(name) self.num_experts = num_experts self.layer_fn = layer_fn self.router = router self.mesh = mesh self.shard_activations = shard_activations self.data_axis = data_axis self.model_axis = model_axis @hk.transparent def _inference_call(self, inputs: jax.Array, padding_mask: Optional[jax.Array] = None): routing_probs, _, _ = self.router.compute_routing_prob( inputs, padding_mask, self.num_experts ) expert_gate, expert_index = jax.lax.top_k(routing_probs, k=self.router.num_selected_experts) tmp = jnp.reshape(inputs, (inputs.shape[0] * inputs.shape[1], inputs.shape[2])) broad_inputs = jnp.tile(tmp[:, jnp.newaxis, :], (1, self.router.num_selected_experts, 1)) broad_inputs = jnp.reshape( broad_inputs, (broad_inputs.shape[0] * broad_inputs.shape[1], broad_inputs.shape[2]) ) init_fn, _ = hk.transform(self.layer_fn) vmapped_init_fn = jax.vmap(init_fn, in_axes=0, out_axes=0) lifted_init_fn = hk.experimental.transparent_lift(vmapped_init_fn) # Fetch the vmapped params of the DenseBlock. params = lifted_init_fn( jax.random.split(jax.random.PRNGKey(1), self.num_experts), jnp.zeros((self.num_experts, 1, 1, inputs.shape[-1])), ) # Index and prob are in the shape [m, 2] indicating which token assigned to which experts. # b: num_expert # m: token or sequence dim # k: input embed dim # n: output embed dim # e: the number of experts chosen for each token @functools.partial( shard_map, mesh=self.mesh, in_specs=( P(self.data_axis, None), P(None, None, self.model_axis), P(None, None, self.model_axis), P(None), P(None), ), out_specs=P(self.data_axis, self.model_axis), check_rep=False, ) def moe_slow_matmul1(input, weight, scales, index, prob): weight = weight * scales one_hot_indices = jax.nn.one_hot(index.reshape(-1), 8, axis=0) all_expert_output = jnp.einsum("mk,bkn->bmn", input, weight) output = jnp.einsum("bm,bmn->mn", one_hot_indices, all_expert_output) return output @functools.partial( shard_map, mesh=self.mesh, in_specs=( P(self.data_axis, self.model_axis), P(None, self.model_axis, None), P(None, self.model_axis, None), P(None), P(None), ), out_specs=P(self.data_axis, None), check_rep=False, ) def moe_slow_matmul2(input, weight, scales, index, prob): weight = weight * scales one_hot_indices = jax.nn.one_hot(index.reshape(-1), 8, axis=0) all_expert_output = jnp.einsum("mk,bkn->bmn", input, weight) output = jnp.einsum("bm,bmn->mn", one_hot_indices, all_expert_output) return jax.lax.psum(output, axis_name="model") if hasattr(params["linear"]["w"], "scales"): x = moe_slow_matmul1( broad_inputs, params["linear_v"]["w"].weight, params["linear_v"]["w"].scales, expert_index, expert_gate, ) y = moe_slow_matmul1( broad_inputs, params["linear"]["w"].weight, params["linear"]["w"].scales, expert_index, expert_gate, ) y = jax.nn.gelu(y) out = moe_slow_matmul2( x * y, params["linear_1"]["w"].weight, params["linear_1"]["w"].scales, expert_index, expert_gate, ) out = jnp.reshape( out, [ inputs.shape[0], inputs.shape[1], self.router.num_selected_experts, out.shape[-1], ], ) out = expert_gate[:, :, :, None].astype(jnp.bfloat16) * out out = jnp.sum(out, axis=2) out = out.astype(jnp.bfloat16) else: # This is only here so that we can construct a valid init_fn with this code. return inputs return out def __call__(self, inputs: jax.Array, padding_mask: jax.Array): return self._inference_call(inputs) class MHAOutput(NamedTuple): """Outputs of the multi-head attention operation.""" embeddings: jax.Array memory: Any class DecoderOutput(NamedTuple): embeddings: jax.Array memory: Any class TransformerOutput(NamedTuple): embeddings: jax.Array memory: Any @dataclass class TransformerConfig: emb_size: int key_size: int num_q_heads: int num_kv_heads: int num_layers: int vocab_size: int = 128 * 1024 widening_factor: float = 4.0 attn_output_multiplier: float = 1.0 name: Optional[str] = None num_experts: int = -1 capacity_factor: float = 1.0 num_selected_experts: int = 1 init_scale: float = 1.0 shard_activations: bool = False # Used for activation sharding. data_axis: Union[str, Tuple[str, ...]] = "data" model_axis: Union[str, Tuple[str, ...]] = "model" def __post_init__(self): if isinstance(self.data_axis, list): self.data_axis = tuple(self.data_axis) if isinstance(self.model_axis, list): self.model_axis = tuple(self.model_axis) def partition_rules(self): return TRANSFORMER_PARTITION_RULES def make(self, mesh=None) -> "Transformer": data_axis = tuple(self.data_axis) if isinstance(self.data_axis, list) else self.data_axis model_axis = ( tuple(self.model_axis) if isinstance(self.model_axis, list) else self.model_axis ) return Transformer( num_q_heads=self.num_q_heads, num_kv_heads=self.num_kv_heads, widening_factor=self.widening_factor, key_size=self.key_size, init_scale=self.init_scale, mesh=mesh, attn_output_multiplier=self.attn_output_multiplier, shard_activations=self.shard_activations, num_layers=self.num_layers, num_experts=self.num_experts, num_selected_experts=self.num_selected_experts, data_axis=data_axis, model_axis=model_axis, ) def get_memory_sharding(self): return Memory( layers=[ KVMemory( k=P(self.data_axis, self.model_axis), v=P(self.data_axis, self.model_axis), step=P(self.data_axis), ) for _ in range(self.num_layers) ], ) def hk_rms_norm( x: jax.Array, fixed_scale=False, sharding=P(None), ) -> jax.Array: """Applies a unique LayerNorm to x with default settings.""" ln = RMSNorm(axis=-1, create_scale=not fixed_scale, sharding=sharding) return ln(x) def make_attention_mask( query_input: jax.Array, key_input: jax.Array, pairwise_fn: Callable[..., Any] = jnp.multiply, dtype: Any = jnp.bfloat16, ): """Mask-making helper for attention weights. In case of 1d inputs (i.e., `[batch..., len_q]`, `[batch..., len_kv]`, the attention weights will be `[batch..., heads, len_q, len_kv]` and this function will produce `[batch..., 1, len_q, len_kv]`. Args: query_input: a batched, flat input of query_length size key_input: a batched, flat input of key_length size pairwise_fn: broadcasting elementwise comparison function dtype: mask return dtype Returns: A `[batch..., 1, len_q, len_kv]` shaped mask for 1d attention. """ mask = pairwise_fn(jnp.expand_dims(query_input, axis=-1), jnp.expand_dims(key_input, axis=-2)) mask = jnp.expand_dims(mask, axis=-3) return mask.astype(dtype) class Linear(hk.Linear): def __init__( self, output_size: int, with_bias: bool = True, sharding: Optional[P] = None, mesh: Any = None, name: Optional[str] = None, shard_axis: int = 0, ): super().__init__( output_size=output_size, with_bias=with_bias, name=name, ) self.sharding = sharding self.mesh = mesh self.shard_axis = shard_axis def __call__( self, inputs: jax.Array, ) -> jax.Array: """Computes a linear transform of the input.""" fprop_dtype = inputs.dtype if not inputs.shape: raise ValueError("Input must not be scalar.") input_size = self.input_size = inputs.shape[-1] output_size = self.output_size w = hk.get_parameter( "w", [input_size, output_size], jnp.float32, init=hk.initializers.Constant(0) ) if hasattr(w, "scales"): shape = inputs.shape inputs = jnp.reshape(inputs, (-1, shape[-1])) @functools.partial( shard_map, mesh=self.mesh, in_specs=(self.sharding, self.sharding), out_specs=self.sharding, check_rep=False, ) def mul(w, s): return w.astype(s.dtype) * s w = mul(w.weight, w.scales) out = jnp.dot(inputs, w.astype(fprop_dtype)) if self.with_bias: b = hk.get_parameter( "b", [self.output_size], jnp.float32, init=hk.initializers.Constant(0) ) b = jnp.broadcast_to(b, out.shape) out = out + b.astype(fprop_dtype) return out class RMSNorm(hk.RMSNorm): def __init__( self, axis: Union[int, Sequence[int], slice], eps: float = 1e-5, name: Optional[str] = None, create_scale: bool = True, sharding: Optional[P] = None, ): super().__init__(axis, eps, create_scale=create_scale, name=name) self.sharding = sharding def __call__(self, inputs: jax.Array): fprop_dtype = inputs.dtype param_shape = (inputs.shape[-1],) if self.create_scale: scale = hk.get_parameter( "scale", param_shape, dtype=jnp.float32, init=hk.initializers.Constant(0), ) if self.sharding: scale = with_sharding_constraint(scale, self.sharding) scale = jnp.broadcast_to(scale.astype(jnp.float32), inputs.shape) else: scale = 1.0 inputs = inputs.astype(jnp.float32) scale = scale.astype(jnp.float32) mean_squared = jnp.mean(jnp.square(inputs), axis=[-1], keepdims=True) mean_squared = jnp.broadcast_to(mean_squared, inputs.shape) normed_inputs = inputs * jax.lax.rsqrt(mean_squared + self.eps) outputs = scale * normed_inputs return outputs.astype(fprop_dtype) def rotate_half( x: jax.Array, ) -> jax.Array: """Obtain the rotated counterpart of each feature""" x1, x2 = jnp.split(x, 2, axis=-1) return jnp.concatenate((-x2, x1), axis=-1) class RotaryEmbedding(hk.Module): """Applies rotary embeddings (RoPE) to the input sequence tensor, as described in https://arxiv.org/abs/2104.09864. Attributes: dim (int): Dimensionality of the feature vectors base_exponent (int): Base exponent to compute embeddings from """ def __init__( self, dim: int, name: Optional[str] = None, base_exponent: int = 10000, ): super().__init__(name) self.dim = dim self.base_exponent = base_exponent assert self.dim % 2 == 0 def __call__( self, x: jax.Array, seq_dim: int, offset: jax.Array, const_position: Optional[int] = None, t: Optional[jax.Array] = None, ) -> jax.Array: fprop_dtype = x.dtype # Compute the per-dimension frequencies exponents = jnp.arange(0, self.dim, 2, dtype=jnp.float32) inv_freq = jnp.asarray( 1.0 / (self.base_exponent ** (exponents / self.dim)), dtype=jnp.float32 ) if jnp.shape(offset) == (): # Offset can be a scalar or one offset per batch element. offset = jnp.expand_dims(offset, 0) # Compute the per element phase (to pass into sin and cos) if const_position: t = const_position * jnp.ones( ( 1, x.shape[seq_dim], ), dtype=jnp.float32, ) elif t is None: t = jnp.arange(x.shape[seq_dim], dtype=jnp.float32) + jnp.expand_dims(offset, -1) phase = jnp.einsum("bi,j->bij", t, inv_freq) phase = jnp.tile(phase, reps=(1, 2))[:, :, None, :] x = x * jnp.cos(phase) + rotate_half(x) * jnp.sin(phase) x = x.astype(fprop_dtype) return x class MultiHeadAttention(hk.Module): def __init__( self, num_q_heads: int, num_kv_heads: int, key_size: int, *, with_bias: bool = True, value_size: Optional[int] = None, model_size: Optional[int] = None, attn_output_multiplier: 1.0, data_axis: Union[str, Tuple[str, ...]] = "data", model_axis: Union[str, Tuple[str, ...]] = "model", name: Optional[str] = None, ): super().__init__(name=name) self.num_q_heads = num_q_heads self.num_kv_heads = num_kv_heads self.key_size = key_size self.value_size = value_size or key_size self.model_size = model_size or key_size * num_q_heads self.data_axis = data_axis self.model_axis = model_axis self.attn_output_multiplier = attn_output_multiplier self.with_bias = with_bias def __call__( self, query: jax.Array, key: Optional[jax.Array], value: Optional[jax.Array], mask: Optional[jax.Array] = None, kv_memory: Optional[KVMemory] = None, mesh: Any = None, ) -> MHAOutput: # In shape hints below, we suppress the leading dims [...] for brevity. # Hence e.g. [A, B] should be read in every case as [..., A, B]. sequence_length = query.shape[1] projection = self._linear_projection use_memory = False if kv_memory is not None: if kv_memory.k is None: assert kv_memory.v is None assert key is not None assert value is not None else: assert kv_memory.v is not None use_memory = True else: assert key is not None assert value is not None # Check that the keys and values have consistent batch size and sequence length. if not use_memory: assert key.shape[:2] == value.shape[:2], f"key/value shape: {key.shape}/{value.shape}" if mask is not None: assert mask.ndim == 4 assert mask.shape[0] in { 1, query.shape[0], }, f"mask/query shape: {mask.shape}/{query.shape}" if not use_memory: assert key.shape[0] in { 1, query.shape[0], }, f"key/query shape: {key.shape}/{query.shape}" assert mask.shape[1] == 1 assert mask.shape[2] in { 1, query.shape[1], }, f"mask/query shape: {mask.shape}/{query.shape}" if not use_memory: assert mask.shape[3] in { 1, key.shape[1], }, f"mask/query shape: {mask.shape}/{key.shape}" # Compute key/query/values (overload K/Q/V to denote the respective sizes). assert self.num_q_heads % self.num_kv_heads == 0 query_heads = projection( query, self.key_size, self.num_q_heads, name="query", sharding=P("data", "model"), mesh=mesh, ) # [B, T', H, Q=K] new_memory = None key_heads = projection( key, self.key_size, self.num_kv_heads, name="key", sharding=P("data", "model"), mesh=mesh, ) # [B, T, H, K] value_heads = projection( value, self.value_size, self.num_kv_heads, name="value", sharding=P("data", "model"), mesh=mesh, ) # [B, T, H, V] rotate = RotaryEmbedding(dim=self.key_size, base_exponent=int(1e4)) key_heads = rotate(key_heads, seq_dim=1, offset=(kv_memory.step if kv_memory else 0)) query_heads = rotate(query_heads, seq_dim=1, offset=(kv_memory.step if kv_memory else 0)) @functools.partial(jax.vmap) def update_into(mem, start, update): return jax.lax.dynamic_update_slice_in_dim(mem, update, start, axis=0) if kv_memory: if mesh is not None: @functools.partial( shard_map, mesh=mesh, in_specs=( P("data", None, "model"), P("data"), P("data", None, "model"), ), out_specs=P("data", None, "model"), check_rep=False, ) def update_into_shmap(mems, starts, updates): return update_into(mems, starts, updates) key_heads = update_into_shmap(kv_memory.k, kv_memory.step, key_heads) value_heads = update_into_shmap(kv_memory.v, kv_memory.step, value_heads) else: key_heads = update_into(kv_memory.k, kv_memory.step, key_heads) value_heads = update_into(kv_memory.v, kv_memory.step, value_heads) new_step = kv_memory.step + sequence_length memory_mask = jnp.arange(kv_memory.k.shape[1]) < new_step[:, None] memory_mask = memory_mask[:, None, None, :] # [B, H, T, T] if mask is not None: mask = memory_mask * mask else: mask = memory_mask new_memory = KVMemory( k=key_heads, v=value_heads, step=new_step, ) # Add separate dimension for grouped query heads. query_heads = with_sharding_constraint(query_heads, P(self.data_axis, None, "model", None)) key_heads = with_sharding_constraint(key_heads, P(self.data_axis, None, "model", None)) value_heads = with_sharding_constraint(value_heads, P(self.data_axis, None, "model", None)) b, t, h, d = query_heads.shape _, _, kv_h, _ = key_heads.shape assert h % kv_h == 0, f"query_heads {h} must be a multiple of kv_heads {kv_h}" query_heads = jnp.reshape(query_heads, (b, t, kv_h, h // kv_h, d)) query_heads = with_sharding_constraint( query_heads, P(self.data_axis, None, "model", None, None) ) # Compute attention weights. # Attention softmax is always carried out in fp32. attn_logits = jnp.einsum("...thHd,...Thd->...hHtT", query_heads, key_heads).astype( jnp.float32 ) attn_logits *= self.attn_output_multiplier max_attn_val = jnp.array(30.0, dtype=attn_logits.dtype) attn_logits = max_attn_val * jnp.tanh(attn_logits / max_attn_val) mask = mask[:, :, None, :, :] if mask is not None: if mask.ndim != attn_logits.ndim: raise ValueError( f"Mask dimensionality {mask.ndim} must match logits dimensionality " f"{attn_logits.ndim} for {mask.shape}/{attn_logits.shape}." ) attn_logits = jnp.where(mask, attn_logits, -1e30) attn_weights = jax.nn.softmax(attn_logits).astype(query.dtype) # [H, T', T] # Weight the values by the attention and flatten the head vectors. attn = jnp.einsum("...hHtT,...Thd->...thHd", attn_weights, value_heads) attn = with_sharding_constraint(attn, P(self.data_axis, None, "model", None, None)) leading_dims = attn.shape[:2] attn = jnp.reshape(attn, (*leading_dims, -1)) # [T', H*V] attn = with_sharding_constraint(attn, P(self.data_axis, None, "model")) # Apply another projection to get the final embeddings. final_projection = Linear( self.model_size, with_bias=False, sharding=P("model", "data"), mesh=mesh, ) return MHAOutput(final_projection(attn), new_memory) @hk.transparent def _linear_projection( self, x: jax.Array, head_size: int, num_heads: int, sharding: Optional[P] = None, name: Optional[str] = None, mesh: Any = None, ) -> jax.Array: y = Linear( num_heads * head_size, with_bias=False, name=name, sharding=sharding, mesh=mesh, )(x) *leading_dims, _ = x.shape return y.reshape((*leading_dims, num_heads, head_size)) @dataclass class MHABlock(hk.Module): """A MHA Block""" num_q_heads: int num_kv_heads: int key_size: int attn_output_multiplier: float = 1.0 mesh: Any = None data_axis: Union[str, Tuple[str, ...]] = "data" model_axis: Union[str, Tuple[str, ...]] = "model" @hk.transparent def __call__( self, inputs: jax.Array, # [B, T, D] mask: jax.Array, # [B, 1, T, T] or [B, 1, 1, T] or B[1, 1, 1, 1] layer_memory: Optional[KVMemory], ) -> MHAOutput: _, _, model_size = inputs.shape assert mask.ndim == 4, f"shape: {mask.shape}" assert mask.shape[2] in {1, inputs.shape[1]}, str(mask.shape) assert mask.shape[3] in {1, inputs.shape[1]}, str(mask.shape) side_input = inputs def attn_block(query, key, value, mask, memory) -> MHAOutput: return MultiHeadAttention( num_q_heads=self.num_q_heads, num_kv_heads=self.num_kv_heads, key_size=self.key_size, model_size=model_size, data_axis=self.data_axis, model_axis=self.model_axis, attn_output_multiplier=self.attn_output_multiplier, )( query, key, value, mask, memory, mesh=self.mesh, ) attn_output = attn_block(inputs, side_input, side_input, mask, layer_memory) h_attn = attn_output.embeddings return attn_output._replace(embeddings=h_attn) @dataclass class DenseBlock(hk.Module): num_q_heads: int num_kv_heads: int key_size: int widening_factor: float = 4.0 sharding_constraint: bool = False mesh: Any = None @hk.transparent def __call__( self, inputs: jax.Array, # [B, T, D] ) -> jax.Array: # [B, T, D] _, _, model_size = inputs.shape h_v = Linear( ffn_size( model_size, self.widening_factor, ), with_bias=False, mesh=self.mesh, sharding=P("data", "model"), name="linear_v", )(inputs) h_w1 = jax.nn.gelu( Linear( ffn_size( model_size, self.widening_factor, ), with_bias=False, mesh=self.mesh, sharding=P("data", "model"), )(inputs) ) h_dense = Linear( model_size, with_bias=False, sharding=P("model", "data"), mesh=self.mesh, shard_axis=1, )(h_w1 * h_v) return h_dense @dataclass class DecoderLayer(hk.Module): """A transformer stack.""" num_q_heads: int num_kv_heads: int key_size: int num_layers: int # MoE. num_experts: int layer_index: Optional[int] = None num_selected_experts: int = 1 widening_factor: float = 4.0 name: Optional[str] = None data_axis: Union[str, Tuple[str, ...]] = "data" model_axis: Union[str, Tuple[str, ...]] = "model" shard_activations: bool = False attn_output_multiplier: float = 1.0 mesh: Any = None def __call__( self, inputs: jax.Array, # [B, T, D] mask: jax.Array, # [B, 1, T, T] or [B, 1, 1, T] padding_mask: Optional[jax.Array], layer_memory: Optional[KVMemory], ) -> DecoderOutput: """Transforms input embedding sequences to output embedding sequences.""" def layer_norm(x): return hk_rms_norm(x) if self.shard_activations: sharding = P(self.data_axis, None, self.model_axis) else: sharding = P(self.data_axis, None) h = with_sharding_constraint(inputs, sharding) attn_output = MHABlock( num_q_heads=self.num_q_heads, num_kv_heads=self.num_kv_heads, key_size=self.key_size, attn_output_multiplier=self.attn_output_multiplier, mesh=self.mesh, data_axis=self.data_axis, model_axis=self.model_axis, )(layer_norm(h), mask, layer_memory) h_attn = attn_output.embeddings h_attn = layer_norm(h_attn) h += h_attn h = with_sharding_constraint(h, sharding) def base_dense_block(h): h = DenseBlock( num_q_heads=self.num_q_heads, num_kv_heads=self.num_kv_heads, key_size=self.key_size, widening_factor=self.widening_factor, sharding_constraint=False, mesh=self.mesh, )(h) return h if self.num_experts > 1: rank_logger.debug("Using MoE!") router = Router( num_selected_experts=self.num_selected_experts, shard_activations=self.shard_activations, data_axis=self.data_axis, model_axis=self.model_axis, mesh=self.mesh, ) h_dense = MoELayer( num_experts=self.num_experts, mesh=self.mesh, layer_fn=base_dense_block, router=router, shard_activations=self.shard_activations, data_axis=self.data_axis, model_axis=self.model_axis, )(layer_norm(h), padding_mask) else: h_dense = base_dense_block(layer_norm(h)) h_dense = layer_norm(h_dense) h += h_dense h = with_sharding_constraint(h, sharding) return DecoderOutput( embeddings=h, memory=attn_output.memory, ) class LanguageModelOutput(NamedTuple): logits: jax.Array model_state: Any class InOutEmbed(hk.Embed): """Module for embedding tokens in a low-dimensional space.""" def __init__( self, vocab_size: Optional[int] = None, embed_dim: Optional[int] = None, sharding: Optional[P] = None, name: Optional[str] = None, ): super().__init__( vocab_size=vocab_size, embed_dim=embed_dim, name=name, ) self.sharding = sharding @property def embeddings(self): embed_mat = hk.get_parameter( "embeddings", [self.vocab_size, self.embed_dim], dtype=jnp.float32, init=hk.initializers.Constant(0), ) if self.sharding: embed_mat = with_sharding_constraint(embed_mat, self.sharding) return embed_mat def decode( self, inputs: jax.Array, ) -> jax.Array: return jnp.dot(inputs, self.embeddings.T.astype(inputs.dtype)) @dataclass class LanguageModelConfig: """An autoregressive transformer-based language model.""" model: Optional[TransformerConfig] vocab_size: int pad_token: int eos_token: int sequence_len: int model_size: int = 0 embedding_init_scale: float = 1.0 embedding_multiplier_scale: float = 1.0 output_multiplier_scale: float = 1.0 name: Optional[str] = None fprop_dtype: Any = jnp.bfloat16 model_type: Optional[str] = None init_scale_override: Optional[float] = None shard_embeddings: bool = True _initialized = False def initialize(self): # We cannot specify [] as a default value (it is mutable), hence None. model_config = self.model assert self.init_scale_override is None, ( "Overriding model initialize scale is supported only for predefined models." ) if self.model_size == 0: self.model_size = model_config.emb_size assert self.model is not None, "Model could not be initialized." self._initialized = True return self def make(self, *args, **kwargs): if not self._initialized: logger.warning( f"LanguageModel {self.name} is not initialized. Initializing for one replica." ) self.initialize() return LanguageModel( model=self.model.make(*args, **kwargs), config=self, fprop_dtype=self.fprop_dtype, mesh=kwargs.get("mesh", None), ) def partition_rules(self): return LM_PARTITION_RULES + self.model.partition_rules() def layer_norm(x, model): return hk_rms_norm(x) @dataclass class LanguageModel(hk.Module): """An autoregressive transformer-based language model.""" model: "Transformer" config: LanguageModelConfig fprop_dtype: Any = jnp.bfloat16 name: Optional[str] = None mesh: Any = None def __call__( self, tokens: jax.Array, memory: Optional[Memory] = None, *, batch: Dict[str, jax.Array] = {}, last_hid_only: bool = False, length: Optional[jax.Array] = None, ) -> LanguageModelOutput: """Forward pass, producing a sequence of logits.""" del batch # Unused. config = self.config input_mask = jnp.greater(tokens, config.pad_token) # Embed the input tokens and positions. in_out_embed = InOutEmbed( self.config.vocab_size, embed_dim=self.config.model_size, sharding=P(None, ("data", "model")), ) input_embeddings = in_out_embed(tokens).astype(config.fprop_dtype) input_embeddings = with_sharding_constraint( input_embeddings, P("data", None, self.model.model_axis) ) input_embeddings *= config.embedding_multiplier_scale model_output = self.model( input_embeddings, input_mask, memory=memory, ) # [B, T, D] embeddings, model_state = model_output.embeddings, model_output.memory if self.model.shard_activations: embeddings = with_sharding_constraint( embeddings, P("data", None, self.model.model_axis) ) else: embeddings = with_sharding_constraint(embeddings, P("data", None)) rank_logger.debug(f"Final embedding shape: {embeddings.shape}") embeddings = layer_norm(embeddings, self.model) assert embeddings.dtype == self.fprop_dtype if last_hid_only: last_step = jnp.maximum(jnp.sum(input_mask.astype(jnp.int32), axis=1) - 1, 0) last_hid = jax.vmap(lambda x, i: x[i], in_axes=0, out_axes=0)(embeddings, last_step) return last_hid if length is not None: last_step = jnp.maximum(length.astype(jnp.int32) - 1, 0) embeddings = jax.vmap(lambda x, i: x[i], in_axes=0, out_axes=0)(embeddings, last_step) embeddings = jnp.expand_dims(embeddings, axis=1) # Decode the embeddings (here, we use tied weights). rank_logger.info(embeddings.shape) out = in_out_embed.decode(embeddings) rank_logger.info(out.shape) out *= config.output_multiplier_scale if self.model.shard_activations: out = with_sharding_constraint(out, P("data", None, self.model.model_axis)) else: out = with_sharding_constraint(out, P("data", None)) return LanguageModelOutput( logits=out, model_state=model_state, ) def init_memory(self, batch_size: int, seq_len: int, dtype=jnp.bfloat16): return self.model.init_memory(batch_size=batch_size, sequence_len=seq_len, dtype=dtype) def prefill_memory(self, prompts, memory): # Pad to the left and right align? # Basically assume prompt is already padded model_output = self(prompts, memory=memory) return model_output.logits, model_output.model_state @dataclass class Transformer(hk.Module): """A transformer stack.""" num_q_heads: int num_kv_heads: int key_size: int widening_factor: float init_scale: float mesh: Any attn_output_multiplier: float shard_activations: bool num_layers: int # MoE num_experts: int num_selected_experts: int name: Optional[str] = None # Used for activation sharding data_axis: Union[str, Tuple[str, ...]] = "data" model_axis: Union[str, Tuple[str, ...]] = "model" def init_memory(self, batch_size: int, sequence_len: int, dtype=jnp.bfloat16): return Memory( layers=init_layer_memories( batch_size, sequence_len, self.num_kv_heads, self.key_size, self.num_layers, step=jnp.zeros(batch_size, dtype=jnp.int32), dtype=dtype, ), ) def __call__( self, embeddings: jax.Array, # [B, T, D] mask: jax.Array, # [B, T] memory: Optional[Memory], ) -> TransformerOutput: """Transforms input embedding sequences to output embedding sequences.""" fprop_dtype = embeddings.dtype _, seq_len, model_size = embeddings.shape padding_mask = mask.copy() mask = mask[:, None, None, :] # [B, H=1, T'=1, T] # Compute causal mask for autoregressive sequence modelling. causal_mask = jnp.tril(jnp.ones((1, 1, seq_len, seq_len))).astype( fprop_dtype ) # [B=1, H=1, T, T] mask = mask * causal_mask # [B, H=1, T, T] h = embeddings kv_memories = [] def block( h, mask, padding_mask, memory, layer_index: Optional[int] = None, widening_factor: Optional[int] = None, name: Optional[str] = None, ) -> DecoderOutput: return DecoderLayer( num_q_heads=self.num_q_heads, num_kv_heads=self.num_kv_heads, key_size=self.key_size, widening_factor=widening_factor or self.widening_factor, num_layers=self.num_layers, mesh=self.mesh, data_axis=self.data_axis, model_axis=self.model_axis, attn_output_multiplier=self.attn_output_multiplier, shard_activations=self.shard_activations, # MoE. num_experts=self.num_experts, num_selected_experts=self.num_selected_experts, name=name, layer_index=layer_index, )( h, mask, padding_mask, memory, ) for i in range(self.num_layers): decoder_output = block( h, mask, padding_mask, memory.layers[i] if memory else None, layer_index=i, name=f"decoder_layer_{i}", ) h, new_kv_memory = ( decoder_output.embeddings, decoder_output.memory, ) kv_memories.append(new_kv_memory) return TransformerOutput( embeddings=h, memory=Memory(layers=kv_memories), )