Removed non-flash classes

configuration_phi.py

@@ -29,8 +29,6 @@ class PhiConfig(PretrainedConfig):
         n_head_kv: Optional[int] = None,
         rotary_dim: Optional[int] = 32,
         activation_function: Optional[str] = "gelu_new",
-        flash_attn: bool = False,
-        flash_rotary: bool = False,
         fused_dense: bool = False,
         attn_pdrop: float = 0.0,
         embd_pdrop: float = 0.0,
@@ -50,8 +48,6 @@ class PhiConfig(PretrainedConfig):
         self.n_head_kv = n_head_kv
         self.rotary_dim = min(rotary_dim, n_embd // n_head)
         self.activation_function = activation_function
-        self.flash_attn = flash_attn
-        self.flash_rotary = flash_rotary
         self.fused_dense = fused_dense
         self.attn_pdrop = attn_pdrop
         self.embd_pdrop = embd_pdrop

modeling_phi.py

@@ -19,16 +19,10 @@ from transformers.modeling_outputs import CausalLMOutputWithPast
 
 from .configuration_phi import PhiConfig
 
-try:
-    from flash_attn.bert_padding import pad_input, unpad_input
-    from flash_attn.layers.rotary import RotaryEmbedding as FlashRotaryEmbedding
-    from flash_attn.modules.mha import FlashCrossAttention, FlashSelfAttention
-    from flash_attn.ops.fused_dense import FusedDense
-except:
-    pad_input, unpad_input = None, None
-    FlashRotaryEmbedding = None
-    FlashSelfAttention, FlashCrossAttention = None, None
-    FusedDense = None
+from flash_attn.bert_padding import pad_input, unpad_input
+from flash_attn.layers.rotary import RotaryEmbedding as FlashRotaryEmbedding
+from flash_attn.modules.mha import FlashCrossAttention, FlashSelfAttention
+from flash_attn.ops.fused_dense import FusedDense
 
 
 @dataclass
@@ -168,128 +162,6 @@ def _apply_rotary_emb_qkv(
     )
 
 
-class RotaryEmbedding(nn.Module):
-    """Rotary positional embedding (RoPE).
-
-    Reference:
-        RoFormer: Enhanced Transformer with Rotary Position Embedding.
-        https://arxiv.org/pdf/2104.09864.pdf.
-
-    """
-
-    def __init__(
-        self,
-        dim: int,
-        base: int = 10000,
-        scale_base: Optional[float] = None,
-        pos_idx_in_fp32: bool = True,
-        max_position_embeddings: int = 2048,
-        device: Optional[str] = None,
-        **kwargs,
-    ) -> None:
-        super().__init__()
-
-        if scale_base is not None:
-            raise NotImplementedError
-
-        self.dim = dim
-        self.base = float(base)
-        self.scale_base = scale_base
-        self.pos_idx_in_fp32 = pos_idx_in_fp32
-        self.max_position_embeddings = max_position_embeddings
-        self.device = device
-
-        # Generate and save the inverse frequency buffer (non-trainable)
-        inv_freq = self._compute_inv_freq(device)
-        self.register_buffer("inv_freq", inv_freq, persistent=False)
-
-        # Generate and save the scale buffer (non-trainable)
-        scale = (
-            (torch.arange(0, dim, 2, device=device, dtype=torch.float32) + 0.4 * dim) / (1.4 * dim)
-            if scale_base is not None
-            else None
-        )
-        self.register_buffer("scale", scale, persistent=False)
-
-        # Initialize cached attributes since ONNX can't rely on dynamic initialization
-        self._update_cos_sin_cache(max_position_embeddings, device=device, dtype=torch.float32)
-
-    def _compute_inv_freq(self, device: Optional[str] = None) -> torch.FloatTensor:
-        return 1.0 / (self.base ** (torch.arange(0, self.dim, 2, device=device, dtype=torch.float32) / self.dim))
-
-    def _update_cos_sin_cache(
-        self,
-        seqlen: int,
-        device: Optional[str] = None,
-        dtype: Optional[torch.dtype] = None,
-    ) -> None:
-        self._seq_len_cached = seqlen
-
-        # fp32 is preferred since the output of `torch.arange` can be quite large
-        # and bf16 would lose a lot of precision
-        if self.pos_idx_in_fp32:
-            t = torch.arange(seqlen, device=device, dtype=torch.float32)
-            if self.inv_freq.dtype != torch.float32:
-                inv_freq = self._compute_inv_freq(device=device)
-            else:
-                inv_freq = self.inv_freq
-        else:
-            t = torch.arange(seqlen, device=device, dtype=self.inv_freq.dtype)
-            inv_freq = self.inv_freq
-
-        # `torch.outer` is preferred since `torch.einsum` converts from fp32 to fp16 if used with AMP
-        freqs = torch.outer(t, inv_freq)
-        if self.scale is None:
-            self._cos_cached = torch.cos(freqs).to(dtype)
-            self._sin_cached = torch.sin(freqs).to(dtype)
-        else:
-            power = (
-                torch.arange(seqlen, dtype=self.scale.dtype, device=self.scale.device) - seqlen // 2
-            ) / self.scale_base
-            scale = self.scale.to(device=power.device) ** rearrange(power, "s -> s 1")
-
-            # Force the scale multiplication to happen in fp32
-            self._cos_cached = (torch.cos(freqs) * scale).to(dtype)
-            self._sin_cached = (torch.sin(freqs) * scale).to(dtype)
-            self._cos_k_cached = (torch.cos(freqs) / scale).to(dtype)
-            self._sin_k_cached = (torch.sin(freqs) / scale).to(dtype)
-
-    def forward(
-        self,
-        qkv: torch.Tensor,
-        kv: Optional[torch.Tensor] = None,
-        seqlen_offset: int = 0,
-        **kwargs,
-    ) -> Tuple[torch.Tensor, torch.Tensor]:
-        if (
-            self._seq_len_cached < qkv.shape[1] + seqlen_offset
-            or self._cos_cached.device != qkv.device
-            or self._cos_cached.dtype != qkv.dtype
-            or (self.training and self._cos_cached.is_inference())
-        ):
-            self._update_cos_sin_cache(qkv.shape[1] + seqlen_offset, device=qkv.device, dtype=qkv.dtype)
-
-        if kv is None:
-            return _apply_rotary_emb_qkv(
-                qkv,
-                self._cos_cached[seqlen_offset:],
-                self._sin_cached[seqlen_offset:],
-            )
-        else:
-            q = _apply_rotary_emb(
-                qkv,
-                self._cos_cached[seqlen_offset:],
-                self._sin_cached[seqlen_offset:],
-            )
-            kv = _apply_rotary_emb_kv(
-                kv,
-                self._cos_cached[seqlen_offset:],
-                self._sin_cached[seqlen_offset:],
-            )
-
-            return q, kv
-
-
 class MLP(nn.Module):
     """Multi-Layer Perceptron.
 
@@ -324,139 +196,6 @@ class MLP(nn.Module):
         return hidden_states
 
 
-class SelfAttention(nn.Module):
-    """Self-attention layer (compatible with PyTorch).
-
-    Reference:
-        https://github.com/Dao-AILab/flash-attention/blob/main/flash_attn/modules/mha.py.
-
-    """
-
-    def __init__(
-        self,
-        causal: bool = True,
-        softmax_scale: Optional[float] = None,
-        attention_dropout: float = 0.0,
-    ) -> None:
-        super().__init__()
-
-        self.causal = causal
-        self.softmax_scale = softmax_scale
-        self.drop = nn.Dropout(attention_dropout)
-
-    @torch.autocast("cpu", enabled=False)
-    @torch.autocast("cuda", enabled=False)
-    def forward(
-        self,
-        qkv: torch.FloatTensor,
-        causal: bool = None,
-        key_padding_mask: Optional[torch.BoolTensor] = None,
-        **kwargs,
-    ) -> torch.FloatTensor:
-        batch_size, seqlen = qkv.shape[0], qkv.shape[1]
-        q, k, v = qkv.unbind(dim=2)
-
-        q = q.to(torch.float32)
-        k = k.to(torch.float32)
-
-        causal = self.causal if causal is None else causal
-        softmax_scale = self.softmax_scale or 1.0 / math.sqrt(q.shape[-1])
-
-        # Autocast is manually disabled to avoid `torch.einsum` performing the operation
-        # using float16, which might lead to overflow
-        scores = torch.einsum("bthd,bshd->bhts", q, k * softmax_scale)
-
-        if key_padding_mask is not None:
-            padding_mask = torch.full((batch_size, seqlen), -10000.0, dtype=scores.dtype, device=scores.device)
-            padding_mask.masked_fill_(key_padding_mask, 0.0)
-
-            scores = scores + rearrange(padding_mask, "b s -> b 1 1 s")
-
-        if causal:
-            causal_mask = torch.triu(torch.full((seqlen, seqlen), -10000.0, device=scores.device), 1)
-            scores = scores + causal_mask.to(dtype=scores.dtype)
-
-        attention = torch.softmax(scores, dim=-1).to(v.dtype)
-        attention = self.drop(attention)
-
-        output = torch.einsum("bhts,bshd->bthd", attention, v)
-
-        return output
-
-
-class CrossAttention(nn.Module):
-    """Cross-attention layer (compatible with PyTorch).
-
-    Reference:
-        https://github.com/Dao-AILab/flash-attention/blob/main/flash_attn/modules/mha.py.
-
-    """
-
-    def __init__(
-        self,
-        causal: bool = True,
-        softmax_scale: Optional[float] = None,
-        attention_dropout: float = 0.0,
-    ) -> None:
-        super().__init__()
-
-        self.causal = causal
-        self.softmax_scale = softmax_scale
-        self.drop = nn.Dropout(attention_dropout)
-
-    @torch.autocast("cpu", enabled=False)
-    @torch.autocast("cuda", enabled=False)
-    def forward(
-        self,
-        q: torch.FloatTensor,
-        kv: torch.FloatTensor,
-        causal: bool = None,
-        key_padding_mask: Optional[torch.BoolTensor] = None,
-        **kwargs,
-    ) -> torch.FloatTensor:
-        batch_size, seqlen_q = q.shape[0], q.shape[1]
-        seqlen_k = kv.shape[1]
-
-        if kv.shape[3] != q.shape[2]:
-            kv = repeat(kv, "... hkv d -> ... (hkv g) d", g=q.shape[2] // kv.shape[3])
-        k, v = kv.unbind(dim=2)
-
-        q = q.to(torch.float32)
-        k = k.to(torch.float32)
-
-        causal = self.causal if causal is None else causal
-        softmax_scale = self.softmax_scale or 1.0 / math.sqrt(q.shape[-1])
-
-        # Autocast is manually disabled to avoid `torch.einsum` performing the operation
-        # using float16, which might lead to overflow
-        scores = torch.einsum("bthd,bshd->bhts", q, k * softmax_scale)
-
-        if key_padding_mask is not None:
-            padding_mask = torch.full(
-                (batch_size, seqlen_k),
-                -10000.0,
-                dtype=scores.dtype,
-                device=scores.device,
-            )
-            padding_mask.masked_fill_(key_padding_mask, 0.0)
-
-            scores = scores + rearrange(padding_mask, "b s -> b 1 1 s")
-
-        if causal:
-            rows = rearrange(torch.arange(seqlen_q, device=q.device, dtype=torch.long), "s -> s 1")
-            cols = torch.arange(seqlen_k, device=k.device, dtype=torch.long)
-            causal_mask = cols > rows + seqlen_k - seqlen_q
-
-            scores = scores.masked_fill(causal_mask, -10000.0)
-
-        attention = torch.softmax(scores, dim=-1).to(v.dtype)
-        attention = self.drop(attention)
-
-        output = torch.einsum("bhts,bshd->bthd", attention, v)
-
-        return output
-
-
 def _find_mha_dims(
     config: PretrainedConfig,
     n_head: Optional[int] = None,
@@ -532,14 +271,8 @@ class MHA(nn.Module):
         # Rotary embedding
         self.rotary_dim = rotary_dim if rotary_dim is not None else getattr(config, "rotary_dim", 0)
         if self.rotary_dim > 0:
-            rotary_cls = FlashRotaryEmbedding if config.flash_rotary else RotaryEmbedding
-            if rotary_cls is None:
-                rotary_cls = RotaryEmbedding
-
+            rotary_cls = FlashRotaryEmbedding
             rotary_kwargs = {}
-            if rotary_cls is RotaryEmbedding:
-                rotary_kwargs["max_position_embeddings"] = config.n_positions
-
             self.rotary_emb = rotary_cls(
                 self.rotary_dim,
                 base=rotary_base,
@@ -563,13 +296,8 @@ class MHA(nn.Module):
         self.out_proj = linear_cls(hidden_size, hidden_size, bias=bias, device=device, dtype=dtype)
 
         # Attention
-        attn_cls = FlashSelfAttention if config.flash_attn else SelfAttention
-        if attn_cls is None:
-            attn_cls = SelfAttention
-
-        cross_attn_cls = FlashCrossAttention if config.flash_attn else CrossAttention
-        if cross_attn_cls is None:
-            cross_attn_cls = CrossAttention
+        attn_cls = FlashSelfAttention
+        cross_attn_cls = FlashCrossAttention
 
         self.inner_attn = attn_cls(
             causal=causal,
@@ -582,7 +310,6 @@ class MHA(nn.Module):
             attention_dropout=config.attn_pdrop,
         )
 
-        self.flash_attn = config.flash_attn and attn_cls is FlashSelfAttention
         self.layer_idx = layer_idx
         self.return_residual = return_residual
         self.checkpointing = checkpointing
@@ -596,24 +323,23 @@ class MHA(nn.Module):
         if self.rotary_dim > 0:
             qkv = self.rotary_emb(qkv)
 
-        if self.flash_attn:
-            batch_size, seqlen = qkv.shape[0], qkv.shape[1]
+        batch_size, seqlen = qkv.shape[0], qkv.shape[1]
 
-            cu_seqlens, max_seqlen = None, None
-            if key_padding_mask is not None:
-                # If `key_padding_mask` is supplied, we need to unpad the input and retrieve
-                # the `cu_seqlens` and `max_seqlen` to be used by `flash-attn`
-                qkv, indices, cu_seqlens, max_seqlen = unpad_input(qkv, key_padding_mask)
+        cu_seqlens, max_seqlen = None, None
+        if key_padding_mask is not None:
+            # If `key_padding_mask` is supplied, we need to unpad the input and retrieve
+            # the `cu_seqlens` and `max_seqlen` to be used by `flash-attn`
+            qkv, indices, cu_seqlens, max_seqlen = unpad_input(qkv, key_padding_mask)
 
-            if self.checkpointing:
-                attn_output = torch.utils.checkpoint.checkpoint(
-                    self.inner_attn, qkv, cu_seqlens=cu_seqlens, max_seqlen=max_seqlen
-                )
-            else:
-                attn_output = self.inner_attn(qkv, cu_seqlens=cu_seqlens, max_seqlen=max_seqlen).to(qkv.device)
+        if self.checkpointing:
+            attn_output = torch.utils.checkpoint.checkpoint(
+                self.inner_attn, qkv, cu_seqlens=cu_seqlens, max_seqlen=max_seqlen
+            )
+        else:
+            attn_output = self.inner_attn(qkv, cu_seqlens=cu_seqlens, max_seqlen=max_seqlen).to(qkv.device)
 
-            # If `key_padding_mask` is supplied, we need to pad the output back to the original shape
-            return pad_input(attn_output, indices, batch_size, seqlen) if key_padding_mask is not None else attn_output
+        # If `key_padding_mask` is supplied, we need to pad the output back to the original shape
+        return pad_input(attn_output, indices, batch_size, seqlen) if key_padding_mask is not None else attn_output
 
         if self.checkpointing:
             return torch.utils.checkpoint.checkpoint(self.inner_attn, qkv, key_padding_mask=key_padding_mask)
@@ -644,54 +370,53 @@ class MHA(nn.Module):
         if past_key_values is not None:
             kv = _update_kv_cache(kv, past_key_values, self.layer_idx)
 
-        if self.flash_attn:
-            batch_size, seqlen_q = q.shape[0], q.shape[1]
-            seqlen_k = kv.shape[1]
+        batch_size, seqlen_q = q.shape[0], q.shape[1]
+        seqlen_k = kv.shape[1]
+
+        cu_seqlens_q, cu_seqlens_k, max_seqlen_q, max_seqlen_k = (
+            None,
+            None,
+            None,
+            None,
+        )
+        if key_padding_mask is not None:
+            kv, _, cu_seqlens_k, max_seqlen_k = unpad_input(kv, key_padding_mask)
+
+            if seqlen_q == 1:
+                key_padding_mask = torch.ones(batch_size, 1, device=q.device)
+            elif seqlen_q != seqlen_k:
+                key_padding_mask = key_padding_mask[:, -seqlen_q:]
+
+            q, indices_q, cu_seqlens_q, max_seqlen_q = unpad_input(q, key_padding_mask)
 
-            cu_seqlens_q, cu_seqlens_k, max_seqlen_q, max_seqlen_k = (
-                None,
-                None,
-                None,
-                None,
+        if self.checkpointing:
+            attn_output = torch.utils.checkpoint.checkpoint(
+                self.inner_cross_attn,
+                q,
+                kv,
+                causal=causal,
+                cu_seqlens=cu_seqlens_q,
+                max_seqlen=max_seqlen_q,
+                cu_seqlens_k=cu_seqlens_k,
+                max_seqlen_k=max_seqlen_k,
             )
-            if key_padding_mask is not None:
-                kv, _, cu_seqlens_k, max_seqlen_k = unpad_input(kv, key_padding_mask)
-
-                if seqlen_q == 1:
-                    key_padding_mask = torch.ones(batch_size, 1, device=q.device)
-                elif seqlen_q != seqlen_k:
-                    key_padding_mask = key_padding_mask[:, -seqlen_q:]
-
-                q, indices_q, cu_seqlens_q, max_seqlen_q = unpad_input(q, key_padding_mask)
-
-            if self.checkpointing:
-                attn_output = torch.utils.checkpoint.checkpoint(
-                    self.inner_cross_attn,
-                    q,
-                    kv,
-                    causal=causal,
-                    cu_seqlens=cu_seqlens_q,
-                    max_seqlen=max_seqlen_q,
-                    cu_seqlens_k=cu_seqlens_k,
-                    max_seqlen_k=max_seqlen_k,
-                )
-            else:
-                attn_output = self.inner_cross_attn(
-                    q,
-                    kv,
-                    causal=causal,
-                    cu_seqlens=cu_seqlens_q,
-                    max_seqlen=max_seqlen_q,
-                    cu_seqlens_k=cu_seqlens_k,
-                    max_seqlen_k=max_seqlen_k,
-                )
-
-            return (
-                pad_input(attn_output, indices_q, batch_size, max_seqlen_q)
-                if key_padding_mask is not None
-                else attn_output
+        else:
+            attn_output = self.inner_cross_attn(
+                q,
+                kv,
+                causal=causal,
+                cu_seqlens=cu_seqlens_q,
+                max_seqlen=max_seqlen_q,
+                cu_seqlens_k=cu_seqlens_k,
+                max_seqlen_k=max_seqlen_k,
             )
 
+        return (
+            pad_input(attn_output, indices_q, batch_size, max_seqlen_q)
+            if key_padding_mask is not None
+            else attn_output
+        )
+
         if self.checkpointing:
             return torch.utils.checkpoint.checkpoint(
                 self.inner_cross_attn,