[C/PyTorch] RoPE fixes and minor improvements for fused attention (#453)

* add support for h2d/2hd in 8.9.6 Signed-off-by: Charlene Yang <[email protected]> * cull unit tests in fused_attn.py and add skipif for layout tests Signed-off-by: Charlene Yang <[email protected]> * add workopt=1 flag for dpa tests Signed-off-by: Charlene Yang <[email protected]> * update support table for arbi_seqlen backend Signed-off-by: Charlene Yang <[email protected]> * fix rotary position embedding and add unit tests accordingly Signed-off-by: Charlene Yang <[email protected]> * further cut down unit tests for CI efficiency Signed-off-by: Charlene Yang <[email protected]> * fix lint Signed-off-by: Charlene Yang <[email protected]> * remove einops dependency Signed-off-by: Charlene Yang <[email protected]> --------- Signed-off-by: Charlene Yang <[email protected]>
NVIDIA · Oct 9, 2023 · 92d1ba0 · 92d1ba0
1 parent 79f5fac
commit 92d1ba0
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diff --git a/tests/pytorch/test_fused_attn.py b/tests/pytorch/test_fused_attn.py
@@ -12,7 +12,7 @@
 )
 from transformer_engine.pytorch.fp8 import FP8GlobalStateManager
 from transformer_engine.pytorch import TransformerLayer
-from transformer_engine.pytorch.attention import DotProductAttention
+from transformer_engine.pytorch.attention import DotProductAttention, RotaryPositionEmbedding
 import os
 
 from pkg_resources import packaging
@@ -21,6 +21,8 @@
 fp8_available, reason_for_no_fp8 = FP8GlobalStateManager.is_fp8_available()
 _flash_attn_version = packaging.version.Version(version("flash-attn"))
 _flash_attn_2_available = _flash_attn_version >= packaging.version.Version("2")
+_cudnn_version = [int(i) for i in os.environ['CUDNN_VERSION'].split('.')]
+
 
 class ModelConfig:
     def __init__(
@@ -45,22 +47,26 @@ def __init__(
     "test5": ModelConfig(1, 1024, 16, 64, 128, 0.0, "no_mask"),
 }
 
-if os.getenv('NVTE_ADDITIONAL_TESTS', '0') == '1':
-    model_configs["test6"] = ModelConfig(1, 1024, 16, 64, 512, 0.0, "causal")
-    model_configs["test7"] = ModelConfig(1, 2048, 16, 128, 512, 0.0, "causal")
-    model_configs["test8"] = ModelConfig(1, 2048, 16, 128, 2048, 0.0, "causal")
-    model_configs["test9"] = ModelConfig(1, 1024, 16, 64, 512, 0.0, "no_mask")
-
 param_types = [torch.float16]
 if torch.cuda.is_bf16_supported():
     param_types.append(torch.bfloat16)
 
-batch_sizes = [1, 2] # add more if needed, e.g. 32
+batch_sizes = [1, 32]
+
+model_configs_lean = {
+    "test6": ModelConfig(1, 1024, 16, 64, 512, 0.0, "no_mask"),
+    "test7": ModelConfig(1, 2048, 16, 128, 2048, 0.0, "causal"),
+}
+
+param_types_lean = [torch.bfloat16]
+
+batch_sizes_lean = [2]
+
 
 @pytest.mark.skipif(
     get_device_compute_capability() < 8.0, reason="Compute capability 8.0+ is required.")
 @pytest.mark.parametrize("dtype", param_types)
-@pytest.mark.parametrize("bs", batch_sizes)
+@pytest.mark.parametrize("bs", batch_sizes_lean)
 @pytest.mark.parametrize("model", model_configs.keys())
 @pytest.mark.parametrize("ckpt_attn", [True, False])
 @pytest.mark.parametrize("bias_type", ["no_bias", "post_scale_bias"])
@@ -69,7 +75,6 @@ def test_dot_product_attention(dtype, bs, model, ckpt_attn, bias_type):
     FlashAttention, FusedAttention and UnfusedDotProductAttention"""
 
     config = model_configs[model]
-
     if bias_type == "no_bias":
         flash_attn_fwd, flash_attn_bwd = _run_dot_product_attention(
                 dtype, bs, config, "FlashAttention", ckpt_attn, bias_type)
@@ -94,6 +99,7 @@ def _run_dot_product_attention(dtype, bs, config, backend, ckpt_attn, bias_type)
         os.environ["NVTE_FLASH_ATTN"] = "1"
     if backend == "FusedAttention":
         os.environ["NVTE_FUSED_ATTN"] = "1"
+        os.environ["NVTE_FUSED_ATTN_FORCE_WORKSPACE_OPT"] = "1"
 
     inp = torch.randn(
             config.seq_len, bs, 3, config.num_attention_heads, config.head_dim,
@@ -150,15 +156,17 @@ def _run_dot_product_attention(dtype, bs, config, backend, ckpt_attn, bias_type)
 
 @pytest.mark.skipif(
     get_device_compute_capability() < 8.0, reason="Compute capability 8.0+ is required.")
-@pytest.mark.parametrize("dtype", param_types)
-@pytest.mark.parametrize("bs", batch_sizes)
-@pytest.mark.parametrize("model", model_configs.keys())
+@pytest.mark.skipif(
+    _cudnn_version >= [8,9,5], reason="cuDNN 8.9.5+ is required.")
+@pytest.mark.parametrize("dtype", param_types_lean)
+@pytest.mark.parametrize("bs", batch_sizes_lean)
+@pytest.mark.parametrize("model", model_configs_lean.keys())
 @pytest.mark.parametrize("workspace_opt", [True, False])
 @pytest.mark.parametrize("qkv_layout", qkv_layouts)
 def test_dpa_qkv_layout(dtype, bs, model, workspace_opt, qkv_layout):
     """Test DotProductAttention module with different QKV layouts"""
 
-    config = model_configs[model]
+    config = model_configs_lean[model]
 
     flash_attn_fwd, flash_attn_bwd = _run_dpa_qkv_layout(
             dtype, bs, config, "FlashAttention", qkv_layout, workspace_opt)
@@ -188,7 +196,6 @@ def _run_dpa_qkv_layout(dtype, bs, config, backend, qkv_layout, workspace_opt):
         os.environ["NVTE_FUSED_ATTN"] = "1"
         os.environ["NVTE_FUSED_ATTN_FORCE_WORKSPACE_OPT"] = "1" if workspace_opt else "0"
 
-
     dim_to_num = {'b': bs,
         's': config.seq_len,
         'h': config.num_attention_heads,
@@ -269,23 +276,23 @@ def _run_dpa_qkv_layout(dtype, bs, config, backend, qkv_layout, workspace_opt):
     get_device_compute_capability() < 8.0, reason="Compute capability 8.0+ is required.")
 @pytest.mark.parametrize("dtype", param_types)
 @pytest.mark.parametrize("bs", batch_sizes)
-@pytest.mark.parametrize("model", model_configs.keys())
-@pytest.mark.parametrize("ckpt_attn", [False])
+@pytest.mark.parametrize("model", model_configs_lean.keys())
 @pytest.mark.parametrize("bias_type", ["no_bias", "post_scale_bias"])
 @pytest.mark.parametrize("fused_qkv_params", [True, False])
-def test_transformer_layer(dtype, bs, model, ckpt_attn, bias_type, fused_qkv_params):
+@pytest.mark.parametrize("RoPE", [True, False])
+def test_transformer_layer(dtype, bs, model, bias_type, fused_qkv_params, RoPE):
     """Test TransformerLayer module when its DotProductAttention is enabled with
     FlashAttention, FusedAttention, or UnfusedDotProductAttention backend"""
 
-    config = model_configs[model]
+    config = model_configs_lean[model]
 
     if bias_type == "no_bias":
         flash_attn_fwd, flash_attn_bwd = _run_transformer_layer(
-                dtype, bs, config, "FlashAttention", ckpt_attn, bias_type, fused_qkv_params)
+                dtype, bs, config, "FlashAttention", bias_type, fused_qkv_params, RoPE)
     fused_attn_fwd, fused_attn_bwd = _run_transformer_layer(
-            dtype, bs, config, "FusedAttention", ckpt_attn, bias_type, fused_qkv_params)
+            dtype, bs, config, "FusedAttention", bias_type, fused_qkv_params, RoPE)
     unfused_attn_fwd, unfused_attn_bwd = _run_transformer_layer(
-            dtype, bs, config, "UnfusedDotProductAttention", ckpt_attn, bias_type, fused_qkv_params)
+            dtype, bs, config, "UnfusedDotProductAttention", bias_type, fused_qkv_params, RoPE)
 
     atol, rtol = (5e-1, 5e-2)
     if bias_type == "no_bias":
@@ -294,7 +301,7 @@ def test_transformer_layer(dtype, bs, model, ckpt_attn, bias_type, fused_qkv_par
     torch.testing.assert_close(fused_attn_fwd, unfused_attn_fwd, atol=atol, rtol=rtol)
     torch.testing.assert_close(fused_attn_bwd, unfused_attn_bwd, atol=atol, rtol=rtol)
 
-def _run_transformer_layer(dtype, bs, config, backend, ckpt_attn, bias_type, fused_qkv_params):
+def _run_transformer_layer(dtype, bs, config, backend, bias_type, fused_qkv_params, RoPE):
 
     reset_rng_states()
     os.environ["NVTE_FLASH_ATTN"] = "0"
@@ -327,6 +334,11 @@ def _run_transformer_layer(dtype, bs, config, backend, ckpt_attn, bias_type, fus
     else:
         bias = None
 
+    rotary_pos_emb = None
+    if RoPE:
+        PE = RotaryPositionEmbedding(dim=config.head_dim)
+        rotary_pos_emb = PE(config.seq_len).cuda().to(dtype=dtype)
+
     block = (
         TransformerLayer(
             config.hidden_size,
@@ -365,7 +377,7 @@ def _run_transformer_layer(dtype, bs, config, backend, ckpt_attn, bias_type, fus
     num_iters = 5
     for i in range(num_iters):
         op = block(inp, self_attn_mask_type=config.attn_mask_type,
-            checkpoint_core_attention=ckpt_attn,
+            rotary_pos_emb=rotary_pos_emb,
             core_attention_bias_type=bias_type,
             core_attention_bias=bias)
         loss = op.sum()
@@ -376,14 +388,14 @@ def _run_transformer_layer(dtype, bs, config, backend, ckpt_attn, bias_type, fus
 @pytest.mark.skipif(not _flash_attn_2_available, reason="FA2.0 is not available")
 @pytest.mark.skipif(
     get_device_compute_capability() < 8.0, reason="Compute capability 8.0+ is required.")
-@pytest.mark.parametrize("dtype", param_types)
-@pytest.mark.parametrize("bs", batch_sizes)
-@pytest.mark.parametrize("model", model_configs.keys())
+@pytest.mark.parametrize("dtype", param_types_lean)
+@pytest.mark.parametrize("bs", batch_sizes_lean)
+@pytest.mark.parametrize("model", model_configs_lean.keys())
 def test_transformer_layer_gqa(dtype, bs, model):
     """Test TransformerLayer module when its DotProductAttention is enabled with
     FlashAttention, FusedAttention, or UnfusedDotProductAttention backend"""
 
-    config = model_configs[model]
+    config = model_configs_lean[model]
     def find_factors(x):
        f = []
        for i in range(1, x + 1):

diff --git a/transformer_engine/common/fused_attn/fused_attn_f16_arbitrary_seqlen.cu b/transformer_engine/common/fused_attn/fused_attn_f16_arbitrary_seqlen.cu
@@ -1338,12 +1338,13 @@ void fused_attn_arbitrary_seqlen_bwd_qkvpacked(size_t batch, size_t max_seqlen,
         }
         use_workspace_opt = transformer_engine::getenv<bool>(
             "NVTE_FUSED_ATTN_FORCE_WORKSPACE_OPT", use_workspace_opt);
-        // will not be needed in cuDNN 8.9.6
+#if (CUDNN_VERSION < 8906)
         NVTE_QKV_Layout_Group layout_group = nvte_get_qkv_layout_group(qkv_layout);
         if ((layout_group == NVTE_QKV_Layout_Group::NVTE_HD_2HD)
             || (layout_group == NVTE_QKV_Layout_Group::NVTE_HD_H2D)) {
                 use_workspace_opt = false;
         }
+#endif
     }
 #endif
 
@@ -1485,12 +1486,13 @@ void fused_attn_arbitrary_seqlen_bwd(size_t batch, size_t max_seqlen_q, size_t m
         }
         use_workspace_opt = transformer_engine::getenv<bool>(
             "NVTE_FUSED_ATTN_FORCE_WORKSPACE_OPT", use_workspace_opt);
-        // will not be needed in cuDNN 8.9.6
+#if (CUDNN_VERSION < 8906)
         NVTE_QKV_Layout_Group layout_group = nvte_get_qkv_layout_group(qkv_layout);
         if ((layout_group == NVTE_QKV_Layout_Group::NVTE_HD_2HD)
             || (layout_group == NVTE_QKV_Layout_Group::NVTE_HD_H2D)) {
                 use_workspace_opt = false;
         }
+#endif
     }
 #endif
 

diff --git a/transformer_engine/pytorch/attention.py b/transformer_engine/pytorch/attention.py
@@ -691,6 +691,64 @@ def flash_attn_forward_func_with_cp(q, k, v, cu_seqlens_q, cu_seqlens_k,
     return out
 
 
+class RotaryPositionEmbedding(torch.nn.Module):
+    """
+    Implements Rotary Position Embedding from https://arxiv.org/abs/2104.09864.
+    """
+    def __init__(
+        self,
+        dim: int,
+        seq_len_interpolation_factor: Optional[int] = None,
+        pretrained_max_position_embeddings: Optional[int] = None,
+    ):
+        """
+        Parameters
+        ----------
+        dim: int
+            rotary embedding dimension
+        seq_len_interpolation_factor: int
+            if not None, discrete positions will be interpolated by this factor via the trick in
+            https://arxiv.org/abs/2306.15595
+        pretrained_max_position_embeddings: int
+            pre-trained max_position_embeddings before position interpolation
+        """
+        super().__init__()
+        self.seq_len_interpolation_factor = seq_len_interpolation_factor
+        inv_freq = 1.0 / (10000 ** (torch.arange(0, dim, 2).float() / dim))
+        self.register_buffer('inv_freq', inv_freq)
+        self.pretrained_max_position_embeddings = pretrained_max_position_embeddings
+
+    def forward(self, max_seq_len: int, offset: int = 0):
+        """
+        Create rotary position embedding frequencies
+
+        Parameters
+        ----------
+        max_seq_len: int
+            sequence length of a sample
+        offset: int, default = 0
+            fixed offset for freqencies
+        """
+        seq = torch.arange(max_seq_len, device=self.inv_freq.device) + offset
+        seq = seq.type_as(self.inv_freq)
+
+        if (self.pretrained_max_position_embeddings is not None
+            and self.seq_len_interpolation_factor is not None):
+            if (max_seq_len >
+                self.pretrained_max_position_embeddings * self.seq_len_interpolation_factor):
+                # dynamic linear scaling (length > position we have learned)
+                seq *= 1 / (max_seq_len / self.pretrained_max_position_embeddings)
+            else:
+                # fixed linear scaling
+                seq *= 1 / self.seq_len_interpolation_factor
+
+        freqs = torch.einsum('i , j -> i j', seq, self.inv_freq)
+        # first part even vector components, second part odd vector components,
+        #  2 * dim in dimension size
+        emb = torch.cat((freqs, freqs), dim=-1)
+        # emb [seq_length, .., dim]
+        return emb.reshape(emb.size(0), 1, 1, emb.size(1))
+
 def _rotate_half(x: torch.Tensor) -> torch.Tensor:
     """
     change sign so the last dimension becomes [-odd, +even]
@@ -1488,9 +1546,10 @@ class FusedAttention(torch.nn.Module):
     | qkv_layout    |                         |                                |
     |  - qkv        | qkv_interleaved         | qkv_interleaved                |
     |  - (q,kv)     | kv_interleaved          |                                |
-    |  - (q,k,v)    | sb3hd, bs3hd            | sb3hd, bs3hd                   |
+    |  - (q,k,v)    | sb3hd, bs3hd            | sb3hd, bs3hd, sbh3d, bsh3d     |
     |               | sbhd_sb2hd, bshd_bs2hd  | sbhd_sb2hd, bshd_bs2hd         |
-    |               | bshd_bshd_bshd          | sbhd_sbhd_sbhd, bshd_bshd_bshd |
+    |               | bshd_bshd_bshd          | sbhd_sbh2d, bshd_bsh2d         |
+    |               |                         | sbhd_sbhd_sbhd, bshd_bshd_bshd |
     | mask_type     | causal/no_mask          | causal                         |
     | bias_type     | no_bias/post_scale_bias | no_bias                        |
     | dropout       | yes                     | yes                            |
@@ -2736,6 +2795,7 @@ def forward(
             q_pos_emb, k_pos_emb = rotary_pos_emb
             query_layer = apply_rotary_pos_emb(query_layer, q_pos_emb)
             key_layer = apply_rotary_pos_emb(key_layer, k_pos_emb)
+            value_layer = value_layer.contiguous()
 
         context_layer = self.core_attention(
             query_layer,