issue/586 - 添加python的self_attention的实现和测试

Author: pengcheng888

include/infinicore/ops/attention.hpp

@@ -2,6 +2,7 @@
 
 #include "../device.hpp"
 #include "common/op.hpp"
+#include <optional>
 
 namespace infinicore::op {
 class Attention {
@@ -13,4 +14,15 @@ class Attention {
 
 Tensor attention(Tensor q, Tensor k, Tensor v, Tensor k_cache, Tensor v_cache, size_t pos);
 void attention_(Tensor out, Tensor q, Tensor k, Tensor v, Tensor k_cache, Tensor v_cache, size_t pos);
+
+Tensor self_attention(Tensor query,
+                      Tensor key,
+                      Tensor value,
+                      std::optional<float> scale);
+
+void self_attention_(Tensor out,
+                     Tensor query,
+                     Tensor key,
+                     Tensor value,
+                     std::optional<float> scale);
 } // namespace infinicore::op

python/infinicore/nn/functional/__init__.py

@@ -4,6 +4,7 @@
 from .random_sample import random_sample
 from .rms_norm import rms_norm
 from .rope import RopeAlgo, rope
+from .self_attention import self_attention
 from .silu import silu
 from .swiglu import swiglu
 
@@ -17,4 +18,5 @@
     "embedding",
     "rope",
     "RopeAlgo",
+    "self_attention",
 ]

python/infinicore/nn/functional/self_attention.py

@@ -0,0 +1,39 @@
+from typing import Optional
+
+from infinicore.lib import _infinicore
+from infinicore.tensor import Tensor
+
+
+def self_attention(
+    query: Tensor,
+    key: Tensor,
+    value: Tensor,
+    scale: Optional[float] = None,
+    *,
+    out=None,
+) -> Tensor:
+    r"""Computes scaled dot product attention on query, key and value tensors."""
+
+    seq_len = query.shape[-2]
+    total_seq_len = key.shape[-2]
+
+    assert (1 == seq_len and total_seq_len > 1) or (seq_len == total_seq_len), (
+        "Incorrect parameter value."
+    )
+
+    if out is None:
+        return Tensor(
+            _infinicore.self_attention(
+                query._underlying, key._underlying, value._underlying, scale
+            )
+        )
+
+    _infinicore.self_attention_(
+        out._underlying,
+        query._underlying,
+        key._underlying,
+        value._underlying,
+        scale,
+    )
+
+    return out

src/infinicore/ops/attention/attention.cc

@@ -1,5 +1,7 @@
 #include "infinicore/ops/attention.hpp"
-
+#include "infinicore/ops/causal_softmax.hpp"
+#include "infinicore/ops/gemm.hpp"
+#include <cmath>
 namespace infinicore::op {
 
 common::OpDispatcher<Attention::schema> &Attention::dispatcher() {
@@ -25,4 +27,88 @@ void attention_(Tensor out, Tensor q, Tensor k, Tensor v, Tensor k_cache, Tensor
     Attention::execute(out, q, k, v, k_cache, v_cache, pos);
 }
 
+Tensor self_attention(Tensor query_states, // [bs, num_attention_heads, ntoken, head_dim]
+                      Tensor key_states,   // [bs, num_key_value_heads, total_token, head_dim]
+                      Tensor value_states, // [bs, num_key_value_heads, total_token, head_dim]
+                      std::optional<float> scale) {
+
+    auto query_shape = query_states->shape();
+    auto key_shape = key_states->shape();
+
+    Size batch_size = query_shape[0];
+    Size num_attention_heads = query_shape[1];
+    Size ntoken = query_shape[2];
+    Size head_dim = key_shape[3];
+
+    Tensor output_values = Tensor::empty({batch_size, num_attention_heads, ntoken, head_dim}, query_states->dtype(), query_states->device());
+
+    self_attention_(output_values, query_states, key_states, value_states, scale);
+
+    return output_values;
+}
+
+void self_attention_(Tensor out,
+                     Tensor query_states,
+                     Tensor key_states,
+                     Tensor value_states,
+                     std::optional<float> scale) {
+
+    auto query_shape = query_states->shape();
+    auto key_shape = key_states->shape();
+
+    Size batch_size = query_shape[0];
+    Size num_attention_heads = query_shape[1];
+    Size ntoken = query_shape[2];
+
+    Size num_key_value_heads = key_shape[1];
+    Size total_token = key_shape[2];
+    Size head_dim = key_shape[3];
+
+    assert(0 == (num_attention_heads % num_key_value_heads));
+    Size ngroup = num_attention_heads / num_key_value_heads;
+
+    float attention_scale{0.0f};
+    if (scale.has_value()) {
+        attention_scale = scale.value();
+    } else {
+        attention_scale = 1.f / float(sqrt(head_dim));
+    }
+
+    Tensor out_view = out->view({batch_size, num_key_value_heads, ngroup * ntoken, head_dim});
+    for (Size ib = 0; ib < batch_size; ++ib) {
+        Tensor q = query_states->narrow({{0, ib, 1}})->view({num_attention_heads, ntoken, head_dim});      // [ num_attention_heads, ntoken, head_dim]
+        Tensor k = key_states->narrow({{0, ib, 1}})->view({num_key_value_heads, total_token, head_dim});   // [ num_key_value_heads, total_token, head_dim]
+        Tensor v = value_states->narrow({{0, ib, 1}})->view({num_key_value_heads, total_token, head_dim}); // [ num_key_value_heads, total_token, head_dim]
+        Tensor output_v = out_view->narrow({{0, ib, 1}})->view({num_key_value_heads, ngroup * ntoken, head_dim});
+        {
+            /*
+            输入：
+                q,  [ num_attention_heads, ntoken, head_dim]
+                k,  [ num_key_value_heads, total_token, head_dim]
+                v,  [ num_key_value_heads, total_token, head_dim]
+            输出：
+                att_val ： {num_key_value_heads, ngroup * ntok, head_dim}
+            */
+
+            auto q_gemm = q->view({num_key_value_heads, ngroup * ntoken, head_dim}); // => {nkvh, ngroup * seq_len, dh}
+            auto k_gemm = k->permute({0, 2, 1});                                     // => { nkvh, dh, total_token}
+            auto v_gemm = v;                                                         // => { nkvh, total_token, dh}
+
+            // qk_score : => {nkvh, ngroup * ntoken, total_token}
+            Tensor qk_score = gemm(q_gemm, // {nkvh, ngroup * ntoken, dh}
+                                   k_gemm, // {nkvh, dh, total_token}
+                                   attention_scale, 0.f);
+
+            // softmax
+            auto qk_softmax = qk_score->view({num_attention_heads, ntoken, total_token});
+            causal_softmax_(qk_softmax, qk_softmax);
+
+            // values
+            gemm_(output_v, // {nkvh, ngroup * ntoken, dh}
+                  qk_score, // {nkvh, ngroup * ntoken, total_token}
+                  v_gemm,   // { nkvh, total_token, dh}
+                  1.0f, 0.0f);
+        }
+    }
+}
 } // namespace infinicore::op

src/infinicore/pybind11/ops/attention.hpp

@@ -8,6 +8,29 @@ namespace py = pybind11;
 
 namespace infinicore::ops {
 
+Tensor py_self_attention(Tensor query,
+                         Tensor key,
+                         Tensor value,
+                         pybind11::object scale) {
+    std::optional<float> scale_float = std::nullopt;
+    if (!scale.is_none()) {
+        scale_float = scale.cast<float>();
+    }
+    return op::self_attention(query, key, value, scale_float);
+}
+
+void py_self_attention_(Tensor out,
+                        Tensor query,
+                        Tensor key,
+                        Tensor value,
+                        pybind11::object scale) {
+    std::optional<float> scale_float = std::nullopt;
+    if (!scale.is_none()) {
+        scale_float = scale.cast<float>();
+    }
+    op::self_attention_(out, query, key, value, scale_float);
+}
+
 inline void bind_attention(py::module &m) {
     m.def("attention",
           &op::attention,
@@ -21,7 +44,7 @@ inline void bind_attention(py::module &m) {
 
 Args:
     q: Query tensor
-    k: Key tensor  
+    k: Key tensor
     v: Value tensor
     k_cache: Key cache tensor
     v_cache: Value cache tensor
@@ -51,6 +74,23 @@ inline void bind_attention(py::module &m) {
     v_cache: Value cache tensor
     pos: Current position in the sequence
 )doc");
+
+    m.def("self_attention",
+          &ops::py_self_attention,
+          py::arg("query"),
+          py::arg("key"),
+          py::arg("value"),
+          py::arg("scale") = py::none(),
+          R"doc(Computes scaled dot product attention on query, key and value tensors)doc");
+
+    m.def("self_attention_",
+          &ops::py_self_attention_,
+          py::arg("out"),
+          py::arg("query"),
+          py::arg("key"),
+          py::arg("value"),
+          py::arg("scale") = py::none(),
+          R"doc(In-place, Computes scaled dot product attention on query, key and value tensors)doc");
 }
 
 } // namespace infinicore::ops

test/infinicore/ops/self_attention.py

@@ -0,0 +1,136 @@
+import sys
+import os
+
+sys.path.insert(0, os.path.join(os.path.dirname(__file__), ".."))
+
+import torch
+import infinicore
+from framework.base import BaseOperatorTest, TensorSpec, TestCase
+from framework.runner import GenericTestRunner
+from framework.utils import is_broadcast
+
+
+# ==============================================================================
+# Operator-specific configuration
+# ==============================================================================
+_TEST_CASES_DATA = [
+    # bs, ntoken, total_token, num_attention_heads, num_key_value_heads, head_dim
+    (1, 4, 4, 8, 8, 64),
+    (1, 1, 4, 8, 8, 64),
+    (4, 16, 16, 32, 8, 64),
+    (4, 1, 128, 32, 8, 64),
+]
+
+
+# Tolerance configuration
+_TOLERANCE_MAP = {
+    infinicore.float16: {"atol": 1e-2, "rtol": 1e-2},
+    infinicore.float32: {"atol": 1e-2, "rtol": 1e-2},
+    infinicore.bfloat16: {"atol": 5e-2, "rtol": 5e-2},
+}
+
+
+# Data types to test
+_TENSOR_DTYPES = [infinicore.float16, infinicore.bfloat16, infinicore.float32]
+# _TENSOR_DTYPES = [infinicore.bfloat16]
+
+
+def parse_test_cases():
+    """
+    Parse test case data and return list of TestCase objects for sdpa operation.
+    Each test case contains all necessary information for execution and validation.
+    """
+    test_cases = []
+
+    for data in _TEST_CASES_DATA:
+        bs = data[0]
+        ntoken, total_token = data[1], data[2]
+        num_attention_heads, num_key_value_heads = data[3], data[4]
+        head_dim = data[5]
+
+        # Determine shapes based on batch dimension
+        query_shape = (bs, num_attention_heads, ntoken, head_dim)
+        key_shape = (bs, num_key_value_heads, total_token, head_dim)
+        value_shape = (bs, num_key_value_heads, total_token, head_dim)
+        out_shape = (bs, num_attention_heads, ntoken, head_dim)
+
+        # Check if tensors support in-place operations
+        c_supports_inplace = not is_broadcast(out_shape)
+
+        # Generate test cases for all data types
+        for dtype in _TENSOR_DTYPES:
+            tolerance = _TOLERANCE_MAP.get(dtype, {"atol": 0, "rtol": 1e-3})
+
+            # Create typed tensor specs
+            query_spec = TensorSpec.from_tensor(query_shape, None, dtype)
+            key_spec = TensorSpec.from_tensor(key_shape, None, dtype)
+            value_spec = TensorSpec.from_tensor(value_shape, None, dtype)
+            out_spec = TensorSpec.from_tensor(out_shape, None, dtype)
+
+            # Test Case 1: Out-of-place (return value)
+            test_cases.append(
+                TestCase(
+                    inputs=[query_spec, key_spec, value_spec],
+                    kwargs={},
+                    output_spec=None,
+                    comparison_target=None,
+                    tolerance=tolerance,
+                    description=f"sdpa - OUT_OF_PLACE",
+                )
+            )
+
+            # Test Case 2: In-place with explicit output tensor
+            if c_supports_inplace:
+                test_cases.append(
+                    TestCase(
+                        inputs=[query_spec, key_spec, value_spec],
+                        kwargs=None,
+                        output_spec=out_spec,  # Specify the output tensor spec
+                        comparison_target="out",
+                        tolerance=tolerance,
+                        description=f"sdpa - INPLACE(out)",
+                    )
+                )
+
+    return test_cases
+
+
+class OpTest(BaseOperatorTest):
+    """sdpa operator test with simplified implementation"""
+
+    def __init__(self):
+        super().__init__("sdpa")
+
+    def get_test_cases(self):
+        return parse_test_cases()
+
+    def torch_operator(self, query, key, value, out=None, **kwargs):
+        """PyTorch sdpa implementation"""
+        ntoken = query.shape[-2]
+        total_token = key.shape[-2]
+
+        is_causal = True
+        if 1 == ntoken and total_token > 1:
+            is_causal = False
+
+        result = torch.nn.functional.scaled_dot_product_attention(
+            query, key, value, is_causal=is_causal, enable_gqa=True
+        )
+        if out is not None:
+            out.copy_(result)
+            return out
+        return result
+
+    def infinicore_operator(self, query, key, value, out=None, **kwargs):
+        """InfiniCore sdpa implementation"""
+        return infinicore.nn.functional.self_attention(query, key, value, out=out)
+
+
+def main():
+    """Main entry point"""
+    runner = GenericTestRunner(OpTest)
+    runner.run_and_exit()
+
+
+if __name__ == "__main__":
+    main()