[FusedMoE] Support sub-channel quantization: FP4, FP8, INT8, ...

tests/kernels/fused_moe_v1_test.py

@@ -1,7 +1,7 @@
 import jax
 import jax.numpy as jnp
 import numpy as np
-from absl.testing import absltest
+from absl.testing import absltest, parameterized
 from jax._src import test_util as jtu
 from jax.sharding import Mesh
 
@@ -43,11 +43,31 @@ def gen_moe_inputs(
     one_hot = (jnp.sum(
         jax.nn.one_hot(top_k_indices, num_experts, dtype=jnp.float32),
         axis=1,
-    ) * 10)
+    ) * 30)
     gating_output = (gating_output + one_hot).astype(dtype)
     return a, w1, w2, gating_output
 
 
+def sub_channel_quantize(x, quant_dtype, wsz=256):
+    """Quantizes x with sub-channel quantization on the 2nd minor."""
+    if jnp.issubdtype(quant_dtype, jnp.floating):
+        dtype_info = jnp.finfo(quant_dtype)
+    else:
+        dtype_info = jnp.iinfo(quant_dtype)
+    dtype_max = float(dtype_info.max)
+    w_lst, scale_lst = [], []
+    assert len(x.shape) >= 2
+    assert x.shape[-2] % wsz == 0
+    for i in range(0, x.shape[-2], wsz):
+        y = x[..., i:i + wsz, :]
+        abs_max = jnp.abs(y).max(axis=-2, keepdims=True)
+        scale = (abs_max / dtype_max).astype(jnp.float32)
+        w = (y / scale).astype(quant_dtype)
+        w_lst.append(w)
+        scale_lst.append(scale)
+    return jnp.concat(w_lst, axis=-2), jnp.concat(scale_lst, axis=-2)
+
+
 @jtu.with_config(jax_numpy_dtype_promotion="standard")
 class MoEKernelTest(jtu.JaxTestCase):
 
@@ -63,42 +83,234 @@ def setUp(self):
         self.mesh = Mesh(np.array(self.mesh_devices).reshape(1, -1),
                          axis_names=("data", "model"))
 
-    def test_basic(self):
-        dtype = jnp.bfloat16
-        top_k = 2
-        num_experts = 16
-        hidden_size = 256
-        intermediate_size = 256
-        num_tokens = 8 * 2
-
+    def _test_moe(
+        self,
+        dtype,
+        top_k,
+        num_experts,
+        hidden_size,
+        intermediate_size,
+        num_tokens,
+        seed,
+        renormalize_topk_logits,
+        bt,
+        bf,
+        bd1,
+        bd2,
+        btc,
+        bfc,
+        bd1c,
+        bd2c,
+        act_fn="silu",
+        w_dtype=None,
+        subc_quant_wsz=None,
+        use_benchmark_baseline=False,
+        atol=2e-1,
+        rtol=2e-1,
+    ):
         a, w1, w2, gating_output = gen_moe_inputs(
             dtype,
             top_k,
             num_experts,
             hidden_size,
             intermediate_size,
             num_tokens,
+            seed=seed,
+        )
+        w1_scale = None
+        w2_scale = None
+        if w_dtype is not None:
+            if subc_quant_wsz is None:
+                subc_quant_wsz = 256
+            w1, w1_scale = sub_channel_quantize(w1, w_dtype, subc_quant_wsz)
+            w2, w2_scale = sub_channel_quantize(w2, w_dtype, subc_quant_wsz)
+
+        actual = fused_ep_moe(
+            mesh=self.mesh,
+            tokens=a,
+            w1=w1,
+            w2=w2,
+            gating_output=gating_output,
+            top_k=top_k,
+            renormalize_topk_logits=renormalize_topk_logits,
+            act_fn=act_fn,
+            subc_quant_wsz=subc_quant_wsz,
+            w1_scale=w1_scale,
+            w2_scale=w2_scale,
+            bt=bt,
+            bf=bf,
+            bd1=bd1,
+            bd2=bd2,
+            btc=btc,
+            bfc=bfc,
+            bd1c=bd1c,
+            bd2c=bd2c,
+        )
+        expected = ref_moe(
+            a,
+            w1,
+            w2,
+            gating_output,
+            top_k,
+            renormalize_topk_logits=renormalize_topk_logits,
+            activation=act_fn,
+            subc_quant_wsz=subc_quant_wsz,
+            w1_scale=w1_scale,
+            w2_scale=w2_scale,
+        )
+        self.assertAllClose(actual, expected, atol=atol, rtol=rtol)
+
+    @parameterized.product(renormalize_topk_logits=[True, False], )
+    def test_basic(self, renormalize_topk_logits):
+        dtype = jnp.bfloat16
+        top_k = 8
+        num_experts = 128
+        hidden_size = 1024
+        intermediate_size = 1024
+        num_tokens = 8 * 32
+        self._test_moe(
+            dtype=dtype,
+            top_k=top_k,
+            num_experts=num_experts,
+            hidden_size=hidden_size,
+            intermediate_size=intermediate_size,
+            num_tokens=num_tokens,
+            seed=1234,
+            renormalize_topk_logits=renormalize_topk_logits,
+            bt=32,
+            bf=1024,
+            bd1=1024,
+            bd2=1024,
+            btc=32,
+            bfc=256,
+            bd1c=256,
+            bd2c=256,
         )
 
-        actual = jax.block_until_ready(
-            fused_ep_moe(
-                mesh=self.mesh,
-                tokens=a,
-                w1=w1,
-                w2=w2,
-                gating_output=gating_output,
-                top_k=top_k,
-                bt=32,
-                bf=512,
-                bd1=512,
-                bd2=512,
-                btc=32,
-                bfc=256,
-                bd1c=256,
-                bd2c=256,
-            ))
-        expected = ref_moe(a, w1, w2, gating_output, top_k)
-        self.assertAllClose(expected, actual, atol=2e-2, rtol=2e-2)
+    @parameterized.product(act_fn=["silu", "gelu", "swigluoai"], )
+    def test_activation(self, act_fn):
+        dtype = jnp.bfloat16
+        top_k = 8
+        num_experts = 128
+        hidden_size = 1024
+        intermediate_size = 1024
+        num_tokens = 8 * 32
+        self._test_moe(
+            dtype=dtype,
+            top_k=top_k,
+            num_experts=num_experts,
+            hidden_size=hidden_size,
+            intermediate_size=intermediate_size,
+            num_tokens=num_tokens,
+            seed=1234,
+            renormalize_topk_logits=True,
+            act_fn=act_fn,
+            bt=32,
+            bf=512,
+            bd1=512,
+            bd2=512,
+            btc=32,
+            bfc=256,
+            bd1c=256,
+            bd2c=256,
+        )
+
+    def test_benchmark_qwen_235(self):
+        num_experts = 128
+        top_k = 8
+        hidden_size = 4096
+        intermediate_size = 1536
+        dtype = jnp.bfloat16
+        num_tokens = 8 * 64
+        seed = 54321
+        renormalize_topk_logits = True
+        self._test_moe(
+            dtype=dtype,
+            top_k=top_k,
+            num_experts=num_experts,
+            hidden_size=hidden_size,
+            intermediate_size=intermediate_size,
+            num_tokens=num_tokens,
+            seed=seed,
+            renormalize_topk_logits=renormalize_topk_logits,
+            bt=64,
+            bf=768,
+            bd1=2048,
+            bd2=2048,
+            btc=64,
+            bfc=768,
+            bd1c=2048,
+            bd2c=2048,
+            act_fn="silu",
+            atol=5e-2,
+            rtol=5e-2,
+        )
+
+    def test_benchmark_qwen_30b_a3b(self):
+        num_experts = 128
+        top_k = 8
+        hidden_size = 2048
+        intermediate_size = 768
+        dtype = jnp.bfloat16
+        num_tokens = 512
+        seed = 54321
+        renormalize_topk_logits = True
+        self._test_moe(
+            dtype=dtype,
+            top_k=top_k,
+            num_experts=num_experts,
+            hidden_size=hidden_size,
+            intermediate_size=intermediate_size,
+            num_tokens=num_tokens,
+            seed=seed,
+            renormalize_topk_logits=renormalize_topk_logits,
+            bt=16,
+            bf=384,
+            bd1=512,
+            bd2=512,
+            btc=16,
+            bfc=384,
+            bd1c=256,
+            bd2c=256,
+            act_fn="silu",
+            atol=5e-2,
+            rtol=5e-2,
+        )
+
+    @parameterized.product(
+        w_dtype=[jnp.int8, jnp.float8_e5m2, jnp.float4_e2m1fn], )
+    def test_sub_channel_quantization(self, w_dtype):
+        if w_dtype in (
+                jnp.float8_e5m2,
+                jnp.float4_e2m1fn,
+        ) and not jtu.is_device_tpu_at_least(version=7):
+            self.skipTest("Expect TPUv7+")
+        dtype = jnp.bfloat16
+        top_k = 8
+        num_experts = 128
+        hidden_size = 1024
+        intermediate_size = 1024
+        num_tokens = 8 * 32
+        self._test_moe(
+            dtype=dtype,
+            top_k=top_k,
+            num_experts=num_experts,
+            hidden_size=hidden_size,
+            intermediate_size=intermediate_size,
+            num_tokens=num_tokens,
+            seed=1234,
+            renormalize_topk_logits=False,
+            w_dtype=w_dtype,
+            subc_quant_wsz=256,
+            bt=32,
+            bf=1024,
+            bd1=1024,
+            bd2=1024,
+            btc=32,
+            bfc=256,
+            bd1c=256,
+            bd2c=256,
+        )
 
 
 if __name__ == "__main__":